Document Sectionization Using Visual and Language Models
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Solution Overview
Problem
Existing sectionization processes in electronic documents are brittle and poorly suited for diverse document types, as they rely on fixed rules that fail to adapt to variations in visual layouts and OCR errors, leading to inaccurate identification of section headers.
Innovation Solution
A system using a visual model and a language model to identify and classify section headers in documents, employing OCR operations, fuzzy matching techniques, and named-entity recognition to determine section header types, even when documents deviate from predefined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed rules are used to identify section headers, then the process is simple and fast, but the system becomes brittle and intolerant of slight deviations in document formatting
Solution Approach 1:
The patent transitions from static fixed rules to dynamic machine learning models that can adapt to varying document formats. The visual model dynamically adjusts to different layouts, fonts, and styling patterns, while the language model adapts to diverse section header phrasing and structures, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The system changes the parameters used for section header identification from rigid formatting criteria (fixed font size, boldness, positioning) to flexible semantic and visual features analyzed by ML models. This allows the system to maintain high accuracy across varying document formats while preserving computational efficiency through optimized model inference.
2Measurement precision
If fixed rules tuned for specific document types are used, then identification accuracy is high for those types, but the system works poorly on other document types with different visual layouts
Solution Approach 1:
The patent implements a universal section header identification system using visual and language models that can handle multiple document types and formats. The visual model detects various styling patterns across different document types, while the language model recognizes diverse section header phrasing, enabling the system to maintain high accuracy across healthcare, legal, financial, and other document types without type-specific tuning.
Solution Approach 2:
The system introduces machine learning models as intermediaries between the document input and section header identification output. These models serve as adaptive mediators that translate various document formats and styling conventions into unified section header detections, resolving the contradiction between precision for specific types and versatility across types.
3Manufacturing precision
If strict formatting criteria are enforced, then consistent documents are processed accurately, but OCR errors cause false negatives where valid section headers are missed
Solution Approach 1:
The patent employs machine learning models trained on varied document samples including those with OCR errors and formatting inconsistencies. This training acts as beforehand cushioning, enabling the models to tolerate and correctly identify section headers even when OCR introduces slight variations in text recognition, thereby maintaining both consistency and robustness.
Solution Approach 2:
The system uses confidence scoring and validation mechanisms where the visual and language models provide feedback on their detection confidence. When confidence is high, detections are accepted; when confidence is lower or inconsistencies are detected, the system can apply additional validation or adjustment, resolving the contradiction between strict consistency and error tolerance.
Data Source
AI summary
Some embodiments provide a program that receives a request to sectionize a document, uses a visual model to identify a set of candidate section headers in the document, and uses a language model to determine a type of section header for at least one candidate section header in the set of candidate section headers in the document. Some embodiments provide a program that receives a request to anonymize data in a document, uses a visual model to identify a set of candidate confidential sections in the document that are each predicted to include a collection of confidential data, uses a language model to identify terms in each candidate confidential section that are determined to be confidential data, analyzes the document to identify a set of terms in the document based on the identified terms in the set of candidate confidential sections, and redacts the set of terms in the document.