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Text localization in natural images through effective re identification of the MSER
Authors
Chen H.
Chowdhury A.
+7 more
Gonzalez
Lucas S.M.
Matas J.
Neumann L.
Serra J.
Soh L. K.
Yao C.
Publication date
17 October 2017
Publisher
Doi
Cite
Abstract
© 2017 Association for Computing Machinery. Text detection and recognition from images have numerous applications for document analysis and information retrieval tasks. An accurate and robust method for detecting texts in natural scene images is proposed in this paper. Text-region candidates are detected using maximally stable extremal regions (MSER) and a machine learning based method is then applied to refine and validate the initial detection. The effectiveness of features based on aspect ratio, GLSM, LBP, HOG descriptors are investigated. Text-region classifiers of MLP, SVM and RF are trained using selections of these features and their combination. A publicly available multilingual dataset ICDAR 2003,2011 has been used to evaluate the method. The proposed method achieved excellent performance on both databases and the improvements are significant in terms of Precision, Recall, and F-measure. The results show that using a suitable feature combination and selection approach can can significantly increase the accuracy of the algorithms
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Loughborough University Institutional Repository
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oai:figshare.com:article/94050...
Last time updated on 26/03/2020
Crossref
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info:doi/10.1145%2F3109761.310...
Last time updated on 10/08/2021