Update app.py

app.py

@@ -13,6 +13,7 @@ import pytesseract
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
 import torch
+import re
 
 from glob import glob
 from skimage import io
@@ -180,7 +181,7 @@ def main():
         number_plate_img, coords = yolo_predictions(image, net)
         
         # Display the detected number plate image
-        st.image(number_plate_img, caption="Detected Number Plate", use_column_width=True)
+        # st.image(number_plate_img, caption="Detected Number Plate", use_column_width=True)
 
         for box in coords:
             x, y, width, height = box
@@ -189,7 +190,7 @@ def main():
         cropped_image = crop_image(np.array(number_plate_img), x, y, width, height)
         
         # Display the coordinates
-        st.write("Detected Number Plate Coordinates:", coords)
+        # st.write("Detected Number Plate Coordinates:", coords)
 
         # Display the cropped image
         st.image(cropped_image, caption="Detected Number Plate", use_column_width=True)
@@ -198,7 +199,18 @@ def main():
         cropped_image = np.array(cropped_image)  # Replace ... with your actual image array
         extracted_text = tr_ocr_image_from_array(cropped_image)
         
-        st.write("Extracted text: ")
+        #Remove speical characters from the extracted text
+        def remove_special_characters(text):
+            pattern = r'[^a-zA-Z0-9\s]' # This pattern matches any character that is not alphanumeric or whitespace
+
+            # Use re.sub() to replace all occurrences of the pattern with an empty string
+            cleaned_text = re.sub(pattern, '', text)
+
+            return cleaned_text
+        
+        extracted_text = remove_special_characters(extracted_text)
+        
+        st.markdown("<span style='color:red; font-weight:bold'>Extracted text:</span>", unsafe_allow_html=True)
         st.text(extracted_text)