Update app.py

app.py

@@ -1,6 +1,8 @@
 import streamlit as st
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
+import cv2
+import numpy as np
 
 # Load the model and processor
 @st.cache_resource
@@ -11,8 +13,44 @@ def load_model():
 
 processor, model = load_model()
 
+# Helper function to preprocess the image and detect lines
+def detect_lines(image, min_height=20, min_width=100):
+    # Convert the PIL image to a NumPy array
+    image_np = np.array(image)
+
+    # Convert to grayscale
+    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+
+    # Apply binary thresholding
+    _, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+    # Dilate to merge nearby text
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
+    dilated = cv2.dilate(binary, kernel, iterations=1)
+
+    # Find contours
+    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Sort contours top-to-bottom
+    bounding_boxes = [cv2.boundingRect(c) for c in contours]
+    bounding_boxes = sorted(bounding_boxes, key=lambda b: b[1])  # Sort by y-coordinate
+
+    # Filter out small contours and merge nearby ones
+    filtered_boxes = []
+    for x, y, w, h in bounding_boxes:
+        if h >= min_height and w >= min_width:  # Filter small boxes
+            filtered_boxes.append((x, y, w, h))
+
+    # Extract individual lines as images
+    line_images = []
+    for (x, y, w, h) in filtered_boxes:
+        line = image_np[y:y+h, x:x+w]
+        line_images.append(line)
+
+    return line_images
+
 # Streamlit app
-st.title("OCR API Service")
+st.title("OCR API Service with Multiline Support")
 
 uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
 
@@ -22,19 +60,37 @@ if uploaded_file is not None:
         image = Image.open(uploaded_file).convert("RGB")
         st.image(image, caption="Uploaded Image", use_column_width=True)
 
-        # Perform OCR
-        pixel_values = processor(images=image, return_tensors="pt").pixel_values
-        generated_ids = model.generate(pixel_values)
-        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        # Detect lines in the image
+        st.write("Detecting lines...")
+        line_images = detect_lines(image, min_height=30, min_width=100)
+        st.write(f"Detected {len(line_images)} lines in the image.")
+
+        # Perform OCR on each detected line
+        extracted_text = ""
+        for idx, line_img in enumerate(line_images):
+            # Convert the line image to PIL format
+            line_pil = Image.fromarray(line_img)
+
+            # Prepare the image for OCR
+            pixel_values = processor(images=line_pil, return_tensors="pt").pixel_values
+
+            # Generate text from the line image
+            generated_ids = model.generate(pixel_values)
+            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+            # Append the extracted text
+            extracted_text += f"{generated_text}\n"
 
         # Display extracted text
         st.subheader("Extracted Text:")
-        st.text(generated_text)
+        st.text_area("Output Text", extracted_text, height=300)
 
         # Simulate API-like JSON response
-        json_response = {"extracted_text": generated_text}
+        json_response = {"extracted_text": extracted_text}
         st.write("API Response:")
         st.json(json_response)
 
     except Exception as e:
         st.error(f"An error occurred: {e}")
+else:
+    st.info("Please upload an image to start the OCR process.")