Scene Text Recognition Using Progressive Rectification Network And Spelling Error Correction Language Model

Scene text recognition has gained popularity in deep neural network research. Compared to document text recognition, scene text recognition faces challenges such as complex backgrounds, diverse fonts, and blurred characters. Visual and semantic information must be considered in text recognition. While recent research has focused on improving semantic information, most studies have used English text datasets. Directly applying these methods to Chinese text datasets may not be effective. This work proposes a vision model with progressive rectification network and a Chinese scene text recognition method that uses a robust error-correcting language model to correct errors predicted by vision models. Firstly, the proposed progressive rectification network is of more effectiveness on the multi-oriented scene text images compared to present rectification method. On the other hand, the designed language model is used to correct errors predicted by vision models. The language model can handle low-quality images, including blurred, occluded, or nonsensical text. Experiments demonstrate that our method outperforms recent classic and state-of-the-art methods, making it a more powerful and suitable option for Chinese scene text recognition.