diff --git a/src/eynollah/cli/__init__.py b/src/eynollah/cli/__init__.py
index 43ed046..1584fa5 100644
--- a/src/eynollah/cli/__init__.py
+++ b/src/eynollah/cli/__init__.py
@@ -1,7 +1,3 @@
-# NOTE: For predictable order of imports of torch/shapely/tensorflow
-#       this must be the first import of the CLI!
-from ..eynollah_imports import imported_libs
-
 from .cli import main
 from .cli_binarize import binarize_cli
 from .cli_enhance import enhance_cli
diff --git a/src/eynollah/cli/cli.py b/src/eynollah/cli/cli.py
index ace3f1c..2a4c8d1 100644
--- a/src/eynollah/cli/cli.py
+++ b/src/eynollah/cli/cli.py
@@ -15,6 +15,7 @@ class EynollahCliCtx:
     Holds options relevant for all eynollah subcommands
     """
     model_zoo: EynollahModelZoo
+    device: str = ''
     log_level : Union[str, None] = 'INFO'
 
 
@@ -35,6 +36,11 @@ class EynollahCliCtx:
     type=(str, str, str),
     multiple=True,
 )
+@click.option(
+    "--device",
+    "-D",
+    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
+)
 @click.option(
     "--log_level",
     "-l",
@@ -42,7 +48,7 @@ class EynollahCliCtx:
     help="Override log level globally to this",
 )
 @click.pass_context
-def main(ctx, model_basedir, model_overrides, log_level):
+def main(ctx, model_basedir, model_overrides, device, log_level):
     """
     eynollah - Document Layout Analysis, Image Enhancement, OCR
     """
@@ -58,6 +64,7 @@ def main(ctx, model_basedir, model_overrides, log_level):
     # Initialize CLI context
     ctx.obj = EynollahCliCtx(
         model_zoo=model_zoo,
+        device=device,
         log_level=log_level,
     )
 
diff --git a/src/eynollah/cli/cli_binarize.py b/src/eynollah/cli/cli_binarize.py
index f0e56f5..82209be 100644
--- a/src/eynollah/cli/cli_binarize.py
+++ b/src/eynollah/cli/cli_binarize.py
@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     '--patches/--no-patches',
     default=True,
@@ -31,11 +33,6 @@ import click
     help="overwrite (instead of skipping) if output xml exists",
     is_flag=True,
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
 def binarize_cli(
         ctx,
@@ -44,14 +41,14 @@ def binarize_cli(
         dir_in,
         output,
         overwrite,
-        device,
 ):
     """
     Binarize images with a ML model
     """
     from ..sbb_binarize import SbbBinarizer
     assert bool(input_image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
-    binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo, device=device)
+    binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo,
+                             device=ctx.obj.device)
     binarizer.run(
         image_filename=input_image,
         use_patches=patches,
diff --git a/src/eynollah/cli/cli_enhance.py b/src/eynollah/cli/cli_enhance.py
index 517e1e8..bcb8263 100644
--- a/src/eynollah/cli/cli_enhance.py
+++ b/src/eynollah/cli/cli_enhance.py
@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--image",
     "-i",
@@ -46,13 +48,8 @@ import click
     is_flag=True,
     help="save the enhanced image in original image size",
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
-def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale, device):
+def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale):
     """
     Enhance image
     """
@@ -60,10 +57,10 @@ def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower
     from ..image_enhancer import Enhancer
     enhancer = Enhancer(
         model_zoo=ctx.obj.model_zoo,
+        device=ctx.obj.device,
         num_col_upper=num_col_upper,
         num_col_lower=num_col_lower,
         save_org_scale=save_org_scale,
-        device=device,
     )
     enhancer.run(overwrite=overwrite,
                  dir_in=dir_in,
diff --git a/src/eynollah/cli/cli_extract_images.py b/src/eynollah/cli/cli_extract_images.py
index 0add5b5..acd31f1 100644
--- a/src/eynollah/cli/cli_extract_images.py
+++ b/src/eynollah/cli/cli_extract_images.py
@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--image",
     "-i",
@@ -30,36 +32,40 @@ import click
 @click.option(
     "--save_images",
     "-si",
-    help="if a directory is given, images in documents will be cropped and saved there",
+    help="if a directory is given, cropped images of pages will be saved there",
     type=click.Path(exists=True, file_okay=False),
 )
 @click.option(
-    "--enable-plotting/--disable-plotting",
-    "-ep/-noep",
+    "--enable-plotting",
+    "-ep",
     is_flag=True,
-    help="If set, will plot intermediary files and images",
+    help="plot intermediary diagnostic images to files",
 )
 @click.option(
-    "--input_binary/--input-RGB",
-    "-ib/-irgb",
+    "--input_binary",
+    "-ib",
     is_flag=True,
-    help="In general, eynollah uses RGB as input but if the input document is very dark, very bright or for any other reason you can turn on input binarization. When this flag is set, eynollah will binarize the RGB input document, you should always provide RGB images to eynollah.",
+    help="In general, eynollah uses RGB as input, but if the input document is very dark, very bright or for any other reason you can turn on internal binarization here. When set, eynollah will binarize the RGB input document first.",
 )
 @click.option(
-    "--ignore_page_extraction/--extract_page_included",
-    "-ipe/-epi",
+    "--ignore_page_extraction",
+    "-ipe",
     is_flag=True,
-    help="if this parameter set to true, this tool would ignore page extraction",
+    help="ignore page extraction (cropping via page frame detection model)",
 )
 @click.option(
     "--num_col_upper",
     "-ncu",
-    help="lower limit of columns in document image",
+    default=0,
+    type=click.IntRange(min=0),
+    help="lower limit of columns in document image; 0 means autodetected from model",
 )
 @click.option(
     "--num_col_lower",
     "-ncl",
-    help="upper limit of columns in document image",
+    default=0,
+    type=click.IntRange(min=0),
+    help="upper limit of columns in document image; 0 means autodetected from model",
 )
 @click.pass_context
 def extract_images_cli(
diff --git a/src/eynollah/cli/cli_layout.py b/src/eynollah/cli/cli_layout.py
index 417b202..0a083d5 100644
--- a/src/eynollah/cli/cli_layout.py
+++ b/src/eynollah/cli/cli_layout.py
@@ -172,11 +172,6 @@ import click
     type=click.FloatRange(min=0),
     help="abort when number of failed images exceeds this value (if >=1) or ratio of failed over total images exceeds this value (if <1); 0 means ignore failures",
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
 def layout_cli(
     ctx,
@@ -207,7 +202,6 @@ def layout_cli(
     ignore_page_extraction,
     num_jobs,
     halt_fail,
-    device,
 ):
     """
     Detect Layout (with optional image enhancement and reading order detection)
@@ -223,7 +217,7 @@ def layout_cli(
     assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
     eynollah = Eynollah(
         model_zoo=ctx.obj.model_zoo,
-        device=device,
+        device=ctx.obj.device,
         enable_plotting=enable_plotting,
         allow_enhancement=allow_enhancement,
         curved_line=curved_line,
diff --git a/src/eynollah/cli/cli_ocr.py b/src/eynollah/cli/cli_ocr.py
index 406af61..daeccbe 100644
--- a/src/eynollah/cli/cli_ocr.py
+++ b/src/eynollah/cli/cli_ocr.py
@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--image",
     "-i",
@@ -16,7 +18,7 @@ import click
 @click.option(
     "--dir_in_bin",
     "-dib",
-    help=("directory of binarized images (in addition to --dir_in for RGB images; filename stems must match the RGB image files, with '.png' \n                                                                          Perform prediction using both RGB and binary images. (This does not necessarily improve results, however it may be beneficial for certain document images."),                                                 
+    help=("directory of binarized images (in addition to --dir_in for RGB images; filename stems must match the RGB image files, with '.png'. \n                                                                          Perform prediction using both RGB and binary images. (This may improve results for certain document images.)"),
     type=click.Path(exists=True, file_okay=False),
 )
 @click.option(
@@ -47,25 +49,29 @@ import click
 )
 @click.option(
     "--tr_ocr",
-    "-trocr/-notrocr",
+    "-trocr",
     is_flag=True,
-    help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.",
+    help="use transformer OCR (instead of classic CNN-RNN) model",
 )
 @click.option(
     "--do_not_mask_with_textline_contour",
-    "-nmtc/-mtc",
+    "-nmtc",
     is_flag=True,
-    help="if this parameter set to true, cropped textline images will not be masked with textline contour.",
+    help="skip masking each cropped textline image with its corresponding textline contour",
 )
 @click.option(
     "--batch_size",
     "-bs",
+    default=0,
+    type=click.IntRange(min=0),
     help="number of inference batch size. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively",
 )
 @click.option(
     "--min_conf_value_of_textline_text",
     "-min_conf",
-    help="minimum OCR confidence value. Text lines with a confidence value lower than this threshold will not be included in the output XML file.",
+    default=0.3,
+    type=click.FloatRange(min=0.0, max=1.0),
+    help="minimum OCR confidence threshold. Text lines with a lower confidence value will not be included in the output XML file.",
 )
 @click.pass_context
 def ocr_cli(
@@ -85,14 +91,16 @@ def ocr_cli(
     """
     Recognize text with a CNN/RNN or transformer ML model.
     """
-    assert bool(image) ^ bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both."
+    assert bool(image) != bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both."
     from ..eynollah_ocr import Eynollah_ocr
     eynollah_ocr = Eynollah_ocr(
         model_zoo=ctx.obj.model_zoo,
+        device=ctx.obj.device,
         tr_ocr=tr_ocr,
         do_not_mask_with_textline_contour=do_not_mask_with_textline_contour,
         batch_size=batch_size,
-        min_conf_value_of_textline_text=min_conf_value_of_textline_text)
+        min_conf_value_of_textline_text=min_conf_value_of_textline_text,
+    )
     eynollah_ocr.run(overwrite=overwrite,
                      dir_in=dir_in,
                      dir_in_bin=dir_in_bin,
diff --git a/src/eynollah/cli/cli_readingorder.py b/src/eynollah/cli/cli_readingorder.py
index 0f44b7f..ac52e38 100644
--- a/src/eynollah/cli/cli_readingorder.py
+++ b/src/eynollah/cli/cli_readingorder.py
@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--input",
     "-i",
@@ -25,9 +27,10 @@ def readingorder_cli(ctx, input, dir_in, out):
     """
     Generate ReadingOrder with a ML model
     """
-    from ..mb_ro_on_layout import machine_based_reading_order_on_layout
+    from ..mb_ro_on_layout import Reorder
     assert bool(input) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
-    orderer = machine_based_reading_order_on_layout(model_zoo=ctx.obj.model_zoo)
+    orderer = Reorder(model_zoo=ctx.obj.model_zoo,
+                      device=ctx.obj.device)
     orderer.run(xml_filename=input,
                 dir_in=dir_in,
                 dir_out=out,
diff --git a/src/eynollah/extract_images.py b/src/eynollah/extract_images.py
index 7a7e3f6..40476a3 100644
--- a/src/eynollah/extract_images.py
+++ b/src/eynollah/extract_images.py
@@ -9,7 +9,6 @@ import os
 import time
 from typing import Optional
 from pathlib import Path
-import tensorflow as tf
 import numpy as np
 import cv2
 
@@ -64,12 +63,6 @@ class EynollahImageExtractor(Eynollah):
 
         t_start = time.time()
 
-        try:
-            for device in tf.config.list_physical_devices('GPU'):
-                tf.config.experimental.set_memory_growth(device, True)
-        except:
-            self.logger.warning("no GPU device available")
-            
         self.logger.info("Loading models...")
         self.setup_models()
         self.logger.info(f"Model initialization complete ({time.time() - t_start:.1f}s)")
diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py
index c632941..9db47ce 100644
--- a/src/eynollah/eynollah.py
+++ b/src/eynollah/eynollah.py
@@ -1148,7 +1148,6 @@ class Eynollah:
             boxes,
             textline_mask_tot
     ):
-        assert np.any(textline_mask_tot)
         self.logger.debug("enter do_order_of_regions")
         contours_only_text_parent = ensure_array(contours_only_text_parent)
         contours_only_text_parent_h = ensure_array(contours_only_text_parent_h)
diff --git a/src/eynollah/eynollah_imports.py b/src/eynollah/eynollah_imports.py
deleted file mode 100644
index 496406c..0000000
--- a/src/eynollah/eynollah_imports.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""
-Load libraries with possible race conditions once. This must be imported as the first module of eynollah.
-"""
-import os
-os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
-
-from ocrd_utils import tf_disable_interactive_logs
-from torch import *
-tf_disable_interactive_logs()
-import tensorflow.keras
-from shapely import *
-imported_libs = True
-__all__ = ['imported_libs']
diff --git a/src/eynollah/eynollah_ocr.py b/src/eynollah/eynollah_ocr.py
index 1b49077..b94853b 100644
--- a/src/eynollah/eynollah_ocr.py
+++ b/src/eynollah/eynollah_ocr.py
@@ -14,16 +14,14 @@ from cv2.typing import MatLike
 from xml.etree import ElementTree as ET
 from PIL import Image, ImageDraw
 import numpy as np
-from eynollah.model_zoo import EynollahModelZoo
-from eynollah.utils.font import get_font
-from eynollah.utils.xml import etree_namespace_for_element_tag
-try:
-    import torch
-except ImportError:
-    torch = None
+from ocrd_utils import polygon_from_points, xywh_from_polygon
 
 
+from .eynollah import Eynollah
+from .model_zoo import EynollahModelZoo
 from .utils import is_image_filename
+from .utils.font import get_font
+from .utils.xml import etree_namespace_for_element_tag
 from .utils.resize import resize_image
 from .utils.utils_ocr import (
     break_curved_line_into_small_pieces_and_then_merge,
@@ -34,6 +32,7 @@ from .utils.utils_ocr import (
     preprocess_and_resize_image_for_ocrcnn_model,
     return_textlines_split_if_needed,
     rotate_image_with_padding,
+    batched,
 )
 
 # TODO: refine typing
@@ -44,45 +43,44 @@ class EynollahOcrResult:
     cropped_lines_region_indexer: List
     total_bb_coordinates:List
 
-class Eynollah_ocr:
+class Eynollah_ocr(Eynollah):
     def __init__(
         self,
         *,
         model_zoo: EynollahModelZoo,
         tr_ocr=False,
-        batch_size: Optional[int]=None,
+        batch_size: int=0,
         do_not_mask_with_textline_contour: bool=False,
-        min_conf_value_of_textline_text : Optional[float]=None,
+        min_conf_value_of_textline_text : float=0.3,
         logger: Optional[Logger]=None,
+        device: str = '',
     ):
         self.tr_ocr = tr_ocr
         # masking for OCR and GT generation, relevant for skewed lines and bounding boxes
         self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour
         self.logger = logger if logger else getLogger('eynollah.ocr')
-        self.model_zoo = model_zoo
         
-        self.min_conf_value_of_textline_text = min_conf_value_of_textline_text if min_conf_value_of_textline_text else 0.3
-        self.b_s = 2 if batch_size is None and tr_ocr else 8 if batch_size is None else batch_size
+        self.min_conf_value_of_textline_text = min_conf_value_of_textline_text
+        self.b_s = batch_size or 2 if tr_ocr else 8
 
-        if tr_ocr:
-            self.model_zoo.load_models('trocr_processor')
-            self.model_zoo.load_models(['ocr', 'tr'])
-            self.model_zoo.get('ocr').to(self.device)
+        self.model_zoo = model_zoo
+        self.setup_models(device=device)
+
+    def setup_models(self, device=''):
+        if self.tr_ocr:
+            self.model_zoo.load_models('trocr_processor',
+                                       ('ocr', 'tr'),
+                                       device=device)
         else:
-            self.model_zoo.load_models('ocr')
-            self.model_zoo.load_models('num_to_char')
-            self.model_zoo.load_models('characters')
+            self.model_zoo.load_models('ocr',
+                                       'num_to_char',
+                                       'characters',
+                                       device=device)
             self.end_character = len(self.model_zoo.get('characters')) + 2
 
     @property
     def device(self):
-        assert torch
-        if torch.cuda.is_available():
-            self.logger.info("Using GPU acceleration")
-            return torch.device("cuda:0")
-        else:
-            self.logger.info("Using CPU processing")
-            return torch.device("cpu")
+        return self.model_zoo.get('ocr').device
 
     def run_trocr(
         self,
@@ -94,174 +92,94 @@ class Eynollah_ocr:
     ) -> EynollahOcrResult:
         
         total_bb_coordinates = []
-
-            
         cropped_lines = []
         cropped_lines_region_indexer = []
         cropped_lines_meging_indexing = []
-        
         extracted_texts = []
+        extracted_confs = []
 
-        indexer_text_region = 0
-        indexer_b_s = 0
-        
-        for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'):
-            for child_textregion in nn:
-                if child_textregion.tag.endswith("TextLine"):
-                    
-                    for child_textlines in child_textregion:
-                        if child_textlines.tag.endswith("Coords"):
-                            cropped_lines_region_indexer.append(indexer_text_region)
-                            p_h=child_textlines.attrib['points'].split(' ')
-                            textline_coords =  np.array( [ [int(x.split(',')[0]),
-                                                            int(x.split(',')[1]) ]
-                                                            for x in p_h] )
-                            x,y,w,h = cv2.boundingRect(textline_coords)
-                            
-                            total_bb_coordinates.append([x,y,w,h])
-                            
-                            h2w_ratio = h/float(w)
-                            
-                            img_poly_on_img = np.copy(img)
-                            mask_poly = np.zeros(img.shape)
-                            mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
-                            
-                            mask_poly = mask_poly[y:y+h, x:x+w, :]
-                            img_crop = img_poly_on_img[y:y+h, x:x+w, :]
-                            img_crop[mask_poly==0] = 255
-                            
-                            self.logger.debug("processing %d lines for '%s'",
-                                                len(cropped_lines), nn.attrib['id'])
-                            if h2w_ratio > 0.1:
-                                cropped_lines.append(resize_image(img_crop,
-                                                                    tr_ocr_input_height_and_width,
-                                                                    tr_ocr_input_height_and_width)  )
-                                cropped_lines_meging_indexing.append(0)
-                                indexer_b_s+=1
-                                if indexer_b_s==self.b_s:
-                                    imgs = cropped_lines[:]
-                                    cropped_lines = []
-                                    indexer_b_s = 0
-                                    
-                                    pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-                                    generated_ids_merged = self.model_zoo.get('ocr').generate(
-                                        pixel_values_merged.to(self.device))
-                                    generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-                                        generated_ids_merged, skip_special_tokens=True)
-                                    
-                                    extracted_texts = extracted_texts + generated_text_merged
-                                    
-                            else:
-                                splited_images, _ = return_textlines_split_if_needed(img_crop, None)
-                                #print(splited_images)
-                                if splited_images:
-                                    cropped_lines.append(resize_image(splited_images[0],
-                                                                        tr_ocr_input_height_and_width,
-                                                                        tr_ocr_input_height_and_width))
-                                    cropped_lines_meging_indexing.append(1)
-                                    indexer_b_s+=1
-                                    
-                                    if indexer_b_s==self.b_s:
-                                        imgs = cropped_lines[:]
-                                        cropped_lines = []
-                                        indexer_b_s = 0
-                                        
-                                        pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-                                        generated_ids_merged = self.model_zoo.get('ocr').generate(
-                                            pixel_values_merged.to(self.device))
-                                        generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-                                            generated_ids_merged, skip_special_tokens=True)
-                                        
-                                        extracted_texts = extracted_texts + generated_text_merged
-                                    
-                                    
-                                    cropped_lines.append(resize_image(splited_images[1],
-                                                                        tr_ocr_input_height_and_width,
-                                                                        tr_ocr_input_height_and_width))
-                                    cropped_lines_meging_indexing.append(-1)
-                                    indexer_b_s+=1
-                                    
-                                    if indexer_b_s==self.b_s:
-                                        imgs = cropped_lines[:]
-                                        cropped_lines = []
-                                        indexer_b_s = 0
-                                        
-                                        pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-                                        generated_ids_merged = self.model_zoo.get('ocr').generate(
-                                            pixel_values_merged.to(self.device))
-                                        generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-                                            generated_ids_merged, skip_special_tokens=True)
-                                        
-                                        extracted_texts = extracted_texts + generated_text_merged
-                                        
-                                else:
-                                    cropped_lines.append(img_crop)
-                                    cropped_lines_meging_indexing.append(0)
-                                    indexer_b_s+=1
-                                    
-                                    if indexer_b_s==self.b_s:
-                                        imgs = cropped_lines[:]
-                                        cropped_lines = []
-                                        indexer_b_s = 0
-                                        
-                                        pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-                                        generated_ids_merged = self.model_zoo.get('ocr').generate(
-                                            pixel_values_merged.to(self.device))
-                                        generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-                                            generated_ids_merged, skip_special_tokens=True)
-                                        
-                                        extracted_texts = extracted_texts + generated_text_merged
-                                        
-            
-                                    
-            indexer_text_region = indexer_text_region +1
+        for n_region, region in enumerate(page_tree.getroot().iter('{%s}TextRegion' % page_ns)):
+            for n_line, line in enumerate(region.iter('{%s}TextLine' % page_ns)):
+                cropped_lines_region_indexer.append(n_region)
 
-        if indexer_b_s!=0:
-            imgs = cropped_lines[:]
-            cropped_lines = []
-            indexer_b_s = 0
-            
-            pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-            generated_ids_merged = self.model_zoo.get('ocr').generate(pixel_values_merged.to(self.device))
-            generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(generated_ids_merged, skip_special_tokens=True)
-            
-            extracted_texts = extracted_texts + generated_text_merged
-            
-        ####extracted_texts = []
-        ####n_iterations  = math.ceil(len(cropped_lines) / self.b_s) 
+                coords = line.find('{%s}Coords' % page_ns)
+                if coords is None:
+                    self.logger.warning("region '%s' line '%s' has no Coords", region.attrib['id'], line.attrib['id'])
+                    continue
+                poly = np.array(polygon_from_points(coords.attrib['points'])).astype(int)
+                cont = poly[:, np.newaxis]
+                xywh = xywh_from_polygon(poly)
+                x, y, w, h = xywh['x'], xywh['y'], xywh['w'], xywh['h']
 
-        ####for i in range(n_iterations):
-            ####if i==(n_iterations-1):
-                ####n_start = i*self.b_s
-                ####imgs = cropped_lines[n_start:]
-            ####else:
-                ####n_start = i*self.b_s
-                ####n_end = (i+1)*self.b_s
-                ####imgs = cropped_lines[n_start:n_end]
-            ####pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-            ####generated_ids_merged = self.model_ocr.generate(
-            ####    pixel_values_merged.to(self.device))
-            ####generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-            ####    generated_ids_merged, skip_special_tokens=True)
-            
-            ####extracted_texts = extracted_texts + generated_text_merged
-            
+                total_bb_coordinates.append([x, y, w, h])
+
+                img_crop = img[y: y + h, x: x + w]
+                if not self.do_not_mask_with_textline_contour:
+                    mask_poly = np.zeros(img_crop.shape[:2], dtype=np.uint8)
+                    mask_poly = cv2.fillPoly(mask_poly, pts=[cont - [x, y]], color=1)
+                    img_crop[mask_poly == 0] = 255 # FIXME: or median color?
+
+                if h > 0.1 * w:
+                    cropped_lines.append(resize_image(img_crop,
+                                                        tr_ocr_input_height_and_width,
+                                                        tr_ocr_input_height_and_width)  )
+                    cropped_lines_meging_indexing.append(0)
+                else:
+                    splited_images, _ = return_textlines_split_if_needed(img_crop, None)
+                    if splited_images:
+                        cropped_lines.append(resize_image(splited_images[0],
+                                                            tr_ocr_input_height_and_width,
+                                                            tr_ocr_input_height_and_width))
+                        cropped_lines_meging_indexing.append(1)
+                        cropped_lines.append(resize_image(splited_images[1],
+                                                            tr_ocr_input_height_and_width,
+                                                            tr_ocr_input_height_and_width))
+                        cropped_lines_meging_indexing.append(-1)
+                    else:
+                        cropped_lines.append(img_crop)
+                        cropped_lines_meging_indexing.append(0)
+
+
+        self.logger.debug("processing %d lines for %d regions",
+                          len(cropped_lines), len(set(cropped_lines_region_indexer)))
+        for imgs in batched(cropped_lines, self.b_s):
+            pixel_values = self.model_zoo.get('trocr_processor')(
+                imgs, return_tensors="pt").pixel_values
+            output = self.model_zoo.get('ocr').generate(
+                pixel_values.to(self.device),
+                # beam search instead of greedy decoding:
+                num_beams=4,
+                # also return probability
+                output_scores=True,
+                return_dict_in_generate=True)
+            if output.sequences_scores is not None:
+                # log-prob averaged over length
+                conf = output.sequences_scores.exp().clamp(0.0, 1.0).tolist()
+            else:
+                conf = [1.0] * len(output.sequences)
+            text = self.model_zoo.get('trocr_processor').batch_decode(
+                output.sequences,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False)
+            extracted_confs.extend(conf)
+            extracted_texts.extend(text)
         del cropped_lines
         gc.collect()
 
         extracted_texts_merged = [extracted_texts[ind]
-                                    if cropped_lines_meging_indexing[ind]==0
-                                    else extracted_texts[ind]+" "+extracted_texts[ind+1]
-                                    if cropped_lines_meging_indexing[ind]==1
-                                    else None
-                                    for ind in range(len(cropped_lines_meging_indexing))]
-
-        extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
-        #print(extracted_texts_merged, len(extracted_texts_merged))
+                                  if cropped_lines_meging_indexing[ind] == 0
+                                  else extracted_texts[ind] + " " + extracted_texts[ind + 1]
+                                  for ind in range(len(cropped_lines_meging_indexing))
+                                  if cropped_lines_meging_indexing[ind] >= 0]
+        extracted_confs_merged = [extracted_confs[ind]
+                                  if cropped_lines_meging_indexing[ind] == 0
+                                  else 0.5 * (extracted_confs[ind] + extracted_confs[ind + 1])
+                                  for ind in range(len(cropped_lines_meging_indexing))
+                                  if cropped_lines_meging_indexing[ind] >= 0]
 
         return EynollahOcrResult(
             extracted_texts_merged=extracted_texts_merged,
-            extracted_conf_value_merged=None,
+            extracted_conf_value_merged=extracted_confs_merged,
             cropped_lines_region_indexer=cropped_lines_region_indexer,
             total_bb_coordinates=total_bb_coordinates,
         )
@@ -717,6 +635,7 @@ class Eynollah_ocr:
             
             has_textline = False
             for child_textregion in nn:
+                # FIXME: should remove Word level, if it already exists
                 if child_textregion.tag.endswith("TextLine"):
                     
                     is_textline_text = False
@@ -754,6 +673,7 @@ class Eynollah_ocr:
                 indexer_textregion = indexer_textregion + 1
                 
         ET.register_namespace("",page_ns)
+        self.logger.info("output filename: '%s'", out_file_ocr)
         page_tree.write(out_file_ocr, xml_declaration=True, method='xml', encoding="utf-8", default_namespace=None)
 
     def run(
diff --git a/src/eynollah/mb_ro_on_layout.py b/src/eynollah/mb_ro_on_layout.py
index b0b5910..6c0477b 100644
--- a/src/eynollah/mb_ro_on_layout.py
+++ b/src/eynollah/mb_ro_on_layout.py
@@ -17,9 +17,7 @@ import cv2
 import numpy as np
 import statistics
 
-os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
-import tensorflow as tf
-
+from .eynollah import Eynollah
 from .model_zoo import EynollahModelZoo
 from .utils.resize import resize_image
 from .utils.contour import (
@@ -33,23 +31,27 @@ DPI_THRESHOLD = 298
 KERNEL = np.ones((5, 5), np.uint8)
 
 
-class machine_based_reading_order_on_layout:
+class Reorder(Eynollah):
     def __init__(
-        self,
-        *,
-        model_zoo: EynollahModelZoo,
-        logger : Optional[logging.Logger] = None,
+            self,
+            *,
+            model_zoo: EynollahModelZoo,
+            logger : Optional[logging.Logger] = None,
+            device: str = '',
     ):
         self.logger = logger or logging.getLogger('eynollah.mbreorder')
         self.model_zoo = model_zoo
         
-        try:
-            for device in tf.config.list_physical_devices('GPU'):
-                tf.config.experimental.set_memory_growth(device, True)
-        except:
-            self.logger.warning("no GPU device available")
-            
-        self.model_zoo.load_models('reading_order')
+        self.model_zoo.load_model('reading_order')
+        self.setup_models(device=device)
+
+    def setup_models(self, device=''):
+        loadable = ['reading_order']
+        self.model_zoo.load_models(*loadable, device=device)
+        for model in loadable:
+            self.logger.debug("model %s has input shape %s", model,
+                              self.model_zoo.get(model).input_shape)
+        
 
     def read_xml(self, xml_file):
         tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8'))
@@ -675,7 +677,7 @@ class machine_based_reading_order_on_layout:
                 tot_counter += 1
                 batch.append(j)
                 if tot_counter % inference_bs == 0 or tot_counter == len(ij_list):
-                    y_pr = self.model_zoo.get('reading_order').predict(input_1 , verbose='0')
+                    y_pr = self.model_zoo.get('reading_order').predict(input_1, verbose=0)
                     for jb, j in enumerate(batch):
                         if y_pr[jb][0]>=0.5:
                             post_list.append(j)
diff --git a/src/eynollah/model_zoo/model_zoo.py b/src/eynollah/model_zoo/model_zoo.py
index 9611388..0a68203 100644
--- a/src/eynollah/model_zoo/model_zoo.py
+++ b/src/eynollah/model_zoo/model_zoo.py
@@ -14,6 +14,19 @@ from .default_specs import DEFAULT_MODEL_SPECS
 from .types import AnyModel, T
 
 
+MODEL_VRAM_LIMITS = {
+    "binarization": 868, # due to bs 5
+    "enhancement": 980, # due to bs 3
+    "col_classifier": 210,
+    "page": 618,
+    "textline": 1680, # 954 for bs 1
+    "region_1_2": 1580,
+    "region_fl_np": 1756,
+    "table": 1818,
+    "reading_order": 632,
+    "ocr": 850,
+}
+
 class EynollahModelZoo:
     """
     Wrapper class that handles storage and loading of models for all eynollah runners.
@@ -35,7 +48,7 @@ class EynollahModelZoo:
         self._overrides = []
         if model_overrides:
             self.override_models(*model_overrides)
-        self._loaded: Dict[str, Predictor] = {}
+        self._loaded: Dict[str, Union[Predictor, AnyModel]] = {}
 
     @property
     def model_overrides(self):
@@ -70,6 +83,13 @@ class EynollahModelZoo:
             model_path = Path(self.model_basedir).joinpath(spec.filename)
         else:
             model_path = Path(spec.filename)
+        if model_path.suffix == '.h5' and Path(model_path.stem).exists():
+            # prefer SavedModel over HDF5 format if it exists
+            model_path = Path(model_path.stem)
+        if model_path.with_suffix('.onnx').exists():
+            # prefer ONNX over SavedModel format if it exists
+            model_path = model_path.with_suffix('.onnx')
+
         return model_path
 
     def load_models(
@@ -82,32 +102,50 @@ class EynollahModelZoo:
         """
         ret = {} # cannot use self._loaded here, yet – first spawn all predictors
         for load_args in all_load_args:
+            load_kwargs = dict(device=device)
             if isinstance(load_args, str):
-                model_category = load_args
-                load_args = [model_category]
+                model_category, model_variant = load_args, ""
+            elif len(load_args) > 2:
+                # for calls to self.model_path
+                self.override_models(load_args)
+                # for calls to Predictor.load_model
+                model_category, model_variant, model_path = load_args
+                load_kwargs["model_variant"] = model_variant
+                load_kwargs["model_path_override"] = model_path
             else:
-                model_category = load_args[0]
-            load_kwargs = {}
+                model_category, model_variant = load_args
+                load_kwargs["model_variant"] = model_variant
+
             if model_category.endswith('_resized'):
-                load_args[0] = model_category[:-8]
+                model_category = model_category[:-8]
                 load_kwargs["resized"] = True
             elif model_category.endswith('_patched'):
-                load_args[0] = model_category[:-8]
+                model_category = model_category[:-8]
                 load_kwargs["patched"] = True
-            spec = self.specs.get(model_category, load_args[1] if len(load_args) > 1 else '')
-            if spec.type in ['Keras'] and spec.category != 'ocr':
-                ret[model_category] = Predictor(self.logger, self)
-                ret[model_category].load_model(*load_args, **load_kwargs, device=device)
+
+            if model_category == 'ocr':
+                model = self._load_ocr_model(variant=model_variant, device=device)
+            elif model_category == 'num_to_char':
+                model = self._load_num_to_char()
+            elif model_category == 'characters':
+                model = self._load_characters()
+            elif model_category == 'trocr_processor':
+                from transformers import TrOCRProcessor
+                model_path = self.model_path(model_category, model_variant)
+                model = TrOCRProcessor.from_pretrained(model_path)
             else:
-                ret[model_category] = self.load_model(*load_args, **load_kwargs, device=device)
+                model = Predictor(self.logger, self)
+                model.load_model(model_category, **load_kwargs)
+
+            ret[model_category] = model
         self._loaded.update(ret)
         return self._loaded
 
     def load_model(
-        self,
-        model_category: str,
-        model_variant: str = '',
-        model_path_override: Optional[str] = None,
+            self,
+            model_category: str,
+            model_variant: str = '',
+            model_path_override: Optional[str] = None,
             patched: bool = False,
             resized: bool = False,
             device: str = '',
@@ -115,24 +153,39 @@ class EynollahModelZoo:
         """
         Load any model
         """
-        os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
+        if model_path_override:
+            self.override_models((model_category, model_variant, model_path_override))
+        model_path = self.model_path(model_category, model_variant)
+
+        if model_path.is_dir() and (model_path / "keras_metadata.pb").exists():
+            # Keras model
+            model = self._load_keras_model(model_category, model_path, device=device)
+        elif model_path.is_dir():
+            # TF-Serving model
+            model = self._load_serving_model(model_category, model_path, device=device)
+        elif model_path.suffix == '.onnx':
+            # ONNX model
+            model = self._load_onnx_model(model_category, model_path, device=device)
+        else:
+            raise ValueError("unknown model type for '%s'" % str(model_path))
+        model._name = model_category
+        return model
+
+    def get(self, model_category: str) -> Union[Predictor, AnyModel]:
+        if model_category not in self._loaded:
+            raise ValueError(f'Model "{model_category}" not previously loaded with "load_model(..)"')
+        return self._loaded[model_category]
+
+    def _configure_tf_device(self, model_category, device=''):
         from ocrd_utils import tf_disable_interactive_logs
         tf_disable_interactive_logs()
-
         import tensorflow as tf
-        from tensorflow.keras.models import load_model
 
-        from ..patch_encoder import (
-            PatchEncoder,
-            Patches,
-            wrap_layout_model_patched,
-            wrap_layout_model_resized,
-        )
         cuda = False
         try:
             gpus = tf.config.list_physical_devices('GPU')
             if device:
-                if ',' in device:
+                if ':' in device:
                     for spec in device.split(','):
                         cat, dev = spec.split(':')
                         if fnmatchcase(model_category, cat):
@@ -147,7 +200,14 @@ class EynollahModelZoo:
                 gpus = gpus[:1] # TF will always use first allowable
             tf.config.set_visible_devices(gpus, 'GPU')
             for device in gpus:
-                tf.config.experimental.set_memory_growth(device, True)
+                # tf.config.experimental.set_memory_growth(device, True)
+                # dynamic growth never frees memory (to avoid fragmentation),
+                # so the VRAM requirements end up much larger than feasible
+                # (for small GPUs); so try hard (calibrated) limits instead:
+                tf.config.set_logical_device_configuration(
+                    device,
+                    [tf.config.LogicalDeviceConfiguration(
+                        memory_limit=MODEL_VRAM_LIMITS[model_category])])
                 vendor_name = (
                     tf.config.experimental.get_device_details(device)
                     .get('device_name', 'unknown'))
@@ -155,76 +215,159 @@ class EynollahModelZoo:
                 self.logger.info("using GPU %s (%s) for model %s",
                                  device.name,
                                  vendor_name,
-                                 model_category + (
-                                     "_patched" if patched else
-                                     "_resized" if resized else ""))
+                                 model_category # + (
+                                     # "_patched" if patched else
+                                     # "_resized" if resized else "")
+                )
         except RuntimeError:
             self.logger.exception("cannot configure GPU devices")
         if not cuda:
             self.logger.warning("no GPU device available")
 
-        if model_path_override:
-            self.override_models((model_category, model_variant, model_path_override))
-        model_path = self.model_path(model_category, model_variant)
-        if model_path.suffix == '.h5' and Path(model_path.stem).exists():
-            # prefer SavedModel over HDF5 format if it exists
-            model_path = Path(model_path.stem)
+    def _load_keras_model(self, model_category, model_path, device=''):
+        os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
+        from ocrd_utils import tf_disable_interactive_logs
+        tf_disable_interactive_logs()
+
+        from tensorflow.keras.models import load_model
+        from tensorflow.keras.models import Model as KerasModel
+
+        self._configure_tf_device(model_category, device=device)
+
+        model = load_model(model_path, compile=False)
+
+        # from ..patch_encoder import (
+        #     wrap_layout_model_patched,
+        #     wrap_layout_model_resized,
+        # )
+        # if resized:
+        #     model = wrap_layout_model_resized(model)
+        #     model._name = model_category + '_resized'
+        # elif patched:
+        #     model = wrap_layout_model_patched(model)
+        #     model._name = model_category + '_patched'
+
         if model_category == 'ocr':
-            model = self._load_ocr_model(variant=model_variant)
-        elif model_category == 'num_to_char':
-            model = self._load_num_to_char()
-        elif model_category == 'characters':
-            model = self._load_characters()
-        elif model_category == 'trocr_processor':
-            from transformers import TrOCRProcessor
-            model = TrOCRProcessor.from_pretrained(model_path)
-        else:
+            # cnn-rnn-ocr task model may not be in inference mode, yet
             try:
-                # avoid wasting VRAM on non-transformer models
-                model = load_model(model_path, compile=False)
-            except Exception as e:
-                self.logger.error(e)
-                model = load_model(
-                    model_path, compile=False,
-                    custom_objects=dict(PatchEncoder=PatchEncoder,
-                                        Patches=Patches))
-            model._name = model_category
-            if resized:
-                model = wrap_layout_model_resized(model)
-                model._name = model_category + '_resized'
-            elif patched:
-                model = wrap_layout_model_patched(model)
-                model._name = model_category + '_patched'
+                model.get_layer(name='ctc_loss')
+            except ValueError:
+                pass
             else:
-                model.jit_compile = True
-            model.make_predict_function()
+                model = KerasModel(
+                    model.get_layer(name="image").input,   # type: ignore
+                    model.get_layer(name="dense2").output, # type: ignore
+                )
+
+        model.make_predict_function()
+
         return model
 
-    def get(self, model_category: str) -> Predictor:
-        if model_category not in self._loaded:
-            raise ValueError(f'Model "{model_category}" not previously loaded with "load_model(..)"')
-        return self._loaded[model_category]
+    def _load_serving_model(self, model_category, model_path, device=''):
+        from ocrd_utils import tf_disable_interactive_logs
+        tf_disable_interactive_logs()
+        import tensorflow as tf
 
-    def _load_ocr_model(self, variant: str) -> AnyModel:
+        self._configure_tf_device(model_category, device=device)
+        model = tf.saved_model.load(model_path)
+        model.predict_on_batch = model.serve
+        model.input_shape = tuple(model.signatures.get('serving_default').inputs[0].shape)
+
+        return model
+
+    def _load_onnx_model(self, model_category, model_path, device=''):
+        import onnxruntime as ort
+        import numpy as np
+
+        providers = ort.get_available_providers()
+        if device:
+            if ':' in device:
+                for spec in device.split(','):
+                    cat, dev = spec.split(':')
+                    if fnmatchcase(model_category, cat):
+                        device = dev
+                        break
+            if device == 'CPU':
+                gpu = -1
+            else:
+                assert device.startswith('GPU')
+                gpu = int(device[3:] or "0")
+        else:
+            gpu = 0 # try first allowable
+        # configure and prioritise
+        if 'CUDAExecutionProvider' in providers:
+            providers.remove('CUDAExecutionProvider')
+            if gpu >= 0:
+                providers = [('CUDAExecutionProvider', {
+                    'device_id': gpu,
+                    # 'arena_extend_strategy': 'kNextPowerOfTwo',
+                    'gpu_mem_limit': MODEL_VRAM_LIMITS[model_category] * 1024 * 1024,
+                    # 'cudnn_conv_algo_search': 'EXHAUSTIVE',
+                    # 'do_copy_in_default_stream': True,
+                    # ...
+                })] + providers
+        if 'TensorrtExecutionProvider' in providers:
+            providers.remove('TensorrtExecutionProvider')
+            if gpu >= 0:
+                providers = [('TensorrtExecutionProvider', {
+                    'device_id': gpu,
+                    'trt_max_workspace_size': MODEL_VRAM_LIMITS[model_category] * 1024 * 1024,
+                    # 'trt_fp16_enable': True,
+                    # 'trt_engine_cache_enable': True,
+                    # 'trt_timing_cache_enable': True,
+                    # ...
+                })] + providers
+        model = ort.InferenceSession(
+            model_path,
+            providers=providers)
+        # FIXME: notify about selected provider/device
+        input_name = model.get_inputs()[0].name
+        output_name = model.get_outputs()[0].name
+        def predict_onnx(inputs):
+            # models expect data_type() == 'tensor(float)', but np.float16 is 'tensor(float16)'
+            # FIXME: do this dynamically (but how to convert .type to np.dtype?)
+            inputs = inputs.astype(np.float32)
+            return model.run(
+                [output_name], {input_name: inputs})[0]
+        model.predict_on_batch = predict_onnx
+        model.input_shape = model.get_inputs()[0].shape
+
+        return model
+
+    def _load_ocr_model(self, variant: str, device: str = "") -> AnyModel:
         """
         Load OCR model
         """
-        from tensorflow.keras.models import Model as KerasModel
-        from tensorflow.keras.models import load_model
-
-        ocr_model_dir = self.model_path('ocr', variant)
+        model_dir = self.model_path('ocr', variant)
         if variant == 'tr':
             from transformers import VisionEncoderDecoderModel
-            ret = VisionEncoderDecoderModel.from_pretrained(ocr_model_dir)
-            assert isinstance(ret, VisionEncoderDecoderModel)
-            return ret
-        else:
-            ocr_model = load_model(ocr_model_dir, compile=False)
-            assert isinstance(ocr_model, KerasModel)
-            return KerasModel(
-                ocr_model.get_layer(name="image").input,   # type: ignore
-                ocr_model.get_layer(name="dense2").output, # type: ignore
-            )
+            import torch
+            model = VisionEncoderDecoderModel.from_pretrained(model_dir)
+            assert isinstance(model, VisionEncoderDecoderModel)
+            device0 = torch.device('cpu')
+            if not device and torch.cuda.is_available():
+                device = 'GPU' # try
+            if device and ':' in device:
+                for spec in device.split(','):
+                    cat, dev = spec.split(':')
+                    if fnmatchcase('ocr', cat):
+                        device = dev
+                        break
+            if device and device.startswith('GPU'):
+                try:
+                    device0 = torch.device('cuda', int(device[3:] or 0))
+                    name = torch.cuda.get_device_name(device0)
+                    self.logger.info("using GPU %s (%s) for model ocr:tr", device0, name)
+                except:
+                    self.logger.exception("cannot configure GPU device")
+                    device0 = torch.device('cpu')
+            if device0.type == 'cuda':
+                model.to(device0)
+            else:
+                self.logger.warning("no GPU device available")
+            return model
+
+        return self.load_model('ocr', model_variant=variant, device=device)
 
     def _load_characters(self) -> List[str]:
         """
@@ -237,6 +380,10 @@ class EynollahModelZoo:
         """
         Load decoder for OCR
         """
+        os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
+        from ocrd_utils import tf_disable_interactive_logs
+        tf_disable_interactive_logs()
+
         from tensorflow.keras.layers import StringLookup
 
         characters = self._load_characters()
@@ -277,5 +424,5 @@ class EynollahModelZoo:
         """
         if hasattr(self, '_loaded') and getattr(self, '_loaded'):
             for needle in list(self._loaded.keys()):
-                self._loaded[needle].shutdown()
-                del self._loaded[needle]
+                if isinstance(self._loaded[needle], Predictor):
+                    self._loaded[needle].shutdown()
diff --git a/src/eynollah/ocrd_cli.py b/src/eynollah/ocrd_cli.py
index acd8d4e..effecb2 100644
--- a/src/eynollah/ocrd_cli.py
+++ b/src/eynollah/ocrd_cli.py
@@ -1,10 +1,8 @@
-# NOTE: For predictable order of imports of torch/shapely/tensorflow
-#       this must be the first import of the CLI!
-from .eynollah_imports import imported_libs
-from .processor import EynollahProcessor
 from click import command
 from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor
 
+from .processor import EynollahProcessor
+
 @command()
 @ocrd_cli_options
 def main(*args, **kwargs):
diff --git a/src/eynollah/patch_encoder.py b/src/eynollah/patch_encoder.py
index f163132..610f0b4 100644
--- a/src/eynollah/patch_encoder.py
+++ b/src/eynollah/patch_encoder.py
@@ -6,8 +6,8 @@ from tensorflow.keras import layers, models
 class PatchEncoder(layers.Layer):
 
     # 441=21*21 # 14*14 # 28*28
-    def __init__(self, num_patches=441, projection_dim=64):
-        super().__init__()
+    def __init__(self, num_patches=441, projection_dim=64, name='encode_patches'):
+        super().__init__(name=name)
         self.num_patches = num_patches
         self.projection_dim = projection_dim
         self.projection = layers.Dense(self.projection_dim)
@@ -23,8 +23,8 @@ class PatchEncoder(layers.Layer):
                     **super().get_config())
 
 class Patches(layers.Layer):
-    def __init__(self, patch_size_x=1, patch_size_y=1):
-        super().__init__()
+    def __init__(self, patch_size_x=1, patch_size_y=1, name='extract_patches'):
+        super().__init__(name=name)
         self.patch_size_x = patch_size_x
         self.patch_size_y = patch_size_y
 
diff --git a/src/eynollah/predictor.py b/src/eynollah/predictor.py
index e1159e7..3c6890e 100644
--- a/src/eynollah/predictor.py
+++ b/src/eynollah/predictor.py
@@ -194,17 +194,18 @@ class Predictor(mp.context.SpawnProcess):
 
     def shutdown(self):
         # do not terminate from forked processor instances
-        if mp.parent_process() is None:
+        if not hasattr(self, 'model'):
             self.stopped.set()
+            self.join()
             self.taskq.close()
             self.taskq.cancel_join_thread()
             self.resultq.close()
             self.resultq.cancel_join_thread()
             self.logq.close()
-            self.terminate()
+            #self.terminate()
         else:
             del self.model
 
     def __del__(self):
-        #self.logger.debug(f"deinit of {self} in {mp.current_process().name}")
+        #self.logger.debug(f"deinit of {self.name} in {mp.current_process().name}")
         self.shutdown()
diff --git a/src/eynollah/training/cli.py b/src/eynollah/training/cli.py
index ae14f04..ccabb82 100644
--- a/src/eynollah/training/cli.py
+++ b/src/eynollah/training/cli.py
@@ -7,17 +7,11 @@ import sys
 from .build_model_load_pretrained_weights_and_save import build_model_load_pretrained_weights_and_save
 from .generate_gt_for_training import main as generate_gt_cli
 from .inference import main as inference_cli
-from .train import ex
+from .train import train_cli
+from .convert import convert_cli
 from .extract_line_gt import linegt_cli
 from .weights_ensembling import ensemble_cli
 
-@click.command(context_settings=dict(
-        ignore_unknown_options=True,
-))
-@click.argument('SACRED_ARGS', nargs=-1, type=click.UNPROCESSED)
-def train_cli(sacred_args):
-    ex.run_commandline([sys.argv[0]] + list(sacred_args))
-
 @click.group('training')
 def main():
     pass
@@ -26,5 +20,6 @@ main.add_command(build_model_load_pretrained_weights_and_save)
 main.add_command(generate_gt_cli, 'generate-gt')
 main.add_command(inference_cli, 'inference')
 main.add_command(train_cli, 'train')
+main.add_command(convert_cli, 'convert')
 main.add_command(linegt_cli, 'export_textline_images_and_text')
 main.add_command(ensemble_cli, 'ensembling')
diff --git a/src/eynollah/training/convert.py b/src/eynollah/training/convert.py
new file mode 100644
index 0000000..dd4271f
--- /dev/null
+++ b/src/eynollah/training/convert.py
@@ -0,0 +1,107 @@
+import os
+from pathlib import Path
+from shutil import copy2
+import logging
+
+import click
+
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
+@click.option(
+    "--rebuild",
+    "-r",
+    help="build new model from code and then load existing weights (requires input in SavedModel directory format with config.json present)",
+    is_flag=True
+)
+@click.option(
+    "--format",
+    "-f",
+    "format_",
+    help="data format to convert to",
+    type=click.Choice(["hdf5", "keras", "tf", "tf-serving", "onnx"]),
+    default="tf"
+)
+@click.option(
+    "--in",
+    "-i",
+    "in_",
+    help="path to input model (file in hdf5 / keras format, or directory in tf format)",
+    required=True,
+    type=click.Path(exists=True, dir_okay=True)
+)
+@click.option(
+    "--out",
+    "-o",
+    help="path to output model  (file in hdf5 / keras / onnx format, or directory in tf / tf-serving format)",
+    required=True,
+    type=click.Path(exists=False, dir_okay=True)
+)
+def convert_cli(rebuild, format_, in_, out):
+    """
+    convert models for inference
+
+    Load model from path, optionally by rebuilding, convert to output format and write model to path.
+    """
+    os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
+    from ocrd_utils import tf_disable_interactive_logs
+    tf_disable_interactive_logs()
+
+    import tensorflow as tf
+    from tensorflow.keras.models import load_model
+    from tensorflow.keras.models import Model as KerasModel
+    
+    model_path = Path(in_)
+    config_path = model_path / "config.json"
+    if model_path.is_dir():
+        assert (model_path / "keras_metadata.pb").exists(), (
+            "input directory must be Keras model in SavedModel format")
+    if rebuild:
+        from .train import ex
+        from .models import get_model
+
+        assert config_path.exists(), (
+            "rebuilding requires input model in SavedModel format with config.json")
+
+        # merge defaults with existing config file
+        ex.add_config(str(config_path))
+        # some models deviate between training and inference
+        ex.add_config(inference=True)
+        # just retrieve final config (via pseudo-run)
+        ex.main(lambda: 0)
+        config = ex.run(options={'--loglevel': 'ERROR'}).config
+        # use the config to capture the model builder
+        model = get_model(config, logging.root)
+        model.load_weights(model_path).assert_existing_objects_matched().expect_partial()
+    else:
+        model = load_model(model_path, compile=False)
+
+        if isinstance(model, KerasModel):
+            # cnn-rnn-ocr task deviates between training and inference
+            try:
+                model.get_layer(name='ctc_loss')
+            except ValueError:
+                pass
+            else:
+                model = KerasModel(
+                    model.get_layer(name='image').input,
+                    model.get_layer(name='dense2').output)
+
+    if format_ in ["hdf5", "keras", "tf"]:
+        kwargs = {"save_format": {"hdf5": "h5"}.get(format_, format_)}
+        if format_ != "keras":
+            kwargs["include_optimizer"] = False
+        model.save(out, **kwargs)
+    elif format_ == "tf-serving":
+        model.export(out)
+    elif format_ == "onnx":
+        import tf2onnx
+        tf2onnx.convert.from_keras(model, opset=18, output_path=out)
+    else:
+        raise ValueError("unknown output format '%s'" % format_)
+
+    # copy config.json if possible
+    if config_path.exists() and format_ in ['tf', 'tf-serving']:
+        copy2(config_path, Path(out) / config_path.name)
+
+        
diff --git a/src/eynollah/training/models.py b/src/eynollah/training/models.py
index 3494249..83058ee 100644
--- a/src/eynollah/training/models.py
+++ b/src/eynollah/training/models.py
@@ -309,11 +309,10 @@ def transformer_block(img,
         # Skip connection 2.
         encoded_patches = Add()([x3, x2])
 
-    encoded_patches = tf.reshape(encoded_patches,
-                                 [-1,
-                                  img.shape[1],
-                                  img.shape[2],
-                                  projection_dim // (patchsize_x * patchsize_y)])
+    encoded_patches = Reshape((img.shape[1],
+                               img.shape[2],
+                               projection_dim // (patchsize_x * patchsize_y)),
+                              name="reshape_patches")(encoded_patches)
     return encoded_patches
 
 def vit_resnet50_unet(num_patches,
@@ -423,11 +422,11 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224
 
     return model
 
-def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_seq=None):
-    input_img = Input(shape=(image_height, image_width, 3), name="image")
+def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_len=None, inference=False):
+    inputs = Input(shape=(image_height, image_width, 3), name="image")
     labels = Input(name="label", shape=(None,))
 
-    x = Conv2D(64,kernel_size=(3,3),padding="same")(input_img)
+    x = Conv2D(64,kernel_size=(3,3),padding="same")(inputs)
     x = BatchNormalization(name="bn1")(x)
     x = Activation("relu", name="relu1")(x)
     x = Conv2D(64,kernel_size=(3,3),padding="same")(x)
@@ -459,44 +458,93 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_s
     x = Activation("relu", name="relu8")(x)
     x2d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x)
     x4d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x2d)
-    
 
     new_shape = (x.shape[1]*x.shape[2], x.shape[3])
     new_shape2 = (x2d.shape[1]*x2d.shape[2], x2d.shape[3])
     new_shape4 = (x4d.shape[1]*x4d.shape[2], x4d.shape[3])
-    
-    x = Reshape(target_shape=new_shape, name="reshape")(x)
-    x2d = Reshape(target_shape=new_shape2, name="reshape2")(x2d)
-    x4d = Reshape(target_shape=new_shape4, name="reshape4")(x4d)
-    
+
+    x = Reshape(new_shape, name="reshape")(x)
+    x2d = Reshape(new_shape2, name="reshape2")(x2d)
+    x4d = Reshape(new_shape4, name="reshape4")(x4d)
+
     xrnnorg = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x)
     xrnn2d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x2d)
     xrnn4d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x4d)
-    
-    xrnn2d = Reshape(target_shape=(1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
-    xrnn4d = Reshape(target_shape=(1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
-    
+
+    xrnn2d = Reshape((1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
+    xrnn4d = Reshape((1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
 
     xrnn2dup = UpSampling2D(size=(1, 2), interpolation="nearest")(xrnn2d)
     xrnn4dup = UpSampling2D(size=(1, 4), interpolation="nearest")(xrnn4d)
-    
-    xrnn2dup = Reshape(target_shape=(xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup)
-    xrnn4dup = Reshape(target_shape=(xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup)
+
+    xrnn2dup = Reshape((xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup)
+    xrnn4dup = Reshape((xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup)
 
     addition = Add()([xrnnorg, xrnn2dup, xrnn4dup])
-    
+
     addition_rnn = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(addition)
-    
-    out = Conv1D(max_seq, 1, data_format="channels_first")(addition_rnn)
+
+    out = Conv1D(max_len, 1, data_format="channels_first")(addition_rnn)
     out = BatchNormalization(name="bn9")(out)
     out = Activation("relu", name="relu9")(out)
     #out = Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)
 
     out = Dense(n_classes, activation="softmax", name="dense2")(out)
 
-    # Add CTC layer for calculating CTC loss at each step.
-    output = CTCLayer(name="ctc_loss")(labels, out)
-    
-    model = Model(inputs=(input_img, labels), outputs=output, name="handwriting_recognizer")
+    if inference:
+        return Model(inputs, out)
 
-    return model
+    # Add CTC layer for calculating CTC loss at each step.
+    out = CTCLayer(name="ctc_loss")(labels, out)
+    
+    return Model((inputs, labels), out)
+
+def get_model(config, logger):
+    from sacred.config import create_captured_function
+
+    task = config['task']
+    if task in ["segmentation", "enhancement", "binarization"]:
+        if config['backbone_type'] == 'nontransformer':
+            builder = resnet50_unet
+        else:
+            num_patches_x, num_patches_y = config['transformer_num_patches_xy']
+            num_patches = num_patches_x * num_patches_y
+
+            if config['transformer_cnn_first']:
+                builder = vit_resnet50_unet
+                multiple = 32
+            else:
+                builder = vit_resnet50_unet_transformer_before_cnn
+                multiple = 1
+
+            assert config['input_height'] == (
+                num_patches_y * config['transformer_patchsize_y'] * multiple), (
+                "transformer_patchsize_y or transformer_num_patches_xy height value error: "
+                "input_height should be equal to "
+                "(transformer_num_patches_xy height value * transformer_patchsize_y * %d)" % multiple)
+            assert config['input_width'] == (
+                num_patches_x * config['transformer_patchsize_x'] * multiple), (
+                    "transformer_patchsize_x or transformer_num_patches_xy width value error: "
+                    "input_width should be equal to "
+                    "(transformer_num_patches_xy width value * transformer_patchsize_x * %d)" % multiple)
+            assert 0 == (config['transformer_projection_dim'] %
+                         (config['transformer_patchsize_y'] *
+                          config['transformer_patchsize_x'])), (
+                             "transformer_projection_dim error: "
+                             "The remainder when parameter transformer_projection_dim is divided by "
+                             "(transformer_patchsize_y*transformer_patchsize_x) should be zero")
+
+            config['num_patches'] = num_patches
+    elif task == "cnn-rnn-ocr":
+        builder = cnn_rnn_ocr_model
+    elif task=='classification':
+        builder = resnet50_classifier
+    elif task=='reading_order':
+        builder = machine_based_reading_order_model
+    else:
+        raise ValueError("unknown model task '%s'" % task)
+
+    builder = create_captured_function(builder)
+    builder.config = config
+    builder.logger = logger
+    return builder()
diff --git a/src/eynollah/training/reload-models-v0.8.mk b/src/eynollah/training/reload-models-v0.8.mk
index b7a38dd..9855f0f 100644
--- a/src/eynollah/training/reload-models-v0.8.mk
+++ b/src/eynollah/training/reload-models-v0.8.mk
@@ -4,38 +4,65 @@ MODELS_SRC = models_eynollah
 MODELS_DST = reloaded/models_eynollah
 
 
-#	$(MODELS_DST)/eynollah-binarization_20210425 \
-#	$(MODELS_DST)/eynollah-column-classifier_20210425 \
-#	$(MODELS_DST)/eynollah-enhancement_20210425 \
-#	$(MODELS_DST)/eynollah-main-regions-aug-rotation_20210425 \
-#	$(MODELS_DST)/eynollah-main-regions-aug-scaling_20210425 \
-#	$(MODELS_DST)/eynollah-main-regions-ensembled_20210425 \
-#	$(MODELS_DST)/eynollah-main-regions_20220314 \
-#	$(MODELS_DST)/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18 \
-#	$(MODELS_DST)/eynollah-tables_20210319 \
-#	$(MODELS_DST)/model_eynollah_ocr_cnnrnn_20250930 \
+# eynollah-main-regions-aug-rotation_20210425
+# eynollah-main-regions-aug-scaling_20210425
+# eynollah-main-regions-ensembled_20210425
+# eynollah-main-regions_20220314
+# eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18
+# eynollah-tables_20210319
 
-RELOADABLE_MODELS = \
-	$(MODELS_DST)/model_eynollah_page_extraction_20250915 \
-	$(MODELS_DST)/model_eynollah_reading_order_20250824 \
-	$(MODELS_DST)/modelens_e_l_all_sp_0_1_2_3_4_171024 \
-	$(MODELS_DST)/modelens_full_lay_1__4_3_091124 \
-	$(MODELS_DST)/modelens_table_0t4_201124 \
-	$(MODELS_DST)/modelens_textline_0_1__2_4_16092024
+CURRENT_MODELS := 
+CURRENT_MODELS += model_eynollah_page_extraction_20250915
+CURRENT_MODELS += model_eynollah_reading_order_20250824
+CURRENT_MODELS += modelens_e_l_all_sp_0_1_2_3_4_171024
+CURRENT_MODELS += modelens_full_lay_1__4_3_091124
+CURRENT_MODELS += modelens_table_0t4_201124
+CURRENT_MODELS += modelens_textline_0_1__2_4_16092024
+CURRENT_MODELS += model_eynollah_ocr_cnnrnn_20250930
+CURRENT_MODELS += eynollah-binarization_20210425
+CURRENT_MODELS += eynollah-column-classifier_20210425
+CURRENT_MODELS += eynollah-enhancement_20210425
 
-all: $(RELOADABLE_MODELS)
+all: tf-serving
+
+tf-serving: $(CURRENT_MODELS:%=$(MODELS_DST)/%)
+keras: $(CURRENT_MODELS:%=$(MODELS_DST)/%.keras)
+hdf5: $(CURRENT_MODELS:%=$(MODELS_DST)/%.h5)
+onnx: $(CURRENT_MODELS:%=$(MODELS_DST)/%.onnx)
 
 $(MODELS_DST)/%: $(MODELS_SRC)/%
-	mkdir -p $@
-	test -e $</config.json || exit 1
-	eynollah-training train --force \
-		with $</config.json \
-		reload_weights=True \
-		continue_training=False \
-		dir_output=$(dir $@) \
-		dir_of_start_model=$< \
-	2>&1 | tee $(notdir $<).log
-	cp $</config.json $@/config.json
+	eynollah-training convert \
+		$(and $(wildcard $</config.json),--rebuild) \
+		--in $< \
+		--format tf-serving \
+		--out $@ \
+	2>&1 | tee $(notdir $<).tf-serving.log
+
+$(MODELS_DST)/%.keras: $(MODELS_SRC)/%
+	eynollah-training convert \
+		$(and $(wildcard $</config.json),--rebuild) \
+		--in $< \
+		--format keras \
+		--out $@ \
+	2>&1 | tee $(notdir $<).keras.log
+
+$(MODELS_DST)/%.h5: $(MODELS_SRC)/%
+	eynollah-training convert \
+		$(and $(wildcard $</config.json),--rebuild) \
+		--in $< \
+		--format hdf5 \
+		--out $@ \
+	2>&1 | tee $(notdir $<).hdf5.log
+
+$(MODELS_DST)/%.onnx: $(MODELS_SRC)/%
+	if jq -e '.task == "segmentation" and .backbone_type == "transformer"' $</config.json &>/dev/null; then \
+	echo skipping $@: vision transformer architecture currently does not work with ONNX; else \
+	eynollah-training convert \
+		$(and $(wildcard $</config.json),--rebuild) \
+		--in $< \
+		--format onnx \
+		--out $@ \
+	2>&1 | tee $(notdir $<).onnx.log; fi
 
 compare: 
 	for i in `find $(MODELS_DST) -mindepth 2`;do \
@@ -43,6 +70,5 @@ compare:
 		du -bs $$n $$i ; \
 	done
 
-
 clear:
 	rm -rf $(MODELS_DST)
diff --git a/src/eynollah/training/train.py b/src/eynollah/training/train.py
index de998fd..62d8e51 100644
--- a/src/eynollah/training/train.py
+++ b/src/eynollah/training/train.py
@@ -2,6 +2,7 @@ import os
 import sys
 import io
 import json
+import click
 
 from tqdm import tqdm
 import requests
@@ -17,7 +18,6 @@ from tensorflow.keras.layers import StringLookup
 from tensorflow.keras.utils import image_dataset_from_directory
 from tensorflow.keras.backend import one_hot
 from sacred import Experiment
-from sacred.config import create_captured_function
 
 import numpy as np
 import cv2
@@ -32,16 +32,9 @@ from .metrics import (
     connected_components_loss,
 )
 from .models import (
-    PatchEncoder,
-    Patches,
-    machine_based_reading_order_model,
-    resnet50_classifier,
-    resnet50_unet,
-    vit_resnet50_unet,
-    vit_resnet50_unet_transformer_before_cnn,
-    cnn_rnn_ocr_model,
     RESNET50_WEIGHTS_PATH,
-    RESNET50_WEIGHTS_URL
+    RESNET50_WEIGHTS_URL,
+    get_model
 )
 from .utils import (
     generate_arrays_from_folder_reading_order,
@@ -355,10 +348,9 @@ def config_params():
     dir_output = None  # Directory where the augmented training data and the model checkpoints will be saved.
     pretraining = False  # Set to true to (down)load pretrained weights of ResNet50 encoder.
     save_interval = None # frequency for writing model checkpoints (positive integer for number of batches saved under "model_step_{batch:04d}", otherwise epoch saved under "model_{epoch:02d}")
-    reload_weights = False # Set true to build new model from config, load weights from dir_of_start_model, save under dir_output and exit.
     continue_training = False  # Whether to continue training an existing model.
+    dir_of_start_model = ''  # Directory of model checkpoint to load to continue training or load weights from. (E.g. if you already trained for 3 epochs, set "dir_of_start_model=dir_output/model_03".)
     if continue_training:
-        dir_of_start_model = ''  # Directory of model checkpoint to load to continue training. (E.g. if you already trained for 3 epochs, set "dir_of_start_model=dir_output/model_03".)
         index_start = 0  #  Epoch counter initial value to continue training. (E.g. if you already trained for 3 epochs, set "index_start=3" to continue naming checkpoints model_04, model_05 etc.)
     data_is_provided = False  # Whether the preprocessed input data (subdirectories "images" and "labels" in both subdirectories "train" and "eval" of "dir_output") has already been generated (in the first epoch of a previous run).
 
@@ -379,7 +371,6 @@ def run(_config,
         weight_decay,
         learning_rate,
         continue_training,
-        reload_weights,
         save_interval,
         augmentation,
         # dependent config keys need a default,
@@ -477,58 +468,15 @@ def run(_config,
         if task == "enhancement":
             assert not is_loss_soft_dice, "for enhancement, soft_dice loss does not apply"
             assert not weighted_loss, "for enhancement, weighted loss does not apply"
+
         if continue_training:
-            custom_objects = dict()
-            if is_loss_soft_dice:
-                custom_objects.update(soft_dice_loss=soft_dice_loss)
-            elif weighted_loss:
-                custom_objects.update(loss=weighted_categorical_crossentropy(weights))
-            if backbone_type == 'transformer':
-                custom_objects.update(PatchEncoder=PatchEncoder,
-                                      Patches=Patches)
-            model = load_model(dir_of_start_model, compile=False,
-                               custom_objects=custom_objects)
+            model = load_model(dir_of_start_model, compile=False)
         else:
             index_start = 0
-            if backbone_type == 'nontransformer':
-                model = resnet50_unet(n_classes,
-                                      input_height,
-                                      input_width,
-                                      task,
-                                      weight_decay,
-                                      pretraining)
-            else:
-                num_patches_x = transformer_num_patches_xy[0]
-                num_patches_y = transformer_num_patches_xy[1]
-                num_patches = num_patches_x * num_patches_y
-
-                if transformer_cnn_first:
-                    model_builder = vit_resnet50_unet
-                    multiple = 32
-                else:
-                    model_builder = vit_resnet50_unet_transformer_before_cnn
-                    multiple = 1
-
-                assert input_height == (
-                    num_patches_y * transformer_patchsize_y * multiple), (
-                    "transformer_patchsize_y or transformer_num_patches_xy height value error: "
-                    "input_height should be equal to "
-                    "(transformer_num_patches_xy height value * transformer_patchsize_y * %d)" % multiple)
-                assert input_width == (
-                    num_patches_x * transformer_patchsize_x * multiple), (
-                        "transformer_patchsize_x or transformer_num_patches_xy width value error: "
-                        "input_width should be equal to "
-                        "(transformer_num_patches_xy width value * transformer_patchsize_x * %d)" % multiple)
-                assert 0 == (transformer_projection_dim %
-                             (transformer_patchsize_y * transformer_patchsize_x)), (
-                                 "transformer_projection_dim error: "
-                                 "The remainder when parameter transformer_projection_dim is divided by "
-                                 "(transformer_patchsize_y*transformer_patchsize_x) should be zero")
-
-                model_builder = create_captured_function(model_builder)
-                model_builder.config = _config
-                model_builder.logger = _log
-                model = model_builder(num_patches)
+            model = get_model(_config, _log)
+            if dir_of_start_model:
+                model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
+                _log.info("reloaded weights from %s", dir_of_start_model)
 
         assert model is not None
         #if you want to see the model structure just uncomment model summary.
@@ -559,15 +507,6 @@ def run(_config,
                       optimizer=Adam(learning_rate=learning_rate),
                       metrics=metrics)
 
-        if reload_weights:
-            model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
-            dir_save = os.path.join(dir_output, os.path.basename(os.path.normpath(dir_of_start_model)))
-            model.save(dir_save, include_optimizer=False)
-            with open(os.path.join(dir_save, "config.json"), "w") as fp:
-                json.dump(_config, fp)  # encode dict into JSON
-            _log.info("reloaded model from %s to %s", dir_of_start_model, dir_save)
-            return
-
         if not data_is_provided:
             # first create a directory in output for both training and evaluations
             # in order to flow data from these directories.
@@ -708,10 +647,11 @@ def run(_config,
             model = load_model(dir_of_start_model)
         else:
             index_start = 0
-            model = cnn_rnn_ocr_model(image_height=input_height,
-                                      image_width=input_width,
-                                      n_classes=n_classes,
-                                      max_seq=max_len)
+            model = get_model(_config, _log)
+            if dir_of_start_model:
+                model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
+                _log.info("reloaded weights from %s", dir_of_start_model)
+
         #initial_learning_rate = 1e-4
         #decay_steps = int (n_epochs * ( len_dataset / n_batch ))
         #alpha = 0.01
@@ -722,15 +662,6 @@ def run(_config,
 
         #print(model.summary())
 
-        if reload_weights:
-            model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
-            dir_save = os.path.join(dir_output, os.path.basename(os.path.normpath(dir_of_start_model)))
-            model.save(dir_save, include_optimizer=False)
-            with open(os.path.join(dir_save, "config.json"), "w") as fp:
-                json.dump(_config, fp)  # encode dict into JSON
-            _log.info("reloaded model from %s to %s", dir_of_start_model, dir_save)
-            return
-
         # todo: use Dataset.map() on Dataset.list_files()
         def get_dataset(dir_img, dir_lab):
             def gen():
@@ -772,25 +703,15 @@ def run(_config,
             model = load_model(dir_of_start_model, compile=False)
         else:
             index_start = 0
-            model = resnet50_classifier(n_classes,
-                                        input_height,
-                                        input_width,
-                                        weight_decay,
-                                        pretraining)
+            model = get_model(_config, _log)
+            if dir_of_start_model:
+                model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
+                _log.info("reloaded weights from %s", dir_of_start_model)
 
         model.compile(loss='categorical_crossentropy',
                       optimizer=Adam(learning_rate=0.001), # rs: why not learning_rate?
                       metrics=['accuracy', F1Score(average='macro', name='f1')])
 
-        if reload_weights:
-            model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
-            dir_save = os.path.join(dir_output, os.path.basename(os.path.normpath(dir_of_start_model)))
-            model.save(dir_save, include_optimizer=False)
-            with open(os.path.join(dir_save, "config.json"), "w") as fp:
-                json.dump(_config, fp)  # encode dict into JSON
-            _log.info("reloaded model from %s to %s", dir_of_start_model, dir_save)
-            return
-
         list_classes = list(classification_classes_name.values())
         data_args = dict(label_mode="categorical",
                          class_names=list_classes,
@@ -828,11 +749,10 @@ def run(_config,
             model = load_model(dir_of_start_model, compile=False)
         else:
             index_start = 0
-            model = machine_based_reading_order_model(n_classes,
-                                                      input_height,
-                                                      input_width,
-                                                      weight_decay,
-                                                      pretraining)
+            model = get_model(_config, _log)
+            if dir_of_start_model:
+                model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
+                _log.info("reloaded weights from %s", dir_of_start_model)
 
         #f1score_tot = [0]
         model.compile(loss="binary_crossentropy",
@@ -840,15 +760,6 @@ def run(_config,
                       optimizer=Adam(learning_rate=0.0001), # rs: why not learning_rate?
                       metrics=['accuracy'])
 
-        if reload_weights:
-            model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
-            dir_save = os.path.join(dir_output, os.path.basename(os.path.normpath(dir_of_start_model)))
-            model.save(dir_save, include_optimizer=False)
-            with open(os.path.join(dir_save, "config.json"), "w") as fp:
-                json.dump(_config, fp)  # encode dict into JSON
-            _log.info("reloaded model from %s to %s", dir_of_start_model, dir_save)
-            return
-
         dir_flow_train_imgs = os.path.join(dir_train, 'images')
         dir_flow_train_labels = os.path.join(dir_train, 'labels')
 
@@ -881,3 +792,23 @@ def run(_config,
             model_dir = os.path.join(dir_out,'model_best')
             model.save(model_dir)
         '''
+
+@click.command(context_settings=dict(
+        ignore_unknown_options=True,
+))
+@click.argument('SACRED_ARGS', nargs=-1, type=click.UNPROCESSED)
+def train_cli(sacred_args):
+    """
+    train model on extracted GT
+
+    SACRED_ARGS as per CLI interface of Sacred, cf.
+    https://sacred.readthedocs.io/en/stable/command_line.html:
+
+    \b
+    To configure the learning task, pass the string `with`,
+    followed by any number of
+    - config JSON file paths
+    - parameter overrides in the form of key=value
+    (where the later settings will override the former).
+    """
+    ex.run_commandline([sys.argv[0]] + list(sacred_args))
diff --git a/src/eynollah/training/weights_ensembling.py b/src/eynollah/training/weights_ensembling.py
index e3ede24..f651c56 100644
--- a/src/eynollah/training/weights_ensembling.py
+++ b/src/eynollah/training/weights_ensembling.py
@@ -43,6 +43,7 @@ def run_ensembling(model_dirs, out_dir):
 @click.option(
     "--in",
     "-i",
+    "in_",
     help="input directory of checkpoint models to be read",
     multiple=True,
     required=True,
diff --git a/src/eynollah/utils/contour.py b/src/eynollah/utils/contour.py
index f1a7a8e..eda60e9 100644
--- a/src/eynollah/utils/contour.py
+++ b/src/eynollah/utils/contour.py
@@ -11,7 +11,7 @@ from shapely.geometry.polygon import orient
 from shapely import set_precision, affinity
 from shapely.ops import unary_union, nearest_points
 
-from .rotate import rotate_image, rotation_image_new
+from .rotate import rotate_image
 
 def contours_in_same_horizon(cy_main_hor):
     """
@@ -120,94 +120,6 @@ def return_contours_of_interested_region(region_pre_p, label, min_area=0.0002, d
                                                          dilate=dilate)
     return contours_imgs
 
-def do_work_of_contours_in_image(contour, index_r_con, img, slope_first):
-    img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-    img_copy = cv2.fillPoly(img_copy, pts=[contour], color=1)
-
-    img_copy = rotation_image_new(img_copy, -slope_first)
-    _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-    cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-    cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-    cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-
-    return cont_int[0], index_r_con
-
-def get_textregion_contours_in_org_image_multi(cnts, img, slope_first, map=map):
-    if not len(cnts):
-        return [], []
-    results = map(partial(do_work_of_contours_in_image,
-                          img=img,
-                          slope_first=slope_first,
-                          ),
-                  cnts, range(len(cnts)))
-    return tuple(zip(*results))
-
-def get_textregion_contours_in_org_image(cnts, img, slope_first):
-    cnts_org = []
-    # print(cnts,'cnts')
-    for i in range(len(cnts)):
-        img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-        img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=1)
-
-        # plt.imshow(img_copy)
-        # plt.show()
-
-        # print(img.shape,'img')
-        img_copy = rotation_image_new(img_copy, -slope_first)
-        ##print(img_copy.shape,'img_copy')
-        # plt.imshow(img_copy)
-        # plt.show()
-
-        _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-        cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-        # print(np.shape(cont_int[0]))
-        cnts_org.append(cont_int[0])
-
-    return cnts_org
-
-def get_textregion_confidences_old(cnts, img, slope_first):
-    zoom = 3
-    img = cv2.resize(img, (img.shape[1] // zoom,
-                           img.shape[0] // zoom),
-                     interpolation=cv2.INTER_NEAREST)
-    cnts_org = []
-    for cnt in cnts:
-        img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-        img_copy = cv2.fillPoly(img_copy, pts=[cnt // zoom], color=1)
-
-        img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8)
-        _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-        cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-        cnts_org.append(cont_int[0] * zoom)
-
-    return cnts_org
-
-def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first, confidence_matrix):
-    img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-    img_copy = cv2.fillPoly(img_copy, pts=[contour_par], color=1)
-    confidence_matrix_mapped_with_contour = confidence_matrix * img_copy
-    confidence_contour = np.sum(confidence_matrix_mapped_with_contour) / float(np.sum(img_copy))
-
-    img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8)
-    _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-    cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    if len(cont_int)==0:
-        cont_int = [contour_par]
-        confidence_contour = 0
-    else:
-        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-    return cont_int[0], index_r_con, confidence_contour
-
 def get_region_confidences(cnts, confidence_matrix):
     if not len(cnts):
         return []
@@ -418,7 +330,7 @@ def estimate_skew_contours(contours):
     if not np.any(usable):
         raise ValueError("not enough contours with consistent length")
     if np.count_nonzero(usable) == 1:
-        return angle_in[usable]
+        return angle_in[usable][0]
     # 4. there is no way to distinguish between +90 and -89.9 here,
     # so map to [0,180] when calculating averages, then map back to [-90,90]
     # (we don't want -90 and +89 to average zero, or +1 and +179 to average 90)
diff --git a/src/eynollah/utils/rotate.py b/src/eynollah/utils/rotate.py
index 6651c4e..e45a438 100644
--- a/src/eynollah/utils/rotate.py
+++ b/src/eynollah/utils/rotate.py
@@ -2,10 +2,6 @@ import math
 import cv2
 
 
-def rotation_image_new(img, thetha):
-    rotated = rotate_image(img, thetha)
-    return rotate_max_area_new(img, rotated, thetha)
-
 def rotate_image(img_patch, slope):
     (h, w) = img_patch.shape[:2]
     center = (w // 2, h // 2)
diff --git a/src/eynollah/utils/utils_ocr.py b/src/eynollah/utils/utils_ocr.py
index 93d1137..817406c 100644
--- a/src/eynollah/utils/utils_ocr.py
+++ b/src/eynollah/utils/utils_ocr.py
@@ -1,9 +1,12 @@
 import math
 import copy
+from itertools import islice
 
 import numpy as np
 import cv2
-import tensorflow as tf
+# avoid module-level import:
+# import tensorflow as tf
+# (wait for tf-keras and logging setup in ModelZoo.load_model)
 from scipy.signal import find_peaks
 from scipy.ndimage import gaussian_filter1d
 from PIL import Image, ImageDraw, ImageFont
@@ -12,6 +15,8 @@ from .resize import resize_image
 
 
 def decode_batch_predictions(pred, num_to_char, max_len = 128):
+    import tensorflow as tf
+
     # input_len is the product of the batch size and the
     # number of time steps.
     input_len = np.ones(pred.shape[0]) * pred.shape[1]
@@ -39,6 +44,8 @@ def decode_batch_predictions(pred, num_to_char, max_len = 128):
     
     
 def distortion_free_resize(image, img_size):
+    import tensorflow as tf
+
     w, h = img_size
     image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)
 
@@ -502,3 +509,8 @@ def return_rnn_cnn_ocr_of_given_textlines(image,
             ocr_textline_in_textregion.append(text_textline)
         ocr_all_textlines.append(ocr_textline_in_textregion)
     return ocr_all_textlines
+
+def batched(iterable, n):
+    iterator = iter(iterable)
+    while batch := tuple(islice(iterator, n)):
+        yield batch
diff --git a/tests/cli_tests/conftest.py b/tests/cli_tests/conftest.py
index 601d76b..2e1501c 100644
--- a/tests/cli_tests/conftest.py
+++ b/tests/cli_tests/conftest.py
@@ -1,4 +1,5 @@
 from typing import List
+import os
 import pytest
 import logging
 
@@ -31,6 +32,8 @@ def run_eynollah_ok_and_check_logs(
                 subcommand,
                 *args
         ]
+        if 'EYNOLLAH_OPTIONS' in os.environ:
+            args = os.environ['EYNOLLAH_OPTIONS'].split() + args
         if pytestconfig.getoption('verbose') > 0:
             args = ['-l', 'DEBUG'] + args
         caplog.set_level(logging.INFO)
diff --git a/tests/cli_tests/test_layout.py b/tests/cli_tests/test_layout.py
index 7cbe013..503aeac 100644
--- a/tests/cli_tests/test_layout.py
+++ b/tests/cli_tests/test_layout.py
@@ -6,11 +6,12 @@ from ocrd_models.constants import NAMESPACES as NS
     "options",
     [
             [], # defaults
-            #["--allow_scaling", "--curved-line"],
-            ["--allow_scaling", "--curved-line", "--full-layout"],
-            ["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based"],
+            #["--curved-line"],
+            ["--curved-line", "--full-layout"],
+            ["--curved-line", "--full-layout", "--reading_order_machine_based"],
             # -ep ...
-            # -eoi ...
+            # --input_binary
+            # --ignore_page_extraction
             # --skip_layout_and_reading_order
     ], ids=str)
 def test_run_eynollah_layout_filename(
diff --git a/tests/cli_tests/test_ocr.py b/tests/cli_tests/test_ocr.py
index 6bf3080..cf34e06 100644
--- a/tests/cli_tests/test_ocr.py
+++ b/tests/cli_tests/test_ocr.py
@@ -30,7 +30,7 @@ def test_run_eynollah_ocr_filename(
             '-o', str(outfile.parent),
         ] + options,
         [
-            # FIXME: ocr has no logging!
+            'output filename:'
         ]
     )
     assert outfile.exists()
@@ -57,7 +57,7 @@ def test_run_eynollah_ocr_directory(
             '-o', str(outdir),
         ],
         [
-            # FIXME: ocr has no logging!
+            'output filename:'
         ]
     )
     assert len(list(outdir.iterdir())) == 2
diff --git a/tests/test_model_zoo.py b/tests/test_model_zoo.py
index 9d37431..341bc21 100644
--- a/tests/test_model_zoo.py
+++ b/tests/test_model_zoo.py
@@ -6,10 +6,10 @@ def test_trocr1(
     model_zoo = EynollahModelZoo(model_dir)
     try:
         from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-        model_zoo.load_models('trocr_processor')
+        model_zoo.load_models('trocr_processor',
+                              ('ocr', 'tr'))
         proc = model_zoo.get('trocr_processor')
         assert isinstance(proc, TrOCRProcessor)
-        model_zoo.load_models(['ocr', 'tr'])
         model = model_zoo.get('ocr')
         assert isinstance(model, VisionEncoderDecoderModel)
     except ImportError: