Automated Document Separation via Content Analysis
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Solution Overview
Problem
Current methods for scanning and organizing documents are inefficient, relying on manual placement of separator sheets, which can lead to errors such as missing or extra pages, and incorrect routing to workflows or repositories, lacking automated categorization and grouping of scanned pages.
Innovation Solution
A system and method for automatically identifying and classifying scanned pages, determining initial pages, and appending subsequent pages to create electronic documents, eliminating the need for separator sheets by using classification weights and bookmarks to organize and route documents accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual separator sheets are used to organize documents, then document grouping is achieved, but time consumption and error rates increase
Solution Approach 1:
The system enables self-service document organization by automatically detecting document boundaries and grouping pages using content analysis. The scanner identifies first pages through classification algorithms and automatically appends subsequent pages without requiring manual separator sheet placement, allowing the system to organize itself without human intervention.
Solution Approach 2:
The patent replaces the mechanical system of physical separator sheets with an automated digital classification system. Instead of using physical dividers to mark document boundaries, the system uses optical character recognition, content analysis, and machine learning algorithms to detect first pages and automatically group pages into documents, eliminating the need for mechanical insertion of separator sheets.
2Ease of operation
If manual separator sheets are used for document separation, then document grouping is possible, but accuracy and reliability decrease due to human errors
Solution Approach 1:
The system incorporates feedback mechanisms where the classification algorithm continuously analyzes page content, detects patterns indicating first pages, and adjusts its classification thresholds based on detected document structures. This feedback loop ensures accurate identification of document boundaries and proper grouping, eliminating the reliability issues associated with manual separator sheet placement.
Solution Approach 2:
The automated system performs self-verification by analyzing content patterns and document structures to confirm proper document separation. The system independently validates its own classification decisions through multiple detection methods, ensuring high accuracy without requiring human verification of separator sheet placement.
3Ease of operation
If separator sheets are manually placed, then document organization is achieved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical systems involving physical separator sheets with a streamlined digital processing system. The complexity is shifted from physical manipulation to software-based content analysis, using algorithms to detect document boundaries and group pages automatically, thereby reducing overall system complexity while maintaining or improving functionality.
4Productivity
If automated page classification is implemented, then scanning efficiency improves, but processing complexity increases
Solution Approach 1:
The classification system is segmented into modular components: initial page detection algorithms, content analysis engines, document boundary identification routines, and page appending mechanisms. This segmentation allows each component to be optimized independently and processed in sequence, improving scanning efficiency while managing complexity through modular architecture.
Data Source
AI summary
A method of automatically separating a stack of pages into documents includes receiving a plurality of page images from a scanner; extracting content from each of the plurality of page images; for each of the plurality of page images, determining, based upon the extracted content, whether the page image is an initial page image of a document; and appending each of the plurality of page images located between a first determined initial page image and a second determined initial page image to the first determined initial page image.