Table Extraction Implementation Solution
Kreuzberg uses a layered processing strategy to cope with different types of PDF forms:
- Native Spreadsheets: Directly parse structured data built into PDF
- Scanned Forms: Combined with OCR technology to recognize text and layout information
Specific methods of operation
Standard extraction process code example:
from kreuzberg import Kreuzberg
extractor = Kreuzberg()
# 基本文本提取
text_data = extractor.extract_text('table.pdf')
# 高级表格模式
tables = extractor.extract_tables('table.pdf', mode='structured')
Parameter Tuning Tips
An important parameter for improving the accuracy of form recognition:
- layout_analysis: Set to True to enable layout analysis algorithm
- ocr_lang: Specify the correct documentation language code (e.g., 'chi_sim').
- table_detection_sensitivity: Adjustment of table detection thresholds
Recommendations for reprocessing
Recommendations for improving data availability:
- Data cleansing and reorganization using pandas
- Manual verification of recognition results
- Consider adding table header auto-detection
This answer comes from the articleKreuzberg: open source tool to extract text from any documentThe
May not be reproduced without permission:AI productivity tools " How to use Kreuzberg to extract table data from PDF?
