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README.md
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@ -1,27 +1,25 @@
# Textline-Recognition
# Textline Detection
***
# Tool
This tool does textline detection of image and throw result as xml data.
## Introduction
This tool performs textline detection from document image data and returns the results as PAGE-XML.
# Models
In order to run this tool you need corresponding models. You can find them here:
## Installation
`pip install .`
## Models
In order to run this tool you also need trained models. You can download our pre-trained models from here:
https://file.spk-berlin.de:8443/textline_detection/
# Installation
sudo pip install .
# Usage
sbb_textline_detector -i 'image file name' -o 'directory to write output xml' -m 'directory of models'
## Usage
`sbb_textline_detector -i <image file name> -o <directory to write output xml> -m <directory of models>`
## Usage with OCR-D
~~~
ocrd-example-binarize -I OCR-D-IMG -O OCR-D-IMG-BIN
ocrd_sbb_textline_detector -I OCR-D-IMG-BIN -O OCR-D-SEG-LINE-SBB \
ocrd-sbb-textline-detector -I OCR-D-IMG-BIN -O OCR-D-SEG-LINE-SBB \
-p '{ "model": "/path/to/the/models/textline_detection" }'
~~~

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qurator/sbb_textline_detector/main.py
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@ -34,11 +34,11 @@ with warnings.catch_warnings():
__doc__ = \
"""
tool to extract table form data from alto xml data
tool to extract text lines from document images
"""
class textlineerkenner:
class textline_detector:
def __init__(self, image_dir, dir_out, f_name, dir_models):
self.image_dir = image_dir # XXX This does not seem to be a directory as the name suggests, but a file
self.dir_out = dir_out
@ -70,7 +70,7 @@ class textlineerkenner:
np.array([point for point in polygon.exterior.coords], dtype=np.uint))
return found_polygons_early
def filter_contours_area_of_image(self, image, contours, hirarchy, max_area, min_area):
def filter_contours_area_of_image(self, image, contours, hierarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
@ -81,13 +81,13 @@ class textlineerkenner:
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(
image.shape[:2]) and hirarchy[0][jv][3] == -1 : # and hirarchy[0][jv][3]==-1 :
image.shape[:2]) and hierarchy[0][jv][3] == -1 : # and hierarchy[0][jv][3]==-1 :
found_polygons_early.append(
np.array([ [point] for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_interiors(self, image, contours, hirarchy, max_area, min_area):
def filter_contours_area_of_image_interiors(self, image, contours, hierarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
@ -98,7 +98,7 @@ class textlineerkenner:
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and \
hirarchy[0][jv][3] != -1:
hierarchy[0][jv][3] != -1:
# print(c[0][0][1])
found_polygons_early.append(
np.array([point for point in polygon.exterior.coords], dtype=np.uint))
@ -486,9 +486,9 @@ class textlineerkenner:
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
main_contours = self.filter_contours_area_of_image(thresh, contours, hierarchy, max_area=1, min_area=0.00001)
self.boxes = []
for jj in range(len(main_contours)):
@ -916,8 +916,8 @@ class textlineerkenner:
image_box_tabels=image_box_tabels.astype(np.uint8)
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
return contours,hierachy
contours,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
return contours,hierarchy
def find_contours_mean_y_diff(self,contours_main):
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
@ -1236,9 +1236,9 @@ class textlineerkenner:
# create the file structure
data = ET.Element('PcGts')
data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15")
data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15")
@ -1378,7 +1378,7 @@ class textlineerkenner:
def run(self):
#get image and sclaes, then extract the page of scanned image
#get image and scales, then extract the page of scanned image
t1=time.time()
self.get_image_and_scales()
image_page,page_coord=self.extract_page()
@ -1475,7 +1475,7 @@ class textlineerkenner:
def main(image, out, model):
possibles = globals() # XXX unused?
possibles.update(locals())
x = textlineerkenner(image, out, None, model)
x = textline_detector(image, out, None, model)
x.run()

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qurator/sbb_textline_detector/ocrd-tool.json
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@ -1,8 +1,8 @@
{
"version": "0.0.1",
"tools": {
"ocrd_sbb_textline_detector": {
"executable": "ocrd_sbb_textline_detector",
"ocrd-sbb-textline-detector": {
"executable": "ocrd-sbb-textline-detector",
"description": "Detect lines",
"steps": ["layout/segmentation/line"],
"input_file_grp": [

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qurator/sbb_textline_detector/ocrd_cli.py
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@ -12,7 +12,7 @@ from ocrd_models.ocrd_page_generateds import MetadataItemType, LabelsType, Label
from ocrd_utils import concat_padded, getLogger, MIMETYPE_PAGE
from pkg_resources import resource_string
from qurator.sbb_textline_detector import textlineerkenner
from qurator.sbb_textline_detector import textline_detector
log = getLogger('processor.OcrdSbbTextlineDetectorRecognize')
@ -67,7 +67,7 @@ class OcrdSbbTextlineDetectorRecognize(Processor):
# Segment the image
image_file = self._resolve_image_file(input_file)
model = self.parameter['model']
x = textlineerkenner(image_file, tmp_dirname, file_id, model)
x = textline_detector(image_file, tmp_dirname, file_id, model)
x.run()
# Read segmentation results

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requirements.txt
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@ -1,5 +1,4 @@
opencv-python
numpy
opencv-python-headless
matplotlib
seaborn
tqdm
@ -8,5 +7,4 @@ shapely
scikit-learn
tensorflow-gpu < 2.0
scipy
click
ocrd >= 2.0.0

2
setup.py
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@ -24,7 +24,7 @@ setup(
entry_points={
'console_scripts': [
"sbb_textline_detector=qurator.sbb_textline_detector:main",
"ocrd_sbb_textline_detector=qurator.sbb_textline_detector:ocrd_sbb_textline_detector",
"ocrd-sbb-textline-detector=qurator.sbb_textline_detector:ocrd_sbb_textline_detector",
]
},
python_requires='>=3.6.0',