Self-adaptive text extraction method and system based on artificial intelligence

By using document structure entropy analysis and a multi-level confidence verification mechanism, and adaptively configuring preprocessing and recognition parameters, the problem of insufficient recognition accuracy and adaptability caused by differences in document quality is solved, and efficient text extraction is achieved.

CN121658657APending Publication Date: 2026-03-13BEIJING VOCATIONAL COLLEGE OF ECONOMICS & MANAGEMENT (BEIJING MANAGER COLLEGE)
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Patent Information

Application Number
CN202511838076.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-08
Publication Date
2026-03-13

AI Technical Summary

Technical Problem

Existing text extraction technologies cannot adapt to the differences in document quality, resulting in wasted computational resources when processing high-quality documents, while the recognition accuracy is insufficient when processing low-quality documents. Furthermore, they lack multi-level verification and semantic-assisted decision-making mechanisms, resulting in insufficient robustness.

Method used

By dynamically configuring preprocessing and recognition parameters through document structure entropy analysis, combined with a multi-level confidence verification mechanism, the preprocessing pipeline strength level is adaptively selected, and an AI recognition model is used to generate recognition engine instances for text detection and recognition. Combined with rule-based coarse screening and multi-person collaborative verification, structured text extraction information is output.

Benefits of technology

It achieves accurate processing of documents of different quality, improves extraction accuracy and system robustness, reduces the risk of recognition errors, and improves recognition performance in complex scenarios.

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Abstract

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.
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