Cascading Training for Relaxation CNN on Handwritten Character Recognition

With the development of deep learning, many difficult recognition problems can be solved by deep learning models. For handwritten character recognition, the CNN is used the most. In order to improve the performance of CNN, many new models have been proposed and in which the relaxation CNN [35] is widely used. The relaxation CNN has more complicated structure than CNN while the recognition time is the same with which. However, the training of relaxation CNN needs much more time than CNN. In this paper, we propose the cascading training for relaxation CNN. Our method can train a relaxation CNN of better performance while using almost the same training time with normal CNN. The experimental results proved that the relaxation CNN trained by cascading training is able to achieve the state-of-the-art performance on handwritten Chinese character recognition.