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- W2043593442 abstract "Although a great success has been achieved for the situation of high quality images during the past decades, Character recognition in low quality images still remains a challenge. To tackle this challenge, in this paper a novel method in the SVM framework is proposed to recognize the characters in low quality document images by using local and global features. Firstly, a multi-scale sliding window strategy with a pruning method of character traits is adopted to generate potential character sub-regions. Then, the conventional global feature and state-of-art local feature, namely histogram of oriented gradients (HOG), are extracted to form the representation of the potential character sub-region. Finally, the Support Vector Machine (SVM) is used to recognize characters with a late fusion strategy. Experimental results show that the proposed method has a better performance even in the situation of existing touched and broken characters situation compared to the conventional method." @default.
- W2043593442 created "2016-06-24" @default.
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- W2043593442 date "2014-10-01" @default.
- W2043593442 modified "2023-09-23" @default.
- W2043593442 title "Character recognition in low quality document images using local and global features" @default.
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- W2043593442 doi "https://doi.org/10.1109/cisp.2014.7003864" @default.
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