Reading Scene Text in Deep Convolutional Sequences
文献类型：会议论文
| 作者 | Pan He; Weilin Huang; Yu Qiao; Chen Change Loy; Xiaoou Tang |
| 出版日期 | 2016 |
| 会议名称 | AAAI2016 |
| 会议地点 | 美国亚利桑那州凤凰城 |
| 英文摘要 | 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 highlevel 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. It achieves impressive results on several benchmarks, advancing the-state-of-the-art substantially. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10004]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Pan He,Weilin Huang,Yu Qiao,et al. Reading Scene Text in Deep Convolutional Sequences[C]. 见:AAAI2016. 美国亚利桑那州凤凰城. |
入库方式： OAI收割
来源：深圳先进技术研究院
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