NDLI: Specific category region proposal network for text detection in natural scene

Content Provider	IET Digital Library
Author	Zhong, Yuanhong Cheng, Xinyu Zhou, Zhaokun Zhang, Shun Zhang, Jing Huang, Guan
Abstract	Natural scene text usually carries considerable abstract semantic information, which is closely related to the surrounding environment. Thus, natural scene text detection plays a vital role in image content retrieval and understanding. In this study, the authors propose a novel specific category region proposal network (SCRPN) based on maximally stable extremal regions (MSER) and fully convolutional network (FCN) for natural scene text detection. First, FCN for pixel-level recognition is utilised to obtain the text saliency map and MSER is used to obtain oversegmented regions. Then, the multiple features of oversegmented regions and text saliency map are used for region aggregation. Next, single-linkage clustering method is adopted to cluster the segmentation regions to obtain a hierarchical structure of text region proposals. Finally, for the top-ranking region proposals, SCRPN built an end-to-end pipeline for scene text detection directly. Experiments on street view text and international conference on document analysis and recognition (ICDAR) 2013 have demonstrated the effectiveness of SCRPN for generating the text proposals. SCRPN could work with various two-stage text detection networks; thus, faster region convolutional neural network was used as the text detection framework to evaluate the performance of SCRPN in the ICDAR 2015 and MSRA-TD500 benchmarks. The experimental results confirmed that SCRPN makes text detection more robust in complex scenarios.
Starting Page	1832
Ending Page	1839
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0652
Journal	IET Image Processing
Publisher Date	2020-03-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Category Region Proposal Network Computer Vision And Image Processing Technique Considerable Abstract Semantic Information Convolutional Neural Nets Data Handling Technique Document Processing Technique Feature Extraction Fully Convolutional Network Image Classification Image Colour Analysis Image Recognition Image Segmentation Maximally Stable Extremal Region Natural Scene Text Detection Natural Scenes Neural Computing Technique Object Detection Oversegmented Region Pattern Clustering Region Aggregation Region Convolutional Neural Network SCRPN Segmentation Region Street View Text Text Analysis Text Detection Text Detection Framework Text Proposals Text Region Proposals Text Saliency Map Top-ranking Region Proposals Two-stage Text Detection Network Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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