A hybrid method of natural scene text detection using MSERs masks in HSV space color

Houssem Turki; Mohamed Ben Halima; Adel M. Alimi

doi:10.1117/12.2268993

17 March 2017 A hybrid method of natural scene text detection using MSERs masks in HSV space color

Houssem Turki, Mohamed Ben Halima, Adel M. Alimi

Author Affiliations +

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 1034111 (2017) https://doi.org/10.1117/12.2268993
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

Text detection in natural scenes holds great importance in the field of research and still remains a challenge and an important task because of size, various fonts, line orientation, different illumination conditions, weak characters and complex backgrounds in image. The contribution of our proposed method is to filtering out complex backgrounds by combining three strategies. These are enhancing the edge candidate detection in HSV space color, then using MSER candidate detection to get different masks applied in HSV space color as well as gray color. After that, we opt for the Stroke Width Transform (SWT) and heuristic filtering. Such strategies are followed so as to maximize the capacity of zones text pixels candidates and distinguish between text boxes and the rest of the image. The non-text components are filtered by classifying the characters candidates based on Support Vector Machines (SVM) using Histogram of Oriented Gradients (HOG) features. Finally we apply boundary box localization after a stage of word grouping where false positives are eliminated by geometrical properties of text blocks. The proposed method has been evaluated on ICDAR 2013 scene text detection competition dataset and the encouraging experiments results demonstrate the robustness of our method.

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Houssem Turki, Mohamed Ben Halima, and Adel M. Alimi "A hybrid method of natural scene text detection using MSERs masks in HSV space color", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034111 (17 March 2017); https://doi.org/10.1117/12.2268993

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