Text localization in natural images through effective re-identification of the MSER
Article No.: 42, Pages 1 - 9
Abstract
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. Keywords---text detection; scene images; ICDAR; feature selection.
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Published In
October 2017
581 pages
ISBN:9781450352437
DOI:10.1145/3109761
- General Chairs:
- Hani Hamdan,
- Djallel Eddine Boubiche,
- Program Chair:
- Fanny Klett
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Published: 17 October 2017
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IML 2017
IML 2017: International Conference on Internet of Things and Machine Learning
October 17 - 18, 2017
Liverpool, United Kingdom
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