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>
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test_opencv_advanced.py
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test_opencv_advanced.py
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#!/usr/bin/env python3
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"""
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Advanced OpenCV separation based on key observations:
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1. 手写字比印刷字大 (Handwriting is LARGER)
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2. 手写笔画长度更长 (Handwriting strokes are LONGER)
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3. 印刷标楷体规律，手写潦草 (Printed is regular, handwriting is messy)
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"""
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import cv2
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import numpy as np
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from pathlib import Path
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from scipy import ndimage
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# Test image
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TEST_IMAGE = "/Volumes/NV2/PDF-Processing/signature-image-output/paddleocr_improved/signature_02_original.png"
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OUTPUT_DIR = "/Volumes/NV2/PDF-Processing/signature-image-output/opencv_advanced_test"
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print("="*80)
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print("Advanced OpenCV Separation - Size + Stroke Length + Regularity")
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print("="*80)
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Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)
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# Load and preprocess
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image = cv2.imread(TEST_IMAGE)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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_, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
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print(f"\nImage: {image.shape[1]}x{image.shape[0]}")
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# Save binary
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cv2.imwrite(str(Path(OUTPUT_DIR) / "00_binary.png"), binary)
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print("\n" + "="*80)
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print("METHOD 3: Comprehensive Feature Analysis")
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print("="*80)
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# Find connected components
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary, connectivity=8)
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print(f"\nFound {num_labels - 1} connected components")
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print("\nAnalyzing each component...")
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# Store analysis for each component
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components_analysis = []
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for i in range(1, num_labels):
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x, y, w, h, area = stats[i]
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# Extract component mask
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component_mask = (labels == i).astype(np.uint8) * 255
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# ============================================
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# FEATURE 1: Size (手写字比印刷字大)
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# ============================================
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bbox_area = w * h
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font_height = h # Character height is a good indicator
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# ============================================
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# FEATURE 2: Stroke Length (笔画长度)
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# ============================================
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# Skeletonize to get the actual stroke centerline
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from skimage.morphology import skeletonize
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skeleton = skeletonize(component_mask // 255)
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stroke_length = np.sum(skeleton) # Total length of strokes
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# Stroke length ratio (length relative to area)
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stroke_length_ratio = stroke_length / area if area > 0 else 0
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# ============================================
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# FEATURE 3: Regularity vs Messiness
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# ============================================
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# 3a. Compactness (regular shapes are more compact)
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contours, _ = cv2.findContours(component_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if contours:
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perimeter = cv2.arcLength(contours[0], True)
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compactness = (4 * np.pi * area) / (perimeter * perimeter) if perimeter > 0 else 0
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else:
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compactness = 0
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# 3b. Solidity (ratio of area to convex hull area)
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if contours:
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hull = cv2.convexHull(contours[0])
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hull_area = cv2.contourArea(hull)
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solidity = area / hull_area if hull_area > 0 else 0
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else:
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solidity = 0
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# 3c. Extent (ratio of area to bounding box area)
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extent = area / bbox_area if bbox_area > 0 else 0
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# 3d. Edge roughness (measure irregularity)
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# More irregular edges = more "messy" = likely handwriting
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edges = cv2.Canny(component_mask, 50, 150)
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edge_pixels = np.sum(edges > 0)
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edge_roughness = edge_pixels / perimeter if perimeter > 0 else 0
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# ============================================
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# CLASSIFICATION LOGIC
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# ============================================
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# Large characters are likely handwriting
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is_large = font_height > 40 # Threshold for "large" characters
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# Long strokes relative to area indicate handwriting
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is_long_stroke = stroke_length_ratio > 0.4 # Handwriting has higher ratio
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# Regular shapes (high compactness, high solidity) = printed
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# Irregular shapes (low compactness, low solidity) = handwriting
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is_irregular = compactness < 0.3 or solidity < 0.7 or extent < 0.5
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# DECISION RULES
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handwriting_score = 0
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# Size-based scoring (重要!)
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if font_height > 50:
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handwriting_score += 3 # Very large = likely handwriting
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elif font_height > 35:
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handwriting_score += 2 # Medium-large = possibly handwriting
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elif font_height < 25:
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handwriting_score -= 2 # Small = likely printed
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# Stroke length scoring
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if stroke_length_ratio > 0.5:
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handwriting_score += 2 # Long strokes
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elif stroke_length_ratio > 0.35:
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handwriting_score += 1
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# Regularity scoring (标楷体 is regular, 手写 is messy)
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if is_irregular:
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handwriting_score += 1 # Irregular = handwriting
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else:
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handwriting_score -= 1 # Regular = printed
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# Area scoring
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if area > 2000:
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handwriting_score += 2 # Large area = handwriting
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elif area < 500:
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handwriting_score -= 1 # Small area = printed
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# Final classification
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is_handwriting = handwriting_score > 0
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components_analysis.append({
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'id': i,
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'box': (x, y, w, h),
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'area': area,
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'height': font_height,
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'stroke_length': stroke_length,
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'stroke_ratio': stroke_length_ratio,
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'compactness': compactness,
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'solidity': solidity,
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'extent': extent,
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'edge_roughness': edge_roughness,
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'handwriting_score': handwriting_score,
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'is_handwriting': is_handwriting,
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'mask': component_mask
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})
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# Sort by area (largest first)
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components_analysis.sort(key=lambda c: c['area'], reverse=True)
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# Print analysis
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print("\n" + "-"*80)
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print("Top 10 Components Analysis:")
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print("-"*80)
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print(f"{'ID':<4} {'Area':<6} {'H':<4} {'StrokeLen':<9} {'StrokeR':<7} {'Compact':<7} "
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f"{'Solid':<6} {'Score':<5} {'Type':<12}")
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print("-"*80)
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for i, comp in enumerate(components_analysis[:10]):
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comp_type = "✅ Handwriting" if comp['is_handwriting'] else "❌ Printed"
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print(f"{comp['id']:<4} {comp['area']:<6} {comp['height']:<4} "
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f"{comp['stroke_length']:<9.0f} {comp['stroke_ratio']:<7.3f} "
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f"{comp['compactness']:<7.3f} {comp['solidity']:<6.3f} "
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f"{comp['handwriting_score']:>+5} {comp_type:<12}")
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# Create masks
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handwriting_mask = np.zeros_like(binary)
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printed_mask = np.zeros_like(binary)
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for comp in components_analysis:
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if comp['is_handwriting']:
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handwriting_mask = cv2.bitwise_or(handwriting_mask, comp['mask'])
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else:
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printed_mask = cv2.bitwise_or(printed_mask, comp['mask'])
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# Statistics
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hw_count = sum(1 for c in components_analysis if c['is_handwriting'])
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pr_count = sum(1 for c in components_analysis if not c['is_handwriting'])
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print("\n" + "="*80)
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print("Classification Results:")
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print("="*80)
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print(f" Handwriting components: {hw_count}")
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print(f" Printed components: {pr_count}")
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print(f" Total: {len(components_analysis)}")
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# Apply to original image
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result_handwriting = cv2.bitwise_and(image, image, mask=handwriting_mask)
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result_printed = cv2.bitwise_and(image, image, mask=printed_mask)
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# Save results
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cv2.imwrite(str(Path(OUTPUT_DIR) / "method3_handwriting_mask.png"), handwriting_mask)
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cv2.imwrite(str(Path(OUTPUT_DIR) / "method3_printed_mask.png"), printed_mask)
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cv2.imwrite(str(Path(OUTPUT_DIR) / "method3_handwriting_result.png"), result_handwriting)
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cv2.imwrite(str(Path(OUTPUT_DIR) / "method3_printed_result.png"), result_printed)
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# Create visualization
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vis_overlay = image.copy()
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vis_overlay[handwriting_mask > 0] = [0, 255, 0] # Green for handwriting
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vis_overlay[printed_mask > 0] = [0, 0, 255] # Red for printed
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vis_final = cv2.addWeighted(image, 0.6, vis_overlay, 0.4, 0)
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# Add labels to visualization
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for comp in components_analysis[:15]: # Label top 15
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x, y, w, h = comp['box']
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cx, cy = x + w//2, y + h//2
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color = (0, 255, 0) if comp['is_handwriting'] else (0, 0, 255)
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label = f"H{comp['handwriting_score']:+d}" if comp['is_handwriting'] else f"P{comp['handwriting_score']:+d}"
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cv2.putText(vis_final, label, (cx-15, cy), cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
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cv2.imwrite(str(Path(OUTPUT_DIR) / "method3_visualization.png"), vis_final)
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print("\n📁 Saved results:")
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print(" - method3_handwriting_mask.png")
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print(" - method3_printed_mask.png")
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print(" - method3_handwriting_result.png")
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print(" - method3_printed_result.png")
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print(" - method3_visualization.png")
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# Calculate content pixels
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hw_pixels = np.count_nonzero(handwriting_mask)
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pr_pixels = np.count_nonzero(printed_mask)
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total_pixels = np.count_nonzero(binary)
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print("\n" + "="*80)
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print("Pixel Distribution:")
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print("="*80)
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print(f" Total foreground: {total_pixels:6d} pixels (100.0%)")
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print(f" Handwriting: {hw_pixels:6d} pixels ({hw_pixels/total_pixels*100:5.1f}%)")
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print(f" Printed: {pr_pixels:6d} pixels ({pr_pixels/total_pixels*100:5.1f}%)")
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print("\n" + "="*80)
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print("Test completed!")
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print(f"Results: {OUTPUT_DIR}")
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print("="*80)
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print("\n📊 Feature Analysis Summary:")
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print(" ✅ Size-based classification: Large characters → Handwriting")
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print(" ✅ Stroke length analysis: Long stroke ratio → Handwriting")
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print(" ✅ Regularity analysis: Irregular shapes → Handwriting")
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print("\nNext: Review visualization to tune thresholds if needed")
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