In this paper, we abandon the dominant complex language model and rethink the
linguistic learning process in the scene text recognition. Different from
previous methods considering the visual and linguistic information in two
separate structures, we propose a Visual Language Modeling Network (VisionLAN),
which views the visual and linguistic information as a union by directly
enduing the vision model with language capability. Specially, we introduce the
text recognition of character-wise occluded feature maps in the training stage.
Such operation guides the vision model to use not only the visual texture of
characters, but also the linguistic information in visual context for
recognition when the visual cues are confused (e.g. occlusion, noise, etc.). As
the linguistic information is acquired along with visual features without the
need of extra language model, VisionLAN significantly improves the speed by 39%
and adaptively considers the linguistic information to enhance the visual
features for accurate recognition. Furthermore, an Occlusion Scene Text (OST)
dataset is proposed to evaluate the performance on the case of missing
character-wise visual cues. The state of-the-art results on several benchmarks
prove our effectiveness. Code and dataset are available at
this https URL

本文提出一种名为 VisionLAN 的可提升文字识别速度与精度的模型，结合视觉和语言信息的 Visual Language Modeling，以直接赋予形象模型带有语言能力，从而在训练阶段引导视觉模型利用视觉文本特征以及上下文信息的语言能力进行字符特征判断，绕过视觉噪声等干扰因素。本文中的 Occlusion Scene Text 数据集通过缺失部分字符信息的数据，进一步验证了我们的模型在复杂环境下的有效性。