Text recognition is an important task in computer vision. The recognition of Chinese texts is challenging because of its wide range, complicated structure, and similar classes. In order to improve this problem, an end-to-end recognition model of text is used. The proposed model uses Dense convolutional Network (DenseNet) to extract features of text images, avoiding artificial design and statistics features. Then, the features are sent to Bidirectional Long Short-Term Memory model (BLSTM) for correlation analysis of local data. This step avoids the character segmentation. Finally, the Connectionist Temporal Classifier (CTC) is used to decode the text information. Experiments show that the proposed model can effectively recognize text images, and has strong robustness to various deformed images.