Document Parser Segmentation for Mixed Text and Math Recognition
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Solution Overview
Problem
Existing document recognition techniques struggle to accurately distinguish between text and mathematical expressions, leading to incorrect recognition and mistranslation when both types of content are present in a document, as they rely on characteristics that may conflict or be ambiguous, such as shared symbols and formatting.
Innovation Solution
A document parser identifies regions as either text or mathematics regions using specific parsers for each type, allowing a text recognizer to process text regions and a mathematics recognizer to process mathematical regions, enabling accurate recognition and compilation of both into a composite document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single recognizer is applied to the entire document, then the device complexity is reduced, but the recognition accuracy deteriorates due to ambiguous symbols and formatting that can be interpreted as either text or mathematical expressions
Solution Approach 1:
The patent divides the document into distinct text regions and mathematical expression regions using region classification. This segmentation allows different recognition techniques to be applied to different regions, resolving the contradiction by maintaining low overall system complexity while achieving high local recognition accuracy through specialized processors for each region type.
Solution Approach 2:
The patent applies different recognition qualities to different parts of the document. Text regions use natural language recognition techniques while mathematical regions use mathematical expression recognition techniques. This local quality approach ensures that each region is processed with the most appropriate method, improving overall recognition accuracy without requiring a completely complex unified system.
2Device complexity
If a text recognizer is applied to mathematical expressions, then the device complexity is reduced, but the recognition accuracy deteriorates as mathematical symbols are incorrectly interpreted as text
Solution Approach 1:
The patent segments the document to identify mathematical expression regions separately from text regions. This segmentation ensures that mathematical expressions are not incorrectly processed by text recognizers, maintaining recognition correctness while keeping the system structure simple through automated region classification.
Solution Approach 2:
The patent applies text recognition only to text regions and mathematical expression recognition only to mathematical regions. This local quality approach ensures that each type of content is processed with the appropriate recognition technique, eliminating the reliability issue of misinterpreting mathematical symbols as text while maintaining manageable system complexity.
3Device complexity
If a mathematical expression recognizer is applied to text regions, then the device complexity is reduced, but the recognition accuracy deteriorates as text is incorrectly interpreted as mathematical expressions
Solution Approach 1:
The patent segments the document into text regions and mathematical expression regions using classification techniques. This segmentation prevents mathematical expression recognizers from incorrectly processing text regions, maintaining high recognition accuracy while keeping the system structure simple through automated region identification.
Solution Approach 2:
The patent applies mathematical expression recognition only to identified mathematical regions and text recognition only to text regions. This local quality approach ensures that text is not incorrectly interpreted as mathematical expressions, maintaining recognition accuracy without requiring a completely complex unified recognition system.
4Measurement precision
If region classification is implemented to distinguish text and mathematical regions, then the recognition accuracy is improved, but the device complexity increases due to multiple parsers and recognizers
Solution Approach 1:
The patent implements region classification that segments the document into text and mathematical expression regions. This segmentation improves recognition accuracy by enabling specialized processing for each region type while managing system complexity through a structured approach to region identification and classification.
Solution Approach 2:
The patent applies different recognition qualities to different regions through automated classification. This local quality approach improves overall recognition accuracy by ensuring each region is processed appropriately while managing system complexity through a organized multi-parser architecture that handles different region types systematically.
Data Source
AI summary
Symbol recognition techniques may be applied to documents comprising various forms of content. Documents including both text and mathematical expressions may be problematic, as applying a recognizer that does not match the content may produce anomalous results. Instead, a parser may evaluate the document to classify respective regions as one of a text region or a mathematics region, based on the characteristics of each type of content. The recognizer corresponding to the content of each region may be applied to produce a composite document comprising both recognized text expressions and recognized mathematical expressions. Additional functionality may be presented based on the recognized content; e.g., text-based tools such as spell-check and natural-language translation may be applied to the text regions that comprise text expressions, and mathematics-based tools such as mathematical equation editors, mathematical expression solvers, and automated assignment grading may be applied to the mathematics regions that comprise mathematical expressions.