Document Classification via Page and Document Level Analysis
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Solution Overview
Problem
Current document classification solutions are prone to manual errors and misclassification, particularly due to the lack of consideration for page relationships across documents, leading to inefficiencies in digitization and resource-intensive corrections, which impact document integrity and management accuracy.
Innovation Solution
A computer-implemented method using a machine learning document classification model that simulates human understanding by classifying documents at both individual page and entire document levels, generating classification confidence scores to enhance accuracy, and retraining based on user feedback and statistics to improve document grouping and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current document classification solutions treat each page as an individual unit and identify pages based on keyword weightage only, then the classification process is simple and fast, but document classification accuracy deteriorates due to misclassification and lack of page relationship consideration
Solution Approach 1:
The patent divides the document classification task into two distinct levels: page-level classification (identifying individual page types) and document-level classification (grouping pages into complete documents). This segmentation allows the system to handle complex relationships between pages while maintaining manageable processing steps, thereby improving classification accuracy without overwhelming system complexity.
Solution Approach 2:
The patent implements a feedback mechanism where the system generates classification results, receives user feedback on misclassified documents, and retrain s the machine learning model to improve future classifications. This continuous feedback loop enables the system to learn from errors and progressively improve accuracy, resolving the contradiction between simple processing and accurate results.
2Measurement precision
If personnel manually verify and correct document classification errors in the postprocessing phase, then classification accuracy can be improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent performs classification at both page and document levels simultaneously using a machine learning model, rather than simply grouping pages after individual classification. This preliminary action at the document level anticipates and prevents misclassification errors before they occur, reducing the need for time-consuming manual verification and correction in the postprocessing phase.
Solution Approach 2:
The system employs automated machine learning models that self-correct classification errors through feedback mechanisms, reducing reliance on manual human intervention. The model retrains itself using user feedback, enabling the system to improve its own accuracy over time without requiring continuous manual oversight, thereby minimizing postprocessing time and resource consumption.
3Stability of the object's composition
If current solutions group pages into documents based on keyword availability on individual pages, then the grouping process is straightforward, but document integrity deteriorates due to misplaced pages and incorrect grouping
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between page-level keyword analysis and document-level grouping. This intermediary analyzes both the content of individual pages and the relationships between pages, using learned patterns to determine proper document boundaries and page sequences. This intermediary layer maintains document integrity by correctly identifying document structures while preserving the relative simplicity of the overall process through automated decision-making.
Data Source
AI summary
Classifying documents is provided. A document is received for classification. The classification of the document is performed at an individual page level and an entire document level based on classification confidence scores generated by the machine learning document classification model at the individual page level and the entire document level to increase classification accuracy.