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Multi-branch guided attention network for irregular text recognition | |
Wang, Cong1,2![]() ![]() | |
发表期刊 | Neurocomputing
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ISSN | 0925-2312 |
2020 | |
卷号 | 425期号:0页码:0 |
通讯作者 | Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn) |
摘要 | Reading irregular text of arbitrary shape or low quality in natural scene images is a challenging task. Existing irregular scene text recognition methods mainly focus on irregular text with arbitrary shape, but rarely focus on irregular text of low quality. In this work, we propose a simple but effective method for recognizing irregular texts with arbitrary shape and low quality simultaneously. The proposed Multi-Branch guided Attention Network (MBAN) makes mutual guidance among multi-branch data in training, so as to learn invariant semantic representation between regular text images and the corresponding irregular images. Compared with the standard attention framework for text recognition, MBAN can significantly improve the performance of irregular text recognition while preserving similar performance for regular text recognition. In addition, regarding the attention drift problem encountered in standard attention network, MBAN can significantly improve the accuracy of alignment factors at each time step. We verify the effectiveness of MBAN in irregular text recognition and attention drift problem through extensive experiments. The performance of MBAN is shown to be comparable on regular datasets and superior on some irregular datasets with state-of-the-art methods. |
关键词 | Irregular text recognition, Mutual guidance, Multi-branch guided attention network (MBAN) |
DOI | 10.1016/j.neucom.2020.04.129 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61721004] |
项目资助者 | Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000632014300007 |
出版者 | ELSEVIER |
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/173211/39728 |
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.中国科学院自动化研究所 2.中国科学院大学 3.中国科学院脑科学与智能技术卓越创新中心 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang, Cong,Liu, Cheng-Lin. Multi-branch guided attention network for irregular text recognition[J]. Neurocomputing,2020,425(0):0. |
APA | Wang, Cong,&Liu, Cheng-Lin.(2020).Multi-branch guided attention network for irregular text recognition.Neurocomputing,425(0),0. |
MLA | Wang, Cong,et al."Multi-branch guided attention network for irregular text recognition".Neurocomputing 425.0(2020):0. |
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Multi-branch guided (2370KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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