Automated Document Categorization Using OCR Error Feature Vectors
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Solution Overview
Problem
Manual categorization of large volumes of electronic documents is impractical and inefficient, especially when dealing with low-quality images containing noise and defects, which conventional OCR algorithms struggle to process accurately.
Innovation Solution
An apparatus equipped with an optical character recognition (OCR) algorithm and natural language processing (NLP) algorithm that converts images into text, identifies errors, generates feature vectors incorporating error types, and uses machine learning to assign images to document categories, thereby automating the categorization and assembly of electronic documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization is used for electronic documents, then categorization accuracy can be maintained through human judgment, but processing efficiency and productivity deteriorate due to the impracticality of manually reviewing large volumes of documents
Solution Approach 1:
The patent introduces an intermediary system comprising OCR algorithms, error detection mechanisms, and machine learning classifiers that act as a mediator between the physical document and the categorization database. This intermediary automatically processes document images, extracts text, identifies errors, and performs categorization, thereby resolving the contradiction between maintaining accuracy and improving processing efficiency.
2Speed
If conventional OCR algorithms are used on low-quality images, then processing speed can be maintained, but measurement precision and manufacturing precision deteriorate due to noise and defects in the images
Solution Approach 1:
The patent applies preliminary error detection and correction actions before the main OCR processing step. The system first identifies potential errors in the extracted text by analyzing noise patterns and defects in the original image, then corrects these errors before proceeding with categorization. This preliminary action improves text extraction accuracy without significantly compromising processing speed.
3Productivity
If automated OCR and categorization systems are implemented, then productivity and processing capacity improve, but device complexity and measurement precision requirements increase due to the need for accurate error identification in noisy images
Solution Approach 1:
The patent segments the automated document processing system into distinct functional modules: image preprocessing, OCR text extraction, error detection, error correction, and categorization. Each module handles a specific aspect of the processing pipeline, making the overall complex system more manageable and maintainable while preserving high productivity benefits.
4Measurement precision
If error detection and correction features are added to improve text accuracy, then measurement precision improves, but device complexity and processing time increase
Solution Approach 1:
The patent changes key parameters in the error detection and correction process by adjusting sensitivity thresholds, error type weights, and correction confidence levels. These parameter adjustments allow the system to achieve high text accuracy while controlling processing time, as the system can adaptively skip or simplify certain error correction steps based on image quality and error severity.
Data Source
AI summary
An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.