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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2008.7
- 수록면
- 266 - 269 (4page)
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초록· 키워드
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In this paper, geometric invariant features extraction for numeric and grid features extraction for non-numeric are introduced. The geometric invariant feature contains holes and strokes. Some of them are extracted by scanning the binary numeric image from the vertical, the changes occurred in the scan line are recorded as features, and the local feature is also used for classifying the digits. Experimental results show it is a fast and robust classifier for numeric recognition. Grid feature is introduced to recognize the non-numeric characters due to their complex structures. To overcome the lack of feature extraction of non-numeric characters, the back-propagation neural network is selected as a powerful tool which has a good fault tolerance to perform the recognition process. Experiments have been conducted to corroborate the efficiency of the proposed method. Experimental results illustrate that the proposed method has great robustness even in case of low-quality images or the serious distortion images.
목차
Abstract
1. Introduction
2. Digits Recognition
3. Korean Characters‘ Recognition
4. Experiments and Results
5. Conclusions
Acknowledgements
6. References
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UCI(KEPA) : I410-ECN-0101-2012-004-004249793