from flask import Flask, request, jsonify from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image import cv2 import numpy as np # Initialize Flask app app = Flask(__name__) # Load model and processor processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") def detect_lines(image, min_height=20, min_width=100): # Convert PIL image to NumPy array image_np = np.array(image) gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) _, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) dilated = cv2.dilate(binary, kernel, iterations=1) contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) bounding_boxes = sorted([cv2.boundingRect(c) for c in contours], key=lambda b: b[1]) line_images = [image_np[y:y+h, x:x+w] for x, y, w, h in bounding_boxes if h >= min_height and w >= min_width] return line_images @app.route("/process_image", methods=["POST"]) def process_image(): if 'image' not in request.files: return jsonify({"error": "No image file provided"}), 400 try: image_file = request.files['image'] image = Image.open(image_file).convert("RGB") line_images = detect_lines(image) extracted_text = "" for idx, line_img in enumerate(line_images): line_pil = Image.fromarray(line_img) pixel_values = processor(images=line_pil, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] extracted_text += f"Line {idx + 1}: {generated_text}\n" return jsonify({"extracted_text": extracted_text}), 200 except Exception as e: return jsonify({"error": str(e)}), 500 if __name__ == "__main__": app.run(host="0.0.0.0", port=7860)