ML Document Separation for Resource Optimization
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Solution Overview
Problem
Document management systems face inefficiencies and resource overload as they process increasing volumes of documents without effective optimization, leading to sluggish performance due to the inability to distinguish between pages belonging to single or multiple documents.
Innovation Solution
A system utilizing a machine learning model to predict whether a set of images belongs to a single document or multiple documents, reducing unnecessary processing by applying document separation only to files predicted to contain multiple documents, thereby optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If document management systems process all incoming documents without differentiation, then comprehensive document handling is achieved, but system performance becomes sluggish and resources are wasted on unnecessary processing
Solution Approach 1:
The system segments the document processing workflow into distinct stages: initial classification to identify single-document files versus multi-document files, followed by selective document separation processing. This segmentation allows the system to apply heavy processing only where necessary, improving overall productivity while reducing wasted computational resources on files that don't require separation.
Solution Approach 2:
The system applies partial action by performing document separation only on files predicted to contain multiple documents, rather than processing all files uniformly. The machine learning model enables the system to take exactly the right amount of action needed - full separation processing only when multi-document content is detected, avoiding excessive processing on single-document files.
2Measurement precision
If document separation processing is applied to all files, then accurate document identification is achieved, but unnecessary processing increases system overhead and reduces efficiency
Solution Approach 1:
The system performs preliminary classification using a machine learning model to predict whether a file contains multiple documents before applying document separation processing. This preliminary action filters out single-document files that don't require separation, allowing the system to maintain high measurement precision for document boundary detection while avoiding the productivity loss from processing unnecessary files.
Solution Approach 2:
The machine learning model acts as an intermediary between file intake and document separation processing. It analyzes file characteristics and predicts multi-document content, serving as a gatekeeper that directs files to appropriate processing paths. This intermediary enables accurate document identification only when needed, maintaining precision while preserving system processing speed.
3Quantity of substance
If the system processes increasing volumes of documents without optimization, then comprehensive document management is maintained, but resource overload occurs and performance degrades
Solution Approach 1:
The system dynamically adjusts processing intensity based on file characteristics. The machine learning model enables real-time classification that adapts processing requirements to each file's actual content structure. This dynamic approach allows the system to handle increasing document volumes reliably by applying computational resources proportionally - intensive processing only for multi-document files, minimal processing for single-document files.
Solution Approach 2:
The system changes processing parameters based on file type predictions. Files predicted to contain multiple documents receive full document separation processing with appropriate computational resources, while single-document files receive minimal processing. This parameter change strategy enables the system to scale document volume handling capacity while maintaining performance stability through efficient resource allocation.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program executable by a device. The program receives a request to process a file. The file includes a set of images of text. The program further converts the text in each image in the set of images into a set of machine-readable text. The program also uses a machine learning model to predict, based on the set of machine-readable text, whether the set of images of the file are images of pages that belong to a single document or images of pages that belong to different documents.