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In this paper, we abandon the dominant complex language model and rethink thelinguistic learning process in the scene text recognition. Different fromprevious methods considering the visual and linguistic information in twoseparate structures, we propose a Visual Language Modeling Network (VisionLAN),which views the visual and linguistic information as a union by directlyenduing the vision model with language capability. Specially, we introduce thetext recognition of character-wise occluded feature maps in the training stage.Such operation guides the vision model to use not only the visual texture ofcharacters, but also the linguistic information in visual context forrecognition when the visual cues are confused (e.g. occlusion, noise, etc.). Asthe linguistic information is acquired along with visual features without theneed of extra language model, VisionLAN significantly improves the speed by 39%and adaptively considers the linguistic information to enhance the visualfeatures for accurate recognition. Furthermore, an Occlusion Scene Text (OST)dataset is proposed to evaluate the performance on the case of missingcharacter-wise visual cues. The state of-the-art results on several benchmarksprove our effectiveness. Code and dataset are available athttps://github.com/wangyuxin87/VisionLAN.

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network