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Line Parameter based Word-Level Indic Script Identification System

Line Parameter based Word-Level Indic Script Identification System

Pawan Kumar Singh, Supratim Das, Ram Sarkar, Mita Nasipuri
Copyright: © 2016 |Volume: 6 |Issue: 2 |Pages: 24
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781466692572|DOI: 10.4018/IJCVIP.2016070102
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MLA

Singh, Pawan Kumar, et al. "Line Parameter based Word-Level Indic Script Identification System." IJCVIP vol.6, no.2 2016: pp.18-41. http://doi.org/10.4018/IJCVIP.2016070102

APA

Singh, P. K., Das, S., Sarkar, R., & Nasipuri, M. (2016). Line Parameter based Word-Level Indic Script Identification System. International Journal of Computer Vision and Image Processing (IJCVIP), 6(2), 18-41. http://doi.org/10.4018/IJCVIP.2016070102

Chicago

Singh, Pawan Kumar, et al. "Line Parameter based Word-Level Indic Script Identification System," International Journal of Computer Vision and Image Processing (IJCVIP) 6, no.2: 18-41. http://doi.org/10.4018/IJCVIP.2016070102

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Abstract

In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Manipuri, Oriya, Urdu, and Roman. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentations are performed at word-level using multiple classifiers on a dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%. The performance of the present technique is also compared with those of other state-of-the-art script identification methods on the same database. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentation are performed at word-level on a total dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%.

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