Perspective Scene Text Recognition with Feature Compression and Ranking

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.