Recognition of Multi-Fontstyle Characters Based on Convolutional Neural Network

Convolutional Neural Networks are popularly used in OCR and document recognition. This paper applies stochastic diagonal Levenberg-Marquardt method into a convolutional network, which is presented by Simard. The relations between the sample class number, global learning rate and the network´s convergence speed are discussed, Experiments on different train sets showed that class number is an essantial factor to the neural network´s convergence. We have successfully expeanded Simard network into recognition of multi-font style little character set like Baidu CAPTCHA and got a recognition rate as 98.4% in single Baidu CAPTCHA character, and 93.5% as the overall rate. Experiments in this paper has confirmed that Convolutional Neural Network can be successfully used in recognition of multi-fontstyle little character set.