Welcome to the InfoSci Platform
Reference Hub8
Multilingual Scene Text Detection Using Gradient Morphology

Multilingual Scene Text Detection Using Gradient Morphology

Dibyajyoti Dhar, Neelotpal Chakraborty, Sayan Choudhury, Ashis Paul, Ayatullah Faruk Mollah, Subhadip Basu, Ram Sarkar
Copyright: © 2020 |Volume: 10 |Issue: 3 |Pages: 13
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781799807377|DOI: 10.4018/IJCVIP.2020070103
Cite Article Cite Article

MLA

Dhar, Dibyajyoti, et al. "Multilingual Scene Text Detection Using Gradient Morphology." IJCVIP vol.10, no.3 2020: pp.31-43. http://doi.org/10.4018/IJCVIP.2020070103

APA

Dhar, D., Chakraborty, N., Choudhury, S., Paul, A., Mollah, A. F., Basu, S., & Sarkar, R. (2020). Multilingual Scene Text Detection Using Gradient Morphology. International Journal of Computer Vision and Image Processing (IJCVIP), 10(3), 31-43. http://doi.org/10.4018/IJCVIP.2020070103

Chicago

Dhar, Dibyajyoti, et al. "Multilingual Scene Text Detection Using Gradient Morphology," International Journal of Computer Vision and Image Processing (IJCVIP) 10, no.3: 31-43. http://doi.org/10.4018/IJCVIP.2020070103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Text detection in natural scene images is an interesting problem in the field of information retrieval. Several methods have been proposed over the past few decades for scene text detection. However, the robustness and efficiency of these methods are downgraded due to high sensitivity towards various complexities of an image. Also, in multi-lingual environment where texts may occur in multiple languages, a method may not be suitable for detecting scene texts in certain languages. To counter these challenges, a gradient morphology-based method is proposed in this paper that proves to be robust against image complexities and efficiently detects scene texts irrespective of their languages. The method is validated using low quality images from standard multi-lingual datasets like MSRA-TD500 and MLe2e. The performance of the method is compared with that of some state-of-the-art methods, and comparably better results are observed.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.