Automated OCR Training Material Generation With Noisy Backgrounds
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Solution Overview
Problem
Current methods for generating training materials for optical character recognition (OCR) are time-consuming and prone to errors due to manual annotation, leading to fatigue and reduced accuracy.
Innovation Solution
Automated generation of digital training materials by selecting terms, generating multiple images with varying visual appearances, and positioning them on backgrounds with noise, to replicate real-world document conditions, using a computing device with processing and memory circuitry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation is used to generate training materials, then the training materials can be created with human judgment and correction, but the process is time-consuming and prone to errors due to technician fatigue
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated computer-based system that uses image processing and pattern recognition algorithms to generate training materials, eliminating technician fatigue and time constraints while maintaining or improving accuracy
Solution Approach 2:
The system enables self-service generation of training materials by automatically processing document images, extracting text regions, and creating annotated training datasets without requiring human technicians to perform the repetitive annotation tasks
2Reliability
If a wide variety of training materials are used to improve OCR accuracy, then the training process becomes more comprehensive, but the manual annotation process becomes even more time-consuming and demanding
Solution Approach 1:
The automated system performs multiple functions simultaneously - it processes various document types, applies different annotation rules, and generates diverse training materials through a single unified platform, reducing the perceived complexity for users while maintaining comprehensive training coverage
Solution Approach 2:
The system segments the complex annotation process into distinct automated stages including image preprocessing, text region detection, character segmentation, and annotation generation, making each stage manageable and automatable while collectively handling the full complexity of diverse training material creation
3Manufacturing precision
If manual annotation is performed to create precise training materials, then the training data quality is high, but the productivity of the annotation process is low
Solution Approach 1:
The patent substitutes manual annotation mechanics with automated computer-based image processing and pattern recognition systems that can process numerous documents simultaneously, achieving both high precision through algorithmic consistency and high productivity through parallel processing capabilities
Solution Approach 2:
The system performs preliminary automated processing of document images including preprocessing, text detection, and initial segmentation before final annotation generation, preparing the data in advance to enable rapid high-precision annotation without compromising quality
Data Source
AI summary
The application is directed to the generation of training materials for optical character recognition. Generating the training materials for optical character recognition can include selecting a plurality of terms that include a string of characters. For each term, generating multiple digital term images that each includes the term with a different visual appearance. For generation of a training document, the method includes positioning the term images on a digital background and generating the digital training material.