Machine Learning Document Processing with Multi-Stage Classification
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Solution Overview
Problem
Document processing using optical character recognition often fails to consider the characteristics of a large corpus of documents, leading to less accurate results due to insufficient document count and skew in document categories, which affects machine learning applications.
Innovation Solution
A machine learning document processing system that builds and trains multiple classifiers such as K-nearest neighbors (k-NNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs) to extract and classify semantic and structural features in documents, identifying similar and dissimilar documents, and determining document feature categories based on these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical character recognition is used for document processing, then text detection capability is provided, but document processing accuracy deteriorates due to insufficient consideration of document corpus characteristics
Solution Approach 1:
The system segments document processing into multiple independent classification stages: document type classification, section classification, and element classification. Each stage uses specialized machine learning classifiers trained on specific features, allowing accurate processing while maintaining modular system architecture that avoids excessive complexity.
Solution Approach 2:
The system performs preliminary classification of documents into types and sections before detailed element extraction. Training data is pre-processed to create labeled datasets with document types, sections, and elements annotated, enabling the classifiers to learn patterns in advance and improve processing accuracy without increasing runtime complexity.
2Measurement precision
If a small number of documents are used for machine learning training, then processing speed is maintained, but classification accuracy deteriorates due to insufficient document count and category skew
Solution Approach 1:
The system transforms unstructured document data into structured feature vectors with specific parameters (document type, section, element attributes). By changing the representation parameters and using multiple classification levels, the system achieves high accuracy with limited training documents by maximizing the information extracted from each document's feature space.
Solution Approach 2:
Training data is pre-processed to create balanced datasets with proper representation of all document categories. The system performs preliminary analysis to identify category skew and adjusts training data composition accordingly, ensuring sufficient coverage of rare document types without requiring large volumes of additional documents.
3Measurement precision
If multiple machine learning classifiers are deployed, then document feature classification accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The classification process is segmented into three sequential stages: document type classification, section classification, and element classification. Each stage uses specialized classifiers that process only relevant features for that level, reducing the computational burden on each individual classifier while achieving comprehensive classification accuracy through the multi-stage approach.
Solution Approach 2:
The system applies classification at multiple levels (document, section, element) which may seem excessive, but each level provides partial classification that builds upon the previous level. This partial classification approach at each stage accumulates to complete and accurate document analysis, justifying the multiple classification operations.
Data Source
AI summary
A machine learning document processing system performs natural language processing (NLP) and machine learning to determine a subset of documents from a document dataset based on the structural features and semantic features. The system facilitates an interactive process, e.g., through a client application, to receive user input from a user to identify documents with a specific document feature category. The user input may be provided from a user as speech or text, and NLP is performed on the user input to determine user intent, the document features, and document feature category. Using the user intent and the additional document feature category, the system identifies subsets of the document dataset that matches the document feature category for display.