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논문 기본 정보

자료유형
학술대회자료
저자정보
Yunseo Jeong (Sejong University) Seokjun Kwon (Sejong University) Jeongmin Shin (Sejong University) Yukyung Choi (Sejong University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2024
발행연도
2024.10
수록면
43 - 48 (6page)

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초록· 키워드

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In recent years, research on machine vision systems with artificial intelligence has rapidly increased in the manufacturing industry for identifying defects. Specifically, Optical Character Recognition (OCR) technology has witnessed significant advancements, enabling the effective recognition of characters inscribed on parts. However, a key limitation of prior approaches lies in their restriction to character-centric images. This constraint renders them unsuitable for real-world manufacturing environments where small characters are encountered within a large field of view (FOV). In order to address this limitation, we present a novel two-stage framework. This framework commences with the detection of resistor regions within the image. Subsequently, the identified regions containing the small characters are fed into an OCR model for accurate recognition. In addition, we propose a novel labeling method that leverages a clustering algorithm. Experimental results demonstrate the effectiveness and efficiency of our framework in the real manufacturing industry setting.

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Abstract
1. INTRODUCTION
2. RELATED WORK
3. METHOD
4. EXPERIMENTS
5. CONCLUSION
REFERENCES

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