diff --git a/src/eynollah/eynollah_ocr.py b/src/eynollah/eynollah_ocr.py
index f1b155b..faeb042 100644
--- a/src/eynollah/eynollah_ocr.py
+++ b/src/eynollah/eynollah_ocr.py
@@ -90,12 +90,14 @@ class Eynollah_ocr(Eynollah):
         page_ns,
         tr_ocr_input_height_and_width,
     ) -> EynollahOcrResult:
+        import torch
         
         total_bb_coordinates = []
         cropped_lines = []
         cropped_lines_region_indexer = []
         cropped_lines_meging_indexing = []
         extracted_texts = []
+        extracted_confs = []
 
         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)):
@@ -142,15 +144,20 @@ class Eynollah_ocr(Eynollah):
         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_merged = self.model_zoo.get('trocr_processor')(
+            pixel_values = 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,
+            output = self.model_zoo.get('ocr').generate(
+                pixel_values.to(self.device),
+                output_scores=True,
+                return_dict_in_generate=True)
+            conf = torch.max(torch.softmax(torch.cat(
+                output.scores, dim=0), dim=1), dim=1).values.tolist()
+            text = self.model_zoo.get('trocr_processor').batch_decode(
+                output.sequences,
                 skip_special_tokens=True,
                 clean_up_tokenization_spaces=False)
-            extracted_texts = extracted_texts + generated_text_merged
+            extracted_confs.extend(conf)
+            extracted_texts.extend(text)
         del cropped_lines
         gc.collect()
 
@@ -159,10 +166,15 @@ class Eynollah_ocr(Eynollah):
                                   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,
         )
@@ -618,6 +630,7 @@ class Eynollah_ocr(Eynollah):
             
             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