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The recognition methods for Chinese text lines, as an important component of optical character recognition, have been widely applied in many specific tasks. However, there are still some potential challenges: (1) lack of open Chinese text recognition dataset; (2) challenges caused by the characteristics of Chinese characters, e.g., diverse types, complex structure and various sizes; (3) difficulties brought by text images in different scenes, e.g., blur, illumination and distortion. In order to address these challenges, we propose an end-to-end recognition method based on convolutional recurrent neural networks (CRNNs), i.e., multi-scale attention CRNN, which adds three components on the basis of a CRNN: asymmetric convolution, feature reuse network and attention mechanism. The proposed model is mainly aimed at scene text recognition including Chinese characters. Then the model is trained and tested on two Chinese text recognition datasets, i.e., the open dataset MTWI and our constructed large-scale Chinese text line dataset collected from various scenes. The experimental results demonstrate that the proposed method achieves better performance than other methods. [ABSTRACT FROM AUTHOR]