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.

中文翻译：

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场景文本识别的课程学习

场景文本识别 (STR) 是一项具有挑战性的计算机视觉任务。最近在开发复杂网络以提高识别精度方面取得了进展。大多数STR算法侧重于改进网络结构和纠正倾斜文本，以提高文本识别的准确性。受课程学习概念的启发，我们将这种方法应用到文本识别领域。我们提出了一种易于实施的方法，该方法使用课程学习的概念来提高文本识别的准确性。考虑到文本的具体特点，我们建议从人的角度和机器的角度来定义场景图像的难度。所提出方法的关键思想是引导训练过程从训练简单样本开始，逐步增加训练样本的复杂度。实验结果表明，该方法有效地加快了收敛速度，提高了文本识别的准确率。