Automated Document Separation Using Sender Analysis and Feedback Learning
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Solution Overview
Problem
Existing document separation methods, such as using page separators, are inefficient and require manual corrections, which can be time-consuming and prone to errors, especially when incorrect placement of separators necessitates rescanning multiple documents.
Innovation Solution
An automatic electronic document separation system that analyzes sender information from pages to split documents without separators, using a knowledge base to learn from previous corrections and improve accuracy over time by incrementing success and failure counters for senders and applying automatic correction rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual correction programs are employed to correct misplaced page separators, then correction capability is provided, but the programs cannot learn from prior corrections and require repeated manual intervention
Solution Approach 1:
The system captures feedback from user corrections and automatically updates the knowledge base with new separation patterns and rules. This feedback loop enables the system to learn from manual corrections and improve future automatic separations without requiring repeated manual intervention.
Solution Approach 2:
The system performs self-improvement by automatically learning from user corrections and updating its internal knowledge base. This self-service capability allows the system to enhance its own performance without external programming, transforming manual corrections into automated learning opportunities.
2Productivity
If separator pages are used for document separation, then batch processing is enabled, but time is required to properly insert page separators between documents
Solution Approach 1:
The system extracts and removes the physical separator pages from the document flow, replacing them with automated digital separation based on content analysis. This extraction eliminates the time-consuming manual insertion of physical separators while maintaining batch processing capability.
Solution Approach 2:
The system replaces the mechanical process of physically inserting separator pages with an automated digital analysis system that separates documents based on extracted text, images, and metadata. This substitution eliminates manual separator insertion time while enabling efficient batch processing.
3Productivity
If page separators are used for document separation, then documents can be separated in batches, but incorrect placement requires rescanning multiple documents
Solution Approach 1:
The system continuously monitors separation results and uses feedback from user corrections to refine its accuracy. This feedback mechanism ensures that separation accuracy improves over time, eliminating the need to rescan documents due to incorrect separator placement.
Solution Approach 2:
The system performs preliminary analysis of document content, images, and metadata before separation to predict accurate document boundaries. This preliminary action reduces incorrect placement and the need for rescanning, while maintaining batch processing efficiency.
Data Source
AI summary
Systems and methods for automated document separation. The system includes a host device that is configured to communicate with one or more client devices over a network. The host device includes a splitting module, a correction module, a knowledge base, and a document store. The splitting module is configured to perform a multi-level document splitting. Pages are grouped into documents based on the sender information, the lack of sender information, and whether the sender is known. The splitting module performs an automatic correction of the initial document separation based on information stored within the knowledge base. The knowledge base is updated each time a document is processed and a user provides feedback related to whether the documents were successfully separated. Based on the success or failure of a particular document separation, the knowledge base evaluates the modifications made by a user to learn from the errors made during document separation.