OFD file intelligent abstract generation method fusing OCR and deep learning

By integrating OCR and deep learning, the problem of information extraction and structured presentation in complex electronic documents was solved. It achieved efficient recognition of blurred areas and handwritten characters and accurate division of document regions, generating logically clear structured summaries and improving the accuracy and practicality of document processing.

CN122196170BActive Publication Date: 2026-07-21HUNAN YUNDANG INFORMATION TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN YUNDANG INFORMATION TECH CO LTD
Filing Date
2026-05-14
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately analyze the layout structure and extract logically clear and complete key information when processing electronic documents with complex layouts and poor image quality, resulting in incomplete information and affecting subsequent application effects.

Method used

This method integrates OCR and deep learning, using image recognition technology to scan text and tables, identify blurred areas and perform image enhancement processing, extract key elements of seals and signatures, use layout analysis technology to divide document areas, combine computer vision models to integrate information, apply machine learning models to analyze semantic relationships, generate a set of structured key information and output a concise summary.

Benefits of technology

It achieves efficient information extraction and structured presentation of complex document layouts, improving the accuracy and practicality of document processing. The accuracy rate of fuzzy area recognition is ≥92%, handwriting recognition is ≥88%, region segmentation is ≥93%, key information integrity is ≥94%, and summary accuracy is ≥88%.

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Abstract

The application discloses an OFD file intelligent abstract generation method fusing OCR and deep learning, relates to the technical field of electronic file processing and management, and preliminarily scans text and tables through image recognition technology to identify fuzzy areas; for the areas containing handwriting, image enhancement is combined with semantic analysis to restore the clarity; then, seal and signature key elements are extracted, layout analysis is used to divide the document area and judge the relevance; if the preset condition is met, the information is integrated through a computer vision model to determine the overall logical structure; then, a machine learning model is applied to analyze the semantic relationship to form a structured information set; finally, a concise abstract containing the writing date and the year is generated by fusing the visual model. The application ensures efficient extraction and structured presentation of complex document information, and significantly improves the accuracy and practicability of document processing.
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