一种PDF合同文件的识别方法、系统、介质及程序产品
By combining deep learning and natural language understanding models, the problem of identifying key fields in PDF contract documents with complex layouts has been solved, achieving efficient and accurate automated processing and electronic signature authentication, thus improving the processing efficiency and recognition accuracy of contract documents.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING QIANRUNHE TECH CO LTD
- Filing Date
- 2025-09-17
- Publication Date
- 2026-07-17
AI Technical Summary
Existing PDF contract document information extraction technologies struggle to accurately identify key fields when faced with complex formats, leading to identification errors and requiring manual verification. This severely restricts contract processing efficiency and increases operating costs.
Deep learning models are used for image parsing and structured data generation. Adaptive semantic analysis and natural language understanding models are combined for multi-dimensional feature matching and entity relationship verification. Dependency parsing and semantic role labeling are used to correct extraction errors and generate a list of contract elements that meet electronic signature authentication requirements.
It achieves high-precision automated processing of PDF contract documents, improves the accuracy of key field identification, reduces manual intervention, and enhances system adaptability and contract data flow efficiency.
Smart Images
Figure CN121189323B_ABST