AB-LSTM

Author:

Liu Zhandong1,Zhou Wengang1,Li Houqiang1

Affiliation:

1. University of Science and Technology of China, Shushan District, Hefei, China

Abstract

Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has benefited from Fully Convolutional Network. Text cues contained in multi-level convolutional features are complementary for detecting scene text objects. How to explore these multi-level features is still an open problem. To tackle the above issues, we propose an Attention-based Bidirectional Long Short-Term Memory (AB-LSTM) model for scene text detection. First, word stroke regions (WSRs) and text center blocks (TCBs) are extracted by two AB-LSTM models, respectively. Then, the union of WSRs and TCBs are used to represent text objects. To verify the effectiveness of the proposed method, we perform experiments on four public benchmarks: CTW1500, Total-text, ICDAR2013, and MSRA-TD500, and compare it with existing state-of-the-art methods. Experiment results demonstrate that the proposed method can achieve competitive results, and well handle scene text objects with arbitrary shapes (i.e., curved, oriented, and horizontal forms).

Funder

Youth Innovation Promotion Association of the Chinese Academy of Sciences

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Reference54 articles.

1. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. Retrieved from Arxiv Preprint Arxiv:1409.0473 (2014). Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. Retrieved from Arxiv Preprint Arxiv:1409.0473 (2014).

2. Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition

3. Paying more attention to saliency: Image captioning with saliency and context attention. ACM Trans. Multimedia Comput., Commun;Cornia Marcella;Applic.,2018

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3