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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 중앙대학교 영상콘텐츠융합연구소 TECHART: Journal of Arts and Imaging Science TECHART: Journal of Arts and Imaging Science Vol.10 No.2
- 발행연도
- 2023.6
- 수록면
- 31 - 38 (8page)
- DOI
- 10.15323/techart.2023.6.10.2.31
이용수
초록· 키워드
오류제보하기
Scene text segmentation is a critical task in computer vision with applications in optical character recognition (OCR), document analysis, augmented reality, and content-based image retrieval. It accurately predicts text regions from images, accounting for variations in fonts, sizes, orientations, colors, and backgrounds. Compared to semantic and instance segmentation, however, there are a few dataset and text-specific models. This paper introduces reliable publicly available datasets for scene text segmentation. Additionally, we investigate the text segmentation performances on them with widely-used segmentation models including the text-specific method. Based on the qualitative and quantitative evaluation, this study observes the difficulties of text segmentation and limitations of current models, and discusses solutions.
목차
Abstract
1. Introduction
2. Related works
3. Datasets in Experiments
4. Segmentation Models in Experiments
5. Experiments and Results
6. Conclusion
References
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