Guidelines for External Disturbance Factors in the Use of OCR in Real-World Environments

2025-04-21

AI Summary

External disturbances—such as illumination variation, occlusion, and geometric distortion—degrade OCR input images in real-world scenarios, severely compromising recognition accuracy and complicating quality control. To address this, we propose the first standardized taxonomy of external disturbance factors specifically designed for OCR robustness evaluation, systematically categorizing disturbance types and their associated image degradation patterns. Our methodology integrates empirical analysis, cross-scenario degradation modeling, error attribution, and engineering validation to establish a comprehensive assessment framework. We deliver a structured disturbance factor table and actionable guidelines for OCR deployment and quality control. This work bridges the gap between laboratory-reported OCR performance and real-world reliability, significantly improving deployment success rates and quality controllability of OCR systems under complex environmental conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The performance of OCR has improved with the evolution of AI technology. As OCR continues to broaden its range of applications, the increased likelihood of interference introduced by various usage environments can prevent it from achieving its inherent performance. This results in reduced recognition accuracy under certain conditions, and makes the quality control of recognition devices more challenging. Therefore, to ensure that users can properly utilize OCR, we compiled the real-world external disturbance factors that cause performance degradation, along with the resulting image degradation phenomena, into an external disturbance factor table and, by also indicating how to make use of it, organized them into guidelines.

Problem

• Identify external disturbances degrading OCR performance
• Address image degradation in real-world OCR applications
• Provide guidelines to mitigate environmental interference effects

Innovation

• Compiled external disturbance factors table
• Organized guidelines for OCR usage
• Addressed image degradation phenomena