Random Document Generation for OCR Testing
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Solution Overview
Problem
The manual extraction of data from paper documents for electronic data processing systems is labor-intensive, time-consuming, and prone to errors, especially when dealing with non-standardized documents containing various formats, artifacts, and extraneous markings, which hinders the efficient testing of automatic document processing systems.
Innovation Solution
A method and system for automatically generating a large number of varied test samples of documents, including standardized and semi-structured forms, populated with random or real data, and incorporating artifacts such as creases and handwriting, to simulate real-world conditions and improve the testing of document processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction of data from paper documents is performed, then data can be transferred to electronic systems, but the process is labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical data extraction with an automated optical document processing system that uses image scanning and computer vision algorithms to automatically extract data from paper documents, eliminating the need for manual typing and data entry while significantly improving processing speed and accuracy
Solution Approach 2:
The system enables documents to process themselves through automated optical character recognition and data extraction algorithms that can independently identify, extract, and validate information from various document formats without human intervention, allowing the system to serve itself in the data extraction task
2Extent of automation
If optical character recognition is used to extract data from documents, then data can be read automatically, but the system struggles with non-standardized documents containing various formats and artifacts
Solution Approach 1:
The patent implements a dynamic document processing system that can adapt its extraction algorithms based on the specific characteristics of each document encountered. The system dynamically adjusts its approach to handle different document formats, layouts, and artifacts by using machine learning models that learn from various document types and improve their adaptability over time
Solution Approach 2:
The system changes processing parameters such as image preprocessing techniques, recognition algorithms, and validation rules based on the detected document type and characteristics. By dynamically adjusting these parameters, the system maintains high automation levels while effectively handling non-standardized documents with various formats and artifacts
3Reliability
If a large number of test samples are generated for system testing, then accurate performance predictions can be made, but the generation process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent uses template-based document generation that creates realistic test samples by copying and populating predefined document templates with varied data. This approach efficiently generates large numbers of test documents with consistent formatting and realistic content variations, enabling comprehensive system testing without manually creating each document from scratch
Solution Approach 2:
The system performs preliminary preparation by creating reusable document templates that contain the structural characteristics of target documents. These pre-prepared templates can be rapidly instantiated with different data to generate numerous test samples, significantly reducing the time and resources required for comprehensive testing while maintaining high reliability in performance predictions
Data Source
AI summary
A computer system and method for generating a plurality of unique, randomly structured forms, such as invoices, that may be populated with data to produce test forms for testing automatic document processing systems. The forms may have major blocks such as a header, a body and a footer, and the major blocks may have randomly selected sizes. For each block, the locations of data fields and the ordering of the data fields within the block are defined randomly, the data locations within the fields and the data formats are randomly defined, and a blank image and an XML file of the randomly structured form are produced. The forms generated may be populated with data for the testing document processing system.