Abstract
Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers. Recently, some irregular scene text recognizers either rectify the irregular text to regular text image with approximate 1D layout or transform the 2D image feature map to 1D feature sequence. Though these methods have achieved good performance, the robustness and accuracy are still limited due to the loss of spatial information in the process of 2D to 1D transformation. Different from all of previous, we in this paper propose a framework which transforms the irregular text with 2D layout to character sequence directly via 2D attentional scheme. We utilize a relation attention module to capture the dependencies of feature maps and a parallel attention module to decode all characters in parallel, which make our method more effective and efficient. Extensive experiments on several public benchmarks as well as our collected multi-line text dataset show that our approach is effective to recognize regular and irregular scene text and outperforms previous methods both in accuracy and speed.
Abstract (translated)
不规则场景文本在二维空间具有复杂的布局，对大多数以前的场景文本识别器都具有挑战性。近年来，一些不规则场景文本识别器要么将不规则文本校正为具有近似一维布局的规则文本图像，要么将二维图像特征映射转换为一维特征序列。虽然这些方法取得了良好的性能，但由于二维到一维转换过程中空间信息的丢失，使得其鲁棒性和准确性仍然受到限制。与以往不同的是，本文提出了一个框架，通过二维注意方案，将具有二维布局的不规则文本直接转换为字符序列。利用关系注意模块捕获特征映射的依赖关系，利用并行注意模块对所有字符进行并行解码，使该方法更加有效。对多个公共基准点以及我们收集的多行文本数据集进行的大量实验表明，我们的方法能够有效地识别规则和不规则的场景文本，并且在准确性和速度上都优于以前的方法。
URL
https://arxiv.org/abs/1906.05708
