In this paper, we propose an adversarial learning based attentional scene text recognizer to solve the distortion problem of scene text image. We choose a rectification module which can rectify images in both horizontal and vertical directions, and use a recognizer based on the attention mechanism. Through the adversarial learning of the rectification network and the recognition network, we iteratively improve the rectification effect and the recognition performance. The entire network is trained with weak supervision, so only images and corresponding text labels are needed. Our method achieves high performance for both regular and irregular scene text images, and the experimental results tested on multiple benchmarks prove that our method achieves the performance of state-of-the-art.

本文提出了一种基于对抗学习的注意力场景文本识别器，以解决场景文本图像的失真问题。我们选择一种可以在水平和垂直方向上对图像进行校正的校正模块，并基于注意力机制使用识别器。通过对整流网络和识别网络的对抗学习，迭代地提高了整流效果和识别性能。整个网络都在缺乏监督的情况下进行培训，因此仅需要图像和相应的文本标签。我们的方法在常规和不规则场景文本图像上均实现了高性能，并且在多个基准上测试的实验结果证明了我们的方法可以实现最新的性能。