지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 중앙대학교 영상콘텐츠융합연구소 TECHART: Journal of Arts and Imaging Science TECHART: Journal of Arts and Imaging Science Vol.11 No.2
- 발행연도
- 2024.5
- 수록면
- 50 - 57 (8page)
- DOI
- 10.15323/techart.2024.5.11.2.50
이용수
초록· 키워드
오류제보하기
Scene text segmentation is fundamental in computer vision applications such as optical character recognition (OCR) for augmented reality (AR). This study introduced the signed distance function (SDF) in scene text segmentation to mitigate jagged artifacts around text boundaries. In addition, high-resolution images require large-scale memory storage to store precise SDF maps. Implicit neural representations (INR) were used to learn and store SDF maps in a compressed form. INR-based methods were compared, focusing on the effectiveness of MetaSparseINR in pruning networks to reduce storage requirements. The study shows better performance of the MetaSparseINR than random pruning and excels in few-shot learning contexts while maintaining higher fidelity in SDF representation. Extensive experiments verified the excessive storage demands of traditional SDFs, and an INR-based approach mitigated this storage issue. Finally, by leveraging MetaSparseINR, the study offers advancements in efficient scene text segmentation by reducing storage size and learning time without compromising quality.
목차
Abstract
1. Introduction
2. Related Works
3. Compared Methods in Experiments
4. Experiments
6. Conclusion
References
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