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Handwritten Chinese character recognition is an important research direction and application field of pattern recognition and machine learning. In recent years, with the development of the theory and the new technology, deep neural network have made a breakthrough in the field of image recognition and image generation. Specialty, Deep Residual Networks as the latest method, has been successfully applied to handwritten numeral recognition, image recognition classification and other fields. In this paper, we study the application of Deep Residual Networks in off-line isolated handwritten Chinese character recognition, and optimize the performance of Deep Residual Networks by improving the unit structure of residual learning module. At the same time, we improve the model performance by preprocessing the training set. Then, the experiment is designed to verify the feasibility of the Deep Residual Networks and End-to-End mode in off-line handwritten Chinese character recognition. And finally we analyze and summarize the existing problems and future research directions.