171 lines
4.9 KiB
Python
171 lines
4.9 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Investigation script for 3.pdf seal recognition issue.
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"""
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import sys
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from pathlib import Path
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from paddleocr import PaddleOCR
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def test_seal_recognition():
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"""Test OCR recognition on the unwarp seal image."""
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print("=" * 80)
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print("3.pdf 印章识别调查")
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print("=" * 80)
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# Path to the unwarp seal image
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seal_path = Path("test_reports_full/3.pdf/seal_unwarp_0.png")
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if not seal_path.exists():
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print(f"错误：印章图像不存在: {seal_path}")
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return False
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print(f"\n印章图像: {seal_path}")
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print(f"文件大小: {seal_path.stat().st_size} bytes")
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# Initialize PaddleOCR
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print("\n初始化 PaddleOCR...")
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ocr = PaddleOCR(use_angle_cls=True, lang='ch')
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# Run OCR on unwarp image
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print("\n识别解扭曲印章图像...")
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result = ocr.predict(str(seal_path))
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if result and len(result) > 0 and result[0]:
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print(f"\n识别到 {len(result[0])} 个文本块:")
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all_text = []
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for i, line in enumerate(result[0]):
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box = line[0]
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text_info = line[1]
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# text_info might be a string or a list
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if isinstance(text_info, list):
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text = text_info[0]
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confidence = text_info[1] if len(text_info) > 1 else 0.0
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else:
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text = str(text_info)
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confidence = 0.0
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print(f"\n文本块 {i+1}:")
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print(f" 文字: '{text}'")
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print(f" 置信度: {confidence:.4f}")
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print(f" 位置: {box}")
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all_text.append(text)
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combined_text = ''.join(all_text)
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print(f"\n合并后的文字: '{combined_text}'")
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print(f"文字长度: {len(combined_text)}")
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# Compare with what's expected
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expected = "深圳市中安质量检验认证有限公司"
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print(f"\n期望文字: '{expected}'")
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# Check if any part matches
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if "市场监督管理局" in combined_text:
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print("\n⚠️ 发现问题：识别结果包含'市场监督管理局'，但应该识别印章中的机构名称")
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if "检验认证" in combined_text or "检验" in combined_text:
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print("\n✓ 识别结果包含'检验'相关文字")
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return True
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else:
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print("未识别到任何文本")
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return False
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def test_crop_image():
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"""Test OCR on the original crop image."""
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print("\n" + "=" * 80)
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print("测试原始印章裁剪图像")
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print("=" * 80)
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crop_path = Path("test_reports_full/3.pdf/seal_crop_0.png")
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if not crop_path.exists():
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print(f"错误：裁剪图像不存在: {crop_path}")
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return False
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print(f"\n裁剪图像: {crop_path}")
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# Initialize PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True, lang='ch')
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# Run OCR
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print("识别裁剪印章图像...")
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result = ocr.predict(str(crop_path))
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if result and len(result) > 0 and result[0]:
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print(f"\n识别到 {len(result[0])} 个文本块:")
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all_text = []
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for i, line in enumerate(result[0]):
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text_info = line[1]
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# text_info might be a string or a list
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if isinstance(text_info, list):
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text = text_info[0]
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confidence = text_info[1] if len(text_info) > 1 else 0.0
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else:
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text = str(text_info)
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confidence = 0.0
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print(f" 文字 {i+1}: '{text}' (置信度: {confidence:.4f})")
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all_text.append(text)
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combined_text = ''.join(all_text)
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print(f"\n合并文字: '{combined_text}'")
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return True
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else:
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print("未识别到任何文本")
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return False
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def check_html_report():
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"""Check what the HTML report says."""
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print("\n" + "=" * 80)
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print("检查HTML报告")
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print("=" * 80)
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html_path = Path("test_reports_full/3.pdf/index.html")
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if not html_path.exists():
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print(f"错误：HTML报告不存在: {html_path}")
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return False
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# Read and parse HTML
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content = html_path.read_text(encoding='utf-8')
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# Look for institution info
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import re
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# Find extracted institution
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extracted_match = re.search(r'Extracted Institution.*?<div class="value">(.*?)</div>', content, re.DOTALL)
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if extracted_match:
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extracted = extracted_match.group(1).strip()
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print(f"\n报告中的提取结果:\n '{extracted}'")
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# Find seal recognized text
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seal_match = re.search(r'Recognized Text:</strong>(.*?)</p>', content, re.DOTALL)
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if seal_match:
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seal_text = seal_match.group(1).strip()
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print(f"\n报告中的印章识别文字:\n '{seal_text}'")
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return True
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if __name__ == "__main__":
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print("\n开始调查3.pdf印章识别问题...\n")
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# Test all three
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test_seal_recognition()
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test_crop_image()
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check_html_report()
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print("\n" + "=" * 80)
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print("调查完成")
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print("=" * 80)
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