Automatic Document Separation via Dynamic Clustering
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Solution Overview
Problem
Existing document processing systems lack an efficient method for automatically separating documents into independent subdocuments without manual configuration, especially when dealing with scanned or merged digital documents, which often contain varying entities across pages.
Innovation Solution
The system employs supervised and unsupervised learning techniques to extract features from documents, analyze page similarity, and dynamically select clustering algorithms to separate pages into distinct groups, allowing for automatic document separation without pre-training or configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration is used for document separation, then separation accuracy can be maintained, but processing efficiency and automation level deteriorate
Solution Approach 1:
The system performs self-service by automatically extracting features from document pages, computing similarity metrics, selecting appropriate clustering algorithms, and separating subdocuments without requiring manual configuration. The algorithm autonomously benchmarks multiple clustering methods and selects the optimal one based on the document characteristics, enabling fully automated document separation while maintaining high accuracy.
2Measurement precision
If pre-training or configuration is applied to improve separation accuracy, then separation precision improves, but system complexity and setup time increase
Solution Approach 1:
The system dynamically changes parameters by extracting features from the actual document content and using these extracted features to determine the optimal clustering algorithm. Instead of using fixed pre-configured parameters, the system adapts its separation strategy based on the specific characteristics of each document, such as text density, layout patterns, and page similarity metrics, thereby achieving high accuracy without complex pre-training.
Solution Approach 2:
The system implements dynamics by making the separation process adaptive rather than static. It benchmarks multiple clustering algorithms and dynamically selects the most suitable one based on the document's feature extraction results. This dynamic approach allows the system to adjust its behavior to match the specific requirements of each document, maintaining high separation accuracy while avoiding the complexity of manual configuration for each case.
3Reliability
If multiple clustering algorithms are benchmarked to improve separation quality, then separation reliability improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by benchmarking multiple clustering algorithms but not necessarily executing all of them to completion. Instead, it evaluates the algorithms based on extracted features and similarity metrics, then selects only the most appropriate algorithm for the specific document. This approach ensures high separation reliability through algorithm comparison while minimizing the time loss by avoiding exhaustive execution of all benchmarked algorithms on every document.
Data Source
AI summary
In an approach for an automatic document separation, a processor extracts one or more features from a document containing a plurality of pages. A processor generates a data frame based on the feature extraction. In response to analyzing a similarity between the plurality of pages, a processor determines whether the similarity exceeds a predetermined threshold. In response to determining that the similarity does not exceed the predetermined threshold, a processor transforms text into vectors forming float arrays. In response to benchmarking a set of predetermined clustering algorithms, a processor identifies a clustering algorithm using a predetermined criterion. A processor clusters the plurality of pages, using the clustering algorithm, to create a group of pages. A processor validates the clustered group of pages. In response to passing validation, a processor generates a set of final separated files based on the clustered group of pages.