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app.py

@@ -11,6 +11,12 @@ from transformers import (
 )
 import numpy as np
 import torch
+from collections import Counter
+
+def Most_Common(lst):
+    data = Counter(lst)
+    return data.most_common(1)[0][0]
+
 
 from IndicTransToolkit import IndicProcessor
 
@@ -107,6 +113,7 @@ def process_image(image):
     #         # Recognize text in the cropped area
     #         recognized_text = ocr.recognise(cropped_path, script_lang)
     #         recognized_texts.append(recognized_text)
+    langs = []
     for id, bbox in enumerate(detections):
         # Identify the script and crop the image to this region
         script_lang, cropped_path = ocr.crop_and_identify_script(pil_image, bbox)
@@ -120,13 +127,14 @@ def process_image(image):
         if script_lang:
             recognized_text = ocr.recognise(cropped_path, script_lang)
             recognized_texts[f"img_{id}"] = {"txt": recognized_text, "bbox": [x1, y1, x2, y2]}
+            langs.append(script_lang)
 
     # Combine recognized texts into a single string for display
     # recognized_texts_combined = " ".join(recognized_texts)
     string = detect_para(recognized_texts)
 
     recognized_texts_combined = '\n'.join([' '.join(line) for line in string])
-    recognized_texts_combined = translate(recognized_texts_combined,script_lang)
+    recognized_texts_combined = translate(recognized_texts_combined,Most_Common(langs))
 
     return output_image, recognized_texts_combined