Learning Co-occurrence of Local Spatial Strokes for Robust Character Recognition
Summary :
In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.7 pp.1937-1941
- Publication Date
- 2014/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.1937
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Song GAO
Chinese Academy of Sciences
Chunheng WANG
Chinese Academy of Sciences
Baihua XIAO
Chinese Academy of Sciences
Cunzhao SHI
Chinese Academy of Sciences
Wen ZHOU
Chinese Academy of Sciences
Zhong ZHANG
Chinese Academy of Sciences
Keyword
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Song GAO, Chunheng WANG, Baihua XIAO, Cunzhao SHI, Wen ZHOU, Zhong ZHANG, "Learning Co-occurrence of Local Spatial Strokes for Robust Character Recognition" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 7, pp. 1937-1941, July 2014, doi: 10.1587/transinf.E97.D.1937.
Abstract: In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1937/_p
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@ARTICLE{e97-d_7_1937,
author={Song GAO, Chunheng WANG, Baihua XIAO, Cunzhao SHI, Wen ZHOU, Zhong ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Learning Co-occurrence of Local Spatial Strokes for Robust Character Recognition},
year={2014},
volume={E97-D},
number={7},
pages={1937-1941},
abstract={In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.},
keywords={},
doi={10.1587/transinf.E97.D.1937},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Learning Co-occurrence of Local Spatial Strokes for Robust Character Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1937
EP - 1941
AU - Song GAO
AU - Chunheng WANG
AU - Baihua XIAO
AU - Cunzhao SHI
AU - Wen ZHOU
AU - Zhong ZHANG
PY - 2014
DO - 10.1587/transinf.E97.D.1937
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2014
AB - In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.
ER -
English
