Character recognition based on corner detection and convolution neural network
Beihai Tan, Qiuming Peng, Xiaojiao Yao, Chao Hu, Zhenhao Xu, Zipei Zhang · 2017
Text detection and character recognition plays a very important role in the field of computer vision. Although there are many studies of character recognition, the existing text detection methods are mainly concentrated in the English characters, and now there is a great application needs in Chinese text detection. As Chinese characters are more complex than English, Chinese character detection requires more efficient technology. Aiming at the English and Chinese characters in the picture, this paper presents a character recognition method based on corner detection and convolution neural network. Firstly, we use image processing technology to preprocess the input image, and then use the corner detection method to mark the text candidate area. Secondly, the image histogram of oriented gridients (HOG) feature extraction and support vector machine technology are used to filter the text candidate area. Thirdly, the integral projection method is applied to split the character area as single character blocks. Finally, the model of convolution neural network is used to identify the segmented character blocks. The experimental results have shown the effectiveness of the proposed method in the test image set.