Artificial intelligence-based automatic financial document information input system

By using a dual-domain adaptive enhancement method based on transposed self-attention and a document intelligent partitioning method based on Transformer, the problem of low text detection efficiency in financial invoice information entry systems under complex backgrounds and noise interference is solved, achieving efficient invoice image partitioning and information entry.

CN121074935BActive Publication Date: 2026-03-20BEIJING KAIXUAN CHUANGZHI TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511161750.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-19
Publication Date
2026-03-20
Estimated Expiration
2045-08-19

AI Technical Summary

Technical Problem

Existing financial invoice information entry systems have low text detection efficiency and quality under complex backgrounds or noise interference, and cannot adapt to the recognition problem when the invoice format changes.

Method used

Image denoising is achieved by employing a feature dual-domain adaptive enhancement method based on transposed self-attention, combined with the document intelligent partitioning method of Transformer. High-frequency and low-frequency components are separated through image pyramids and learnable masks to improve image denoising capabilities. At the same time, Sentence-BERT is used for semantic querying and instance interaction to achieve efficient partitioning and information entry of tickets.

Benefits of technology

It improves the efficiency and accuracy of text detection, can adapt to changes in invoice format, and achieves efficient invoice image partitioning and information extraction.

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Abstract

The application belongs to the field of intelligent ticket management, and specifically relates to a financial ticket information automatic input system based on artificial intelligence, which comprises a ticket collection module, a multi-scale self-adaptive noise reduction module, an intelligent classification module and an intelligent partition input module; the application adopts a feature double-domain self-adaptive enhancement method based on transpose self-attention, improves noise reduction robustness, specifically processes different noise types, overcomes the limitation of a single domain, restores the global structure while retaining the details, increases the noise reduction capability of the ticket image, and improves the text detection efficiency; the application adopts a document intelligent partition method, unifies heterogeneous tasks into the combination of instance and semantic segmentation, avoids model redundancy, maps the category name into a semantic query, supports open set classification and zero sample migration, realizes the dynamic interaction of instance and semantic query through hybrid query, enhances the understanding of the model to the complex document structure, and realizes efficient ticket image partition information extraction and input.
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