Document comparison analysis method and system based on table structure analysis

Abstract

The invention belongs to the technical field of data processing, and particularly relates to a document comparison analysis method and system based on table structure analysis. The method comprises the following steps of: S1, receiving various types of source files, and uniformly converting the source files into PDF files; S2, aiming at different types of content parts in the PDF file, extracting, dividing and identifying by using different tools to obtain table data and non-table data with text contents, coordinate information and table structures; and S3, respectively comparing the table data with the non-table data to finally obtain out-of-table text difference and table difference. The system comprises a file conversion module, a file identification module and a data comparison module. The method has the advantages that the method focuses on comparison of the document content and the semantic level, has the ability of comparison between table structures and semantics in document comparison, and is good in comparison effect, low in occupied resource and accurate in character recognition.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for better organization by aligning pictures with tables or other items that are being displayed together within an electronic system's screen space. It also includes mechanisms for identifying changes made during processing (such as adding new pages) without affecting previous ones. Overall, this technology improves efficiency and accuracy when displaying documents across different screens.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the quality control (QC) of documentation verification processes due to variations introduced over time through various factors like format type, file organization structures, processing techniques, and human subjectivity. Current solutions require manually checkings every few months but they lack precision because these checks only work well within specific boundaries. Additionally, conventional approaches use complex algorithms involving pixel templates match technology, resulting in high computational costs.

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