Hanaa-2018-Li.pdf (610.63 kB)
Text localization in natural images through effective re identification of the MSER
conference contribution
posted on 2019-01-21, 11:51 authored by Hanaa Mahmood, Baihua LiBaihua Li, Eran Edirisinghe© 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.
History
School
- Science
Department
- Computer Science
Published in
ACM International Conference Proceeding SeriesCitation
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.42Publisher
© Association for Computing Machinery (ACM)Version
- AM (Accepted Manuscript)
Publication date
2017-10-17Notes
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/10.1145/3109761.3109803ISBN
9781450352437Publisher version
Book series
ACM International Conference Proceeding SeriesLanguage
- en