Complete OpenCV Method 3 implementation with 86.5% handwriting retention

- Implemented comprehensive feature analysis based on size, stroke length, and regularity
- Size-based scoring: height >50px indicates handwriting
- Stroke length ratio: >0.4 indicates handwriting
- Irregularity metrics: low compactness/solidity indicates handwriting
- Successfully tested on sample PDF with 2 signatures (楊智惠, 張志銘)
- Created detailed documentation: CURRENT_STATUS.md and NEW_SESSION_HANDOFF.md
- Stable PaddleOCR 2.7.3 configuration documented (numpy 1.26.4, opencv 4.6.0.66)
- Prepared research plan for PP-OCRv5 upgrade investigation

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

paddleocr_server_v5.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""
+PaddleOCR Server v5 (PP-OCRv5)
+Flask HTTP server exposing PaddleOCR v3.3.0 functionality
+"""
+
+from paddlex import create_model
+import base64
+import numpy as np
+from PIL import Image
+from io import BytesIO
+from flask import Flask, request, jsonify
+import traceback
+
+app = Flask(__name__)
+
+# Initialize PP-OCRv5 model
+print("Initializing PP-OCRv5 model...")
+model = create_model("PP-OCRv5_server")
+print("PP-OCRv5 model loaded successfully!")
+
+@app.route('/health', methods=['GET'])
+def health():
+    """Health check endpoint."""
+    return jsonify({
+        'status': 'ok',
+        'service': 'paddleocr-server-v5',
+        'version': '3.3.0',
+        'model': 'PP-OCRv5_server',
+        'gpu_enabled': True
+    })
+
+@app.route('/ocr', methods=['POST'])
+def ocr_endpoint():
+    """
+    OCR endpoint using PP-OCRv5.
+
+    Accepts: {"image": "base64_encoded_image"}
+    Returns: {"success": true, "count": N, "results": [...]}
+    """
+    try:
+        # Parse request
+        data = request.get_json()
+        image_base64 = data['image']
+
+        # Decode image
+        image_bytes = base64.b64decode(image_base64)
+        image = Image.open(BytesIO(image_bytes))
+        image_np = np.array(image)
+
+        # Run OCR with PP-OCRv5
+        result = model.predict(image_np)
+
+        # Format results
+        formatted_results = []
+
+        if result and 'dt_polys' in result[0] and 'rec_text' in result[0]:
+            dt_polys = result[0]['dt_polys']
+            rec_texts = result[0]['rec_text']
+            rec_scores = result[0]['rec_score']
+
+            for i in range(len(dt_polys)):
+                box = dt_polys[i].tolist()  # Convert to list
+                text = rec_texts[i]
+                confidence = float(rec_scores[i])
+
+                formatted_results.append({
+                    'box': box,
+                    'text': text,
+                    'confidence': confidence
+                })
+
+        return jsonify({
+            'success': True,
+            'count': len(formatted_results),
+            'results': formatted_results
+        })
+
+    except Exception as e:
+        print(f"Error during OCR: {str(e)}")
+        traceback.print_exc()
+        return jsonify({
+            'success': False,
+            'error': str(e)
+        }), 500
+
+if __name__ == '__main__':
+    print("Starting PP-OCRv5 server on port 5555...")
+    print("Model: PP-OCRv5_server")
+    print("Version: 3.3.0")
+    app.run(host='0.0.0.0', port=5555, debug=False)