Update app.py

app.py

@@ -48,42 +48,6 @@ import pytesseract
 
 #pytesseract.pytesseract.tesseract_cmd = r"./Tesseract-OCR/tesseract.exe"
 from PIL import Image
-def mark_region(im):
-    
-    #im = cv2.imread(image_path)
-
-    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
-    blur = cv2.GaussianBlur(gray, (9,9), 0)
-    thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,30)
-
-    # Dilate to combine adjacent text contours
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
-    dilate = cv2.dilate(thresh, kernel, iterations=4)
-
-    # Find contours, highlight text areas, and extract ROIs
-    cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
-
-    line_items_coordinates = []
-    for c in cnts:
-        area = cv2.contourArea(c)
-        x,y,w,h = cv2.boundingRect(c)
-
-        if y >= 600 and x <= 1000:
-            if area > 10000:
-                image = cv2.rectangle(im, (x,y), (2200, y+h), color=(255,0,255), thickness=3)
-                line_items_coordinates.append([(x,y), (2200, y+h)])
-
-        if y >= 2400 and x<= 2000:
-            image = cv2.rectangle(im, (x,y), (2200, y+h), color=(255,0,255), thickness=3)
-            line_items_coordinates.append([(x,y), (2200, y+h)])
-
-
-    return image, line_items_coordinates
-
-
-
-
 @st.experimental_singleton
 def read_pdf(file):
     images=pdf2image.convert_from_path(file)