Text detection in natural images based on character classification
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
T-HOG: An effective gradient-based descriptor for single line text regions
Pattern Recognition
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In this paper, a new feature for text verification is proposed. The difficulties for the selection of features for text verification (FTV) are first discussed, followed by two principles for the FTV: the FTV should minimize the influence of backgrounds, and it should also be expressive enough for all the texts varied in structures prominently. In this paper, we exploit different block partition methods and introduce two widely used features: the gray scale contrast (GSC) feature to eliminate the background difference, and the edge orient histogram (EOH) feature to distinguish the structure of texts from that of non-texts. A texture classifier can be got by SVM training of pre-labeled data. The candidate text lines can be verified by this classifier. Experimental results show that our feature performs well.