Scene text recognition (STR) is a challenging computer vision task. Recent progress has been made on developing a complex network to increase recognition accuracy. Most STR algorithms focus on improving the network structure and correcting slanted text to improve the accuracy of text recognition. Inspired by the concept of curriculum learning, we applied this method to the field of text recognition. We propose an easy-to-implement method that improves the accuracy of text recognition using the concept of curriculum learning. Taking into account the specific characteristics of text, we propose defining the difficulty of scene images from both the human perspective and the machine perspective. The key idea of the proposed method is to guide the training process to begin with training simple samples and progressively increase the complexity of the training samples. Experimental results demonstrate that the proposed method effectively accelerates the convergence and improves the accuracy of text recognition.