Text image skew correction is an important preprocessing step in the front-end of character recognition. To overcome the disadvantage in the limited range of tilt angle detection of the existing methods which is only -90--90°, this study transforms the text image skew angle detection problem into a skew angle class detection problem. Several types of skew angle classes of text images are detected using the classification function of deep convolution neural network by selecting the appropriate loss function and designing the detection structures of one-stage two classification and multi-stage multi-classification, and then getting the tilt angle range of the text image. The experimental results show that the tilt angle class’s detection accuracy, recall, and precision rates are all above 0.93. The classical deep learning method is used to recognize the text image after skew correction. Moreover, the recognition accuracy is greatly improved compared to that before the correction.