The attention mechanism has become the \emph{de facto} module in scene text recognition (STR) methods, due to its capability of extracting character-level representations. These methods can be summarized into implicit attention based and supervised attention based, depended on how the attention is computed, i.e., implicit attention and supervised attention are learned from sequence-level text annotations and or character-level bounding box annotations, respectively. Implicit attention, as it may extract coarse or even incorrect spatial regions as character attention, is prone to suffering from an alignment-drifted issue. Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories. To address the aforementioned issues, we propose a novel attention mechanism for STR, self-supervised implicit glyph attention (SIGA). SIGA delineates the glyph structures of text images by jointly self-supervised text segmentation and implicit attention alignment, which serve as the supervision to improve attention correctness without extra character-level annotations. Experimental results demonstrate that SIGA performs consistently and significantly better than previous attention-based STR methods, in terms of both attention correctness and final recognition performance on publicly available context benchmarks and our contributed contextless benchmarks.

翻译：注意力机制已成为场景文本识别（STR）方法中的事实标准模块，因其具备提取字符级表征的能力。根据注意力计算方式的不同，这些方法可分为隐式注意力方法与监督注意力方法，前者从序列级文本标注中学习，后者则从字符级边界框标注中学习。隐式注意力可能提取到粗糙甚至错误的空间区域作为字符注意力，因此容易受到对齐漂移问题的影响。监督注意力可缓解上述问题，但其具有字符类别特异性，需要额外繁琐的字符级边界框标注，且在处理字符类别较多的语言时可能对内存造成较大负担。为解决上述问题，我们提出了一种新型STR注意力机制——自监督隐式字形注意力（SIGA）。SIGA通过联合自监督文本分割与隐式注意力对齐来描绘文本图像的字形结构，从而在不依赖额外字符级标注的情况下提供监督以提升注意力准确性。实验结果表明，在注意力正确性及公开上下文基准数据集和本文贡献的无上下文基准数据集上的最终识别性能方面，SIGA相较于现有基于注意力的STR方法均取得了一致且显著的性能提升。