Adaptive FrȨchet Kernel Based Support Vector Machine for Text Detection
Shiyan Hu, Minya Chen · 2006
A novel general paradigm for text detection using a support vector machine (SVM) is proposed. Unlike prevailing techniques in the literature, our adaptive SVM incorporates information from each input image. In addition, for better classification results and higher efficiency, a novel kernel called the Fre/spl acute/chet kernel is presented for SVM classification. The adaptive SVM aims to serve as a general paradigm to improve the prevailing techniques. In the experiment, we apply the paradigm to a simple algorithm and successfully obtain a new competitive method for text detection.