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arXiv · 1506.04395

Reading Scene Text in Deep Convolutional Sequences

Pan He·Weilin Huang·Yu Qiao·Chen Change Loy·Xiaoou Tang
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Abstract

We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered high-level sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. Codes for the DTRN will be available.

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Pan He, Weilin Huang, Yu Qiao, Chen Change Loy, Xiaoou Tang. 2015-06-14. Reading Scene Text in Deep Convolutional Sequences. https://arxiv.org/abs/1506.04395

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