Chinese text detection and location for images in multimedia messaging service

Text detection and recognition for images in multimedia messaging service is a very important task. Since Chinese characters are composed of four kinds of strokes, i.e., horizontal line, top-down vertical line, left-downward slope line and short pausing stroke, we present Gabor filters with scale and direction varied to describe the strokes of Chinese characters for candidate text area extraction. By establishing four sub-neural networks to learn the texture of text area, the learnt classifiers are used to detect candidate text areas. Experimental results show that the proposed approach can improve the accuracy of text detection and the recognition rate of images in multimedia messaging service.