Optical Character Recognition Calibration via Evolutionary Parameter Optimization
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Solution Overview
Problem
Mobile OCR processing faces challenges due to varying environmental conditions, such as lighting, lens quality, and resolution, resulting in less accurate output compared to controlled environments, and requires manual adjustments that are time-consuming and uncertain.
Innovation Solution
A system that automatically calibrates OCR process settings by iteratively adjusting image parameters, mimicking biological evolution to select high-scoring candidates, mixing and mutating settings to optimize OCR processing in varying environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error adjustments are made to optimize OCR parameters, then OCR accuracy can be improved, but the process becomes time-consuming and tedious
Solution Approach 1:
The system performs self-calibration by automatically testing multiple parameter combinations and selecting the optimal settings without requiring manual user intervention. The mobile device autonomously adjusts OCR parameters based on environmental conditions, eliminating the need for time-consuming manual trial-and-error adjustments while maintaining high OCR accuracy.
Solution Approach 2:
The system systematically varies multiple OCR parameters (such as contrast, brightness, sharpness, and color thresholds) to find the optimal combination for current environmental conditions. By automatically changing and testing parameter values, the system quickly identifies the best settings without manual intervention, resolving the contradiction between accuracy and time consumption.
2Measurement precision
If OCR processing is performed in controlled environments with fixed equipment, then processing accuracy is high, but the system lacks adaptability to varying mobile environments
Solution Approach 1:
The system dynamically adapts OCR parameters based on real-time environmental conditions detected by the mobile device. Instead of using fixed parameters designed for controlled environments, the system continuously adjusts contrast, brightness, sharpness, and other parameters to match varying lighting, camera quality, and document conditions, maintaining high accuracy across diverse mobile environments.
Solution Approach 2:
The system uses feedback from OCR processing results to iteratively improve parameter selection. By analyzing the quality of OCR output and comparing it against expected results, the system learns from each processing attempt and adjusts parameters accordingly, enabling adaptation to different environments while maintaining high accuracy.
3Measurement precision
If multiple parameter combinations are tested to find optimal settings, then OCR accuracy improves, but the complexity of the processing system increases
Solution Approach 1:
The system tests a limited but sufficient number of parameter combinations rather than exhaustively testing all possible values. By selecting representative parameter sets and using intelligent sampling strategies, the system achieves near-optimal accuracy without requiring complex computational resources or lengthy processing times, thus managing system complexity effectively.
Data Source
AI summary
The disclosed embodiments relate to a system and method for calibrating optical character recognition (OCR) processes for an image captured through a mobile computing device. During operation, the system adjusts the OCR process through pre-recognition functions, OCR functions and/or post-recognition functions with multiple sets of parameter settings. With each of these sets, the system scores the OCR process output against an image with known text. Once the sets are scored, the system sorts the sets of parameters, removes some sets, then mixes and mutates the remaining sets in a process akin to evolutionary biology. By repeating this procedure, the system produces a set of parameter settings that can be used to calibrate OCR processing.