Reading scene text with fully convolutional sequence modeling
文献类型：期刊论文
| 作者 | Gao, Yunze1,2 ; Chen, Yingying1,2; Wang, Jinqiao1,2; Tang, Ming1,2; Lu, Hanqing1,2
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2019-04-28 |
| 卷号 | 339页码:161-170 |
| 关键词 | Fully convolutional sequence modeling Scene text recognition |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.01.094 |
| 通讯作者 | Chen, Yingying(yingying.chen@nlpr.ia.ac.cn) |
| 英文摘要 | Reading text in the wild is a challenging task in computer vision. Existing approaches mainly adopt connectionist temporal classification (CTC) or attention models based on recurrent neural network (RNN), and are computationally expensive and hard to train. In this paper, instead of the chain structure of RNN, we propose an end-to-end fully convolutional network with the stacked convolutional layers to effectively capture the long-term dependencies among elements of scene text image. The stacked convolutional layers are much more efficient than bidirectional long short-term memory (BLSTM) in modeling the contextual dependency. In addition, we design a discriminative feature encoder by incorporating the residual attention blocks into a small densely connected network to enhance the foreground text and suppress the background noise. Extensive experiments on seven standard benchmarks, the Street View Text, IIIT5K, ICDAR03, ICDAR13, ICDAR15, COCO-Text and Total-Text, validate that our method not only achieves state-of-the-art or highly competitive recognition performance, but significantly improves the efficiency and reduces the number of parameters as well. (C) 2019 Elsevier B.V. All rights reserved. |
| WOS关键词 | RECOGNITION |
| 资助项目 | National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000461166500016 |
| 出版者 | ELSEVIER SCIENCE BV |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/24984]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 通讯作者 | Chen, Yingying |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Yunze,Chen, Yingying,Wang, Jinqiao,et al. Reading scene text with fully convolutional sequence modeling[J]. NEUROCOMPUTING,2019,339:161-170. |
| APA | Gao, Yunze,Chen, Yingying,Wang, Jinqiao,Tang, Ming,&Lu, Hanqing.(2019).Reading scene text with fully convolutional sequence modeling.NEUROCOMPUTING,339,161-170. |
| MLA | Gao, Yunze,et al."Reading scene text with fully convolutional sequence modeling".NEUROCOMPUTING 339(2019):161-170. |
入库方式： OAI收割
来源：自动化研究所
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