Contextual Vector Grids for Document Processing Accuracy
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Solution Overview
Problem
Current optical character recognition (OCR) and machine learning algorithms fail to provide semantic meaning to identified characters in documents, lacking contextual information which leads to inaccurate and incorrect results in document analysis.
Innovation Solution
A system that generates a vector grid by analyzing document files using an embedding function and language model AI to map contextual vector values, incorporating positional and neighboring symbol information, enabling downstream processing systems to perform more accurate document analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional OCR and machine learning algorithms are used to identify characters and words, then character and word extraction can be achieved, but semantic meaning and contextual information are lost
Solution Approach 1:
The patent transforms the traditional one-dimensional sequence of identified characters into a two-dimensional contextual grid that preserves spatial relationships and contextual information. This dimensional transformation allows the system to maintain semantic meaning by organizing identified elements in a grid structure that reflects their positional and contextual relationships in the original document.
Solution Approach 2:
The patent segments the document processing into distinct stages: initial character/word identification followed by contextual relationship analysis. By dividing the processing into separate phases and representing contextual relationships in a grid format, the system preserves semantic information without requiring complete reprocessing of the entire document.
2Measurement precision
If contextual information is incorporated into document analysis, then accuracy of document analysis improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs contextual relationship mapping in advance by creating a grid representation that captures spatial and contextual relationships between identified elements. This preliminary structuring of contextual information allows downstream analysis tasks to access pre-processed contextual data, reducing the computational burden and processing time required for subsequent document analysis operations.
3Productivity
If simple character and word identification is used, then processing speed is maintained, but document analysis beyond basic text extraction becomes inaccurate
Solution Approach 1:
The patent introduces a contextual grid representation as an intermediary structure between basic character identification and downstream document analysis tasks. This grid serves as a mediator that preserves essential contextual and spatial information from the original document, enabling more reliable analysis without requiring complete reprocessing of the source material, thus maintaining processing efficiency.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for analyzing contextual symbol information for document processing. In an embodiment, a language model system may generate a vector grid that incorporates contextual document information. The language model system may receive a document file and identify symbols of the document file to generate a symbol grid. The language model system may also identify position parameters corresponding to each of the symbols. The language model system may then analyze the symbols using an embedding function and neighboring symbols to determine contextual vector values corresponding to each of the symbols. The language model system may then generate a vector grid mapping the contextual vector values using the position parameters. The contextual information from the vector grid may provide increase document processing accuracy as well as faster processing convergence.