Scene text recognition is a hot research topic in computer vision. Recently,
many recognition methods based on the encoder-decoder framework have been
proposed, and they can handle scene texts of perspective distortion and curve
shape. Nevertheless, they still face lots of challenges like image blur, uneven
illumination, and incomplete characters. We argue that most encoder-decoder
methods are based on local visual features without explicit global semantic
information. In this work, we propose a semantics enhanced encoder-decoder
framework to robustly recognize low-quality scene texts. The semantic
information is used both in the encoder module for supervision and in the
decoder module for initializing. In particular, the state-of-the art ASTER
method is integrated into the proposed framework as an exemplar. Extensive
experiments demonstrate that the proposed framework is more robust for
low-quality text images, and achieves state-of-the-art results on several
benchmark datasets.

本研究提出了一种基于编码器 - 解码器框架的语义增强模型来识别低质量的场景文本，它使用显式的全局语义信息，并将现有的 ASTER 方法作为示例，实验证明了该模型对低质量文本图像更加鲁棒，并在多个基准数据集上取得了最先进的结果。