Knowledge Commons of Institute of Automation,CAS
Realtime multi-scale scene text detection with scale-based region proposal network | |
He, Wenhao1,2; Zhang, Xu-Yao1,2; Yin, Fei1,2; Luo, Zhenbo3; Ogier, Jean-Marc4; Liu, Cheng-Lin1,2,5 | |
发表期刊 | PATTERN RECOGNITION |
ISSN | 0031-3203 |
2020-02-01 | |
卷号 | 98页码:14 |
通讯作者 | Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn) |
摘要 | Multi-scale approaches have been widely used for achieving high accuracy for scene text detection, but they usually slow down the speed of the whole system. In this paper, we propose a two-stage framework for realtime multi-scale scene text detection. The first stage employs a novel Scale-based Region Proposal Network (SRPN) which can localize text of wide scale range and estimate text scale efficiently. Based on SRPN, non-text regions are filtered out, and text region proposals are generated. Moreover, based on text scale estimation by SRPN, small or big texts in region proposals are resized into a unified normal scale range. The second stage then adopts a Fully Convolutional Network based scene text detector to localize text words from proposals of the first stage. Text detector in the second stage detects texts of narrow scale range but accurately. Since most non-text regions are eliminated through SRPN efficiently, and texts in proposals are properly scaled to avoid multi-scale pyramid processing, the whole system is quite fast. We evaluate both performance and speed of the proposed method on datasets ICDAR2015, ICDAR2013, and MSRA-TD500. On ICDAR2015, our system can reach the state-of-the-art F-measure score of 85.40% at 16.5 fps (frame per second), and competitive performance of 79.66% at 35.1 fps, either of which is more than 5 times faster than previous best methods. On ICDAR2013 and MSRA-TD500, we also achieve remarkable speedup by keeping competitive performance. Ablation experiments are also provided to demonstrate the reasonableness of our method. (C) 2019 Elsevier Ltd. All rights reserved. |
关键词 | Scene text detection Multi-scale Speedup Scale-based region proposal network |
DOI | 10.1016/j.patcog.2019.107026 |
关键词[WOS] | VIDEO |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61411136002] ; National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61633021] ; NVIDIA NVAIL program ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61411136002] ; National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61633021] ; NVIDIA NVAIL program |
项目资助者 | National Natural Science Foundation of China (NSFC) ; NVIDIA NVAIL program |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000497600300013 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 文字识别与文档分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29382 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Liu, Cheng-Lin |
作者单位 | 1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 2.UCAS, Beijing 100049, Peoples R China 3.Beijing Samsung Telecom R&D Ctr, Beijing, Peoples R China 4.Univ La Rochelle, Lab L3i, La Rochelle, France 5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | He, Wenhao,Zhang, Xu-Yao,Yin, Fei,et al. Realtime multi-scale scene text detection with scale-based region proposal network[J]. PATTERN RECOGNITION,2020,98:14. |
APA | He, Wenhao,Zhang, Xu-Yao,Yin, Fei,Luo, Zhenbo,Ogier, Jean-Marc,&Liu, Cheng-Lin.(2020).Realtime multi-scale scene text detection with scale-based region proposal network.PATTERN RECOGNITION,98,14. |
MLA | He, Wenhao,et al."Realtime multi-scale scene text detection with scale-based region proposal network".PATTERN RECOGNITION 98(2020):14. |
条目包含的文件 | 条目无相关文件。 |
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