Document Logical Structure Recognition via Hierarchical Hypothesis Verification
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Solution Overview
Problem
Traditional Optical Character Recognition (OCR) systems fail to comprehend the logical structure of documents, which limits their ability to accurately reproduce the formatting and organization of documents in a machine-readable format, leading to a bottleneck in automated document recognition.
Innovation Solution
A method and system that generate and verify document hypotheses using a collection of models to identify the logical structure of documents, allowing for the reconstruction of the original formatting and organization, including headers, footers, tables, and other form elements, and export the recognized text into structured formats like RTF and PDF.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR systems are used to recognize documents, then character recognition speed is maintained, but the logical structure and formatting of documents cannot be preserved
Solution Approach 1:
The patent segments document analysis into multiple hierarchical levels: character recognition, word recognition, line recognition, block recognition, and document-level logical structure recognition. Each level processes specific features and passes results to the next level, allowing comprehensive structure analysis without overwhelming system complexity at any single stage.
Solution Approach 2:
The patent introduces a new dimension of analysis by creating a hierarchical structure model that operates above traditional flat OCR output. This model adds semantic and structural dimensions to document recognition, transforming 2D image data into multi-layered logical representations that preserve document organization.
2Loss of information
If traditional OCR systems output plain text, then processing speed is maintained, but formatting and layout information is lost
Solution Approach 1:
The patent performs preliminary structure analysis and hypothesis generation during the recognition process itself, rather than as a separate post-processing step. By anticipating and pre-organizing structural elements during OCR, the system preserves formatting information without requiring additional processing passes that would reduce productivity.
Solution Approach 2:
The patent introduces an intermediary hierarchical structure model that sits between the image input and final text output. This mediator captures and preserves formatting and layout information by representing document structure at multiple levels, allowing both speed and information retention.
3Reliability
If document logical structure recognition is implemented, then document understanding is improved, but processing time increases
Solution Approach 1:
The patent implements dynamic hypothesis verification where the system generates multiple possible structural interpretations and verifies them against document features in real-time. The verification process adapts based on confidence levels and document characteristics, allowing the system to invest more time in complex documents and less time in straightforward cases.
Solution Approach 2:
The hierarchical structure model performs self-verification by checking consistency between different recognition levels. Each level validates its findings against the next level, allowing the system to self-correct errors without external intervention and reducing overall processing time through automated consistency checks.
Data Source
AI summary
The invention relates to methods for determining a logical structure of a document. The system stores a collection of models, each of which describes one or more possible logical structures. At least one document hypothesis is generated for the whole document. For each document hypothesis, the system verifies the document hypothesis on each page, for example, by generating at least one block hypothesis for each block in the document based on the document hypothesis, selecting a best block hypothesis for each block, selecting the model that corresponds to a best document hypothesis the document hypothesis that has a best degree of correspondence with the selected best block hypotheses for the document, and forming a representation of the document based on the best document hypothesis described.