Document Classification Using Visual Style and Content Analysis
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Solution Overview
Problem
Existing methods for extracting structured data from unstructured documents, such as restaurant menus in PDF or image formats, face challenges due to the lack of semantic annotations, leading to high false positives and false negatives, as they rely solely on content and context without considering visual cues.
Innovation Solution
The approach introduces additional classification layers that mimic human pattern recognition by identifying visual artifacts and patterns in documents, combining content and visual style classifiers to improve the accuracy of semantic categorization, using Bayesian filters and supervised learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning techniques are used to extract structured data from unstructured documents, then classification accuracy can be improved with carefully annotated data sets, but the process becomes complex and requires extensive manual annotation
Solution Approach 1:
The patent replaces manual mechanical annotation processes with automated computer vision and machine learning systems. The system automatically extracts visual features, detects patterns, and classifies document elements without requiring human annotators to manually label data, thereby reducing process complexity while maintaining or improving classification accuracy through automated intelligent systems
2Productivity
If automated machine learning techniques are used to classify documents, then processing speed increases, but false positives and false negatives increase due to reliance solely on content and context
Solution Approach 1:
The patent merges multiple classification approaches into a unified system that combines automated machine learning algorithms with visual pattern recognition and contextual analysis. By integrating content-based classification, visual style analysis, and contextual relationships, the system maintains high processing speed while reducing false positives and false negatives through complementary verification mechanisms
3Reliability
If visual style classification is added to content classification, then false positives and false negatives are reduced, but system complexity increases
Solution Approach 1:
The patent segments the classification system into distinct independent modules: content classification module, visual style classification module, and contextual relationship module. Each module processes specific features separately and their outputs are integrated through a coordination layer, allowing the system to handle complex classification tasks by dividing them into manageable segments that can be optimized independently
Data Source
AI summary
Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.