Abstract :
Recently, the deep learning methods have achieved great success in pattern recognition tasks. Especially for character recognition, most of the state-of-the-art results belong to the deep learning models. Among those models, the convolutional neural network (CNN) becomes the most popular due to its outstanding performance. Therefore, many trials were made in order to make improvements on CNN. However, most of the trials only focused on the network structure or training skills, the inter-class information is usually ignored. In this paper, we have proposed a novel CNN model with two training feedbacks: the reconstruction feedback and the classification feedback. By using the reconstruction feedback, the inter-class information (for example, shape similarity) of the characters is taken into account. Consequently, without enlarging the network structure, our model can outperform those state-of-the-art improved CNN models, which is proved by the experimental results.
