Document Logical Structure Recognition in OCR Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional OCR systems fail to comprehend the logical structure of documents, which is essential for accurate representation and formatting, leading to a bottleneck in automated document recognition and conversion.
Innovation Solution
A method and system that generate and verify document hypotheses to identify the logical structure of documents by analyzing the arrangement of form elements, selecting the best block hypotheses, and reconstructing the document to replicate its original layout and formatting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR systems are used to transform document images into computer-readable text, then text recognition is achieved, but the logical structure and meaning of form elements are lost
Solution Approach 1:
The document is segmented into form elements (headers, footers, body text, tables, images) with distinct logical roles. Each form element is identified and classified separately, allowing the system to preserve the logical structure while recognizing text content. This segmentation enables the OCR system to not only recognize text but also understand the purpose and meaning of different document components.
Solution Approach 2:
The system adds a logical structure dimension to the traditional text recognition output. Instead of only producing plain text with basic formatting, the system generates a multi-dimensional representation that includes physical layout information and logical structure information (form element types, relationships, reading order). This additional dimension preserves the meaning and purpose of form elements while maintaining text recognition accuracy.
2Shape
If OCR systems reconstruct physical layout of form elements, then document appearance is preserved, but understanding of form element purpose and relations is not achieved
Solution Approach 1:
The system introduces form element classification as an intermediary layer between physical layout detection and logical structure understanding. By identifying and categorizing form elements (distinguishing headers from body text, tables from images, etc.), the system bridges the gap between visual arrangement and semantic meaning. This intermediary classification enables the system to understand the purpose of each element and its relationships to other elements.
Solution Approach 2:
The system changes the parameter set used to describe form elements from purely spatial coordinates to include logical attributes (form element type, purpose, relationships). By transforming the representation parameters from physical-only to physical-plus-logical, the system enables computer comprehension of form element meaning while maintaining layout information.
3Device complexity
If traditional OCR output plain text with simplified formatting, then processing is simplified, but document's logical structure and reading order are not maintained
Solution Approach 1:
The system performs preliminary classification of form elements during the OCR processing stage, before final output generation. By identifying and tagging form elements with their logical roles early in the process, the system preserves reading order and logical structure information without adding significant complexity to the overall processing pipeline. This preliminary action ensures that logical structure is maintained throughout subsequent processing steps.
Solution Approach 2:
The system creates a universal output format that serves multiple functions: it contains text content for searchability, physical layout information for formatting preservation, and logical structure information for proper reading order and meaning. This multi-functional output structure allows the same processed document to be used for various purposes (search, formatting, logical analysis) without requiring separate processing pipelines.
Data Source
AI summary
In one embodiment, the invention provides a method for determining a logical structure of a document. The method comprises generating at least one document hypothesis for the whole document; for each document hypothesis, verifying said document hypothesis including (a) generating at least one block hypothesis for each block in the document based on the document hypothesis; and (b) selecting a best block hypothesis for each block; selecting as a best document hypothesis the document hypothesis that has the best degree of correspondence with the selected best block hypotheses for the document; and forming the document based on the best document hypothesis.