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

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.

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Publication Details

Journal
ArXiv.org
Published
2025-04-21
Primary Topic
Handwritten Text Recognition Techniques
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article
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article

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

ArXiv.org
Handwritten Text Recognition Techniques
article

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

article en

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.

ArXiv.org
National Institute of Advanced Industrial Science and Technology (JP)
Openalex Percentile: Top 49%
Handwritten Text Recognition Techniques
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