Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/36635

Title: Text localization in natural images through effective re identification of the MSER
Authors: Mahmood, Hanaa F.
Li, Baihua
Edirisinghe, Eran A.
Keywords: Text detection
Scene images
ICDAR
Feature selection
Issue Date: 17-Oct-2017
Publisher: © Association for Computing Machinery (ACM)
Citation: MAHMOOD, H.F., LI, B. and EDIRISINGHE, E.A., 2017. Text localization in natural images through effective re identification of the MSER. IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning, Liverpool, United Kingdom, October 17th-18th 2017, article no.42
Series/Report no.: ACM International Conference Proceeding Series
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.
Description: This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning, https://doi.org/10.1145/3109761.3109803
Version: Accepted
DOI: 10.1145/3109761.3109803
URI: https://dspace.lboro.ac.uk/2134/36635
Publisher Link: https://doi.org/10.1145/3109761.3109803
ISBN: 9781450352437
Appears in Collections:Conference Papers and Presentations (Computer Science)

Files associated with this item:

File Description SizeFormat
Hanaa-2018-Li.pdfAccepted version610.63 kBAdobe PDFView/Open

 

SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.