Create app.py

app.py

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+from fastapi import FastAPI, File, UploadFile
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import cv2
+import numpy as np
+import io
+
+app = FastAPI()
+
+# Load the model and processor
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+
+# 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
+
+@app.post("/process_image/")
+async def process_image(file: UploadFile = File(...)):
+    try:
+        # Read image bytes and convert to PIL image
+        image = Image.open(io.BytesIO(await file.read())).convert("RGB")
+
+        # Detect lines in the image
+        line_images = detect_lines(image, min_height=30, min_width=100)
+
+        # 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 with a newline
+            extracted_text += f"{generated_text}\n"
+
+        # Return extracted text as JSON response
+        return {"extracted_text": extracted_text.strip()}  # Remove trailing newline
+
+    except Exception as e:
+        return {"error": str(e)}