Abstract
In this paper we propose a novel character representation for scene text recognition. In order to recognize each individual character, we first adopt a bag-of-words approach, in which the rotation-invariant circular Fourier-HOG features are densely extracted from an individual character and compressed into middle level features. Then we train a set of two-class linear Support Vector Machines in a one-vs-all schema to rank the compressed features by their contributions to the classification. Based on the ranking result we further select and keep those top rated features to build a compact and discriminative codebook. By using densely extracted features that are rotation-invariant and efficient, our method is capable of recognizing perspective texts of arbitrary orientations, and can be combined with the existing word recognition methods. Experimental results demonstrates that our method is highly efficient and achieves state-of-the-art performance on several benchmark datasets.
Y. Zhou and S. Liu are contributed equally.
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Acknowledgment
This paper is partially supported by National Natural Science Foundation of China under Contract nos. 61303170, 61402472 and 61471235, and also supported by the National High Technology Research and Development Program of China (863 programs)under Contract nos. 2013AA014703 and 2012AA012803.
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Zhou, Y., Liu, S., Zhang, Y., Wang, Y., Lin, W. (2015). Perspective Scene Text Recognition with Feature Compression and Ranking. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_14
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