Grammatical Parsing for Visual Recognition Tasks
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Solution Overview
Problem
Traditional OCR techniques face difficulties in processing complex documents and two-dimensional recognition tasks due to computational complexity and the challenge of storing and retrieving vast amounts of possible patterns, especially with elements like equations and documents with enhanced structures.
Innovation Solution
The use of grammatical parsing with image recognition to score parse trees and subtrees, combined with geometric constraints and integral images, facilitates efficient analysis and recognition of documents by rendering images and employing machine learning for classification, thereby improving parsing speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional OCR techniques are used to process complex documents with equations and enhanced structures, then recognition capability is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent segments the complex recognition task into multiple hierarchical levels: first segmenting the document into regions (headers, footers, body text), then further segmenting regions into lines and words. This hierarchical segmentation reduces the computational complexity by breaking down the exponential problem into manageable polynomial sub-problems at each level.
Solution Approach 2:
The patent transitions from traditional one-dimensional linear scanning to two-dimensional spatial analysis by introducing spatial relationships and geometric constraints. This dimensional change allows the system to leverage the natural two-dimensional layout of documents, reducing search space and computational complexity while improving recognition accuracy for complex structures.
2Adaptability or versatility
If traditional OCR techniques are used to process complex documents with equations and enhanced structures, then recognition capability is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary actions by first segmenting the document into regions and establishing spatial relationships before actual character recognition. Geometric constraints and spatial models are pre-computed and used to guide subsequent recognition steps, avoiding exhaustive search and significantly improving processing speed for complex documents.
Solution Approach 2:
By dividing the document into hierarchical segments (page → regions → lines → words → characters), the system processes only relevant portions at each level using geometric constraints, rather than processing the entire document uniformly. This selective segmentation dramatically improves processing speed while maintaining recognition capability.
3Measurement precision
If database oriented recognition systems are used to store equation elements, then recognition accuracy is improved, but database size and retrieval time increase
Solution Approach 1:
The patent changes the approach from storing complete equation element images in a database to using parameter-based geometric constraints and spatial relationships. Instead of retrieving stored images, the system defines equations through mathematical parameters and geometric properties, dramatically reducing database size while maintaining recognition accuracy through parametric matching.
Data Source
AI summary
Image recognition is utilized to facilitate in scoring parse trees for two-dimensional recognition tasks. Trees and subtrees are rendered as images and then utilized to determine parsing scores. Other instances of the subject invention can incorporate additional features such as stroke curvature and/or nearby white space as rendered images as well. Geometric constraints can also be employed to increase performance of a parsing process, substantially improving parsing speed, some even resolvable in polynomial time. Additional performance enhancements can be achieved in yet other instances of the subject invention by employing constellations of integral images and/or integral images of document features.