Abstract The authors proposed a character recognition algorithm to improve the recognition efficiency and recognition accuracy in character recognition. The algorithm is based on a deep belief network classifier. In the character recognition, a comprehensive feature model is established firstly by combining the histogram Gabor feature, grid level feature and gray level co-occurrence matrix. Subsequently, the deep belief network trains the feature model. Finally, the probability model is used to judge the recognition symbols. The algorithm is tested with 74 k data set and is compared and analyzed from three indexes: false acceptance rate, false rejection rate and accuracy. The data set simulation and comparison with other algorithms indicate that the recognition system based on the probability model and depth learning has higher accuracy and better performance.