OCR Pre-processing System Using Gating Neural Network
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Solution Overview
Problem
Conventional OCR systems face significant accuracy reduction when processing low-quality and noisy document images, particularly due to the complexity of handling various degradation types, text formats, and writing systems, and struggle to maintain accuracy without removing desired information like annotations and figures.
Innovation Solution
The implementation of a feedback loop image pre-processing system using conditional computation and deep learning, integrating a mixture of refiners and a gating neural network to select the optimal pre-processing refiner, which efficiently scales with model complexity and handles diverse document image types, noise sources, and writing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional OCR systems process low quality and noisy document images, then they can handle more diverse document types, but accuracy significantly reduces
Solution Approach 1:
The system segments the document image processing task into multiple specialized neural networks (refiners), each trained to handle specific types of degradation, text formats, or writing systems. The gating network divides the input image into regions and selects appropriate refiners for each region, enabling the system to process diverse document types while maintaining high accuracy through specialized processing for each segment.
Solution Approach 2:
The system dynamically selects different pre-processing refiners based on the characteristics of each image region. The gating network analyzes local image features and adaptively chooses the most appropriate refiner for each region, allowing the system to respond to varying degradation patterns and document types in real-time, thus maintaining accuracy across diverse inputs.
2Measurement precision
If a complex neural network model is used to clean noisy document images with many different types of degradation, then pre-processing accuracy improves, but model complexity becomes intractable
Solution Approach 1:
Instead of using a single complex neural network, the system segments the pre-processing task into multiple specialized refiners, each handling specific degradation types. This division reduces the complexity of individual models while collectively achieving high pre-processing accuracy across diverse document types.
Solution Approach 2:
The gating network acts as an intermediary that selects appropriate refiners for different image regions. This mediator component manages the complexity by routing inputs to specialized processors, avoiding the need for a single intractably complex model while maintaining high pre-processing accuracy.
3Measurement precision
If conventional OCR systems perform aggressive pre-processing to improve accuracy, then noise removal improves, but desired information like annotations and figures may be removed
Solution Approach 1:
The system applies different pre-processing refiners to different regions of the document image based on local characteristics. This allows aggressive noise removal in text regions while applying gentler or no processing in regions containing annotations or figures, thus removing noise effectively without losing desired information.
Solution Approach 2:
The gating network dynamically determines the appropriate processing intensity for each region by analyzing local image features. This dynamic adaptation ensures that noise removal is applied selectively and appropriately, preserving annotations and figures while cleaning text regions, thus preventing information loss.
Data Source
AI summary
Disclosed herein are embodiments of an optical character recognition pre-processing software system, which is integrated into a language translation system to provide automated cleaning and correction of noisy and degraded document images to enable seamless and efficient optical character recognition processing and machine translation of information within the document images.