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Characters, which are man-made symbols composed of strokes arranged in a certain structure, could provide semantic information and play an indispensable role in our daily life. In this paper, we try to make use of the intrinsic characteristics of characters and explore the stroke and structure-based methods for character recognition. First, we introduce two existing part-based models to recognize...
Text information contained in scene images is very useful for image understanding. In this paper, we propose a high-level representation named stroke bank for scene character recognition. Inspired by the work of object bank, we train stroke detectors and use detectors' maximal output as features. Specifically, we collect training samples for stroke detectors based on labeled key points. We also propose...
Detecting text in scene images is very challenging due to complex backgrounds, various fonts and different illumination conditions. Without prior knowledge, a detector previously trained using lots of samples still perform badly on a test image because of the disparities in distributions between the training samples and the testing ones. In this paper, we propose to adapt a pre-trained generic scene...
A method of object detecting based on local contour learning and matching is proposed. Firstly, the representative images are obtained through unsupervised clustering to be as templates. The local contour information of template is extracted and generalized as the template feature, at the same time, codebook dictionary of local contour is built up. Secondly, based on codebook dictionary, using simple...
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