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Word-Level Script Identification Using Texture Based Features

Word-Level Script Identification Using Texture Based Features

Pawan Kumar Singh, Ram Sarkar, Mita Nasipuri
Copyright: © 2015 |Volume: 4 |Issue: 2 |Pages: 21
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781466680364|DOI: 10.4018/ijsda.2015040105
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MLA

Singh, Pawan Kumar, et al. "Word-Level Script Identification Using Texture Based Features." IJSDA vol.4, no.2 2015: pp.74-94. http://doi.org/10.4018/ijsda.2015040105

APA

Singh, P. K., Sarkar, R., & Nasipuri, M. (2015). Word-Level Script Identification Using Texture Based Features. International Journal of System Dynamics Applications (IJSDA), 4(2), 74-94. http://doi.org/10.4018/ijsda.2015040105

Chicago

Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "Word-Level Script Identification Using Texture Based Features," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 74-94. http://doi.org/10.4018/ijsda.2015040105

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Abstract

Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many post-processing steps such as automated document sorting, machine translation and searching of text written in a particular script in multilingual environment. For automatic processing of such documents through Optical Character Recognition (OCR) software, it is necessary to identify different script words of the documents before feeding them to the OCR of individual scripts. In this paper, a robust word-level handwritten script identification technique has been proposed using texture based features to identify the words written in any of the seven popular scripts namely, Bangla, Devanagari, Gurumukhi, Malayalam, Oriya, Telugu, and Roman. The texture based features comprise of a combination of Histograms of Oriented Gradients (HOG) and Moment invariants. The technique has been tested on 7000 handwritten text words in which each script contributes 1000 words. Based on the identification accuracies and statistical significance testing of seven well-known classifiers, Multi-Layer Perceptron (MLP) has been chosen as the final classifier which is then tested comprehensively using different folds and with different epoch sizes. The overall accuracy of the system is found to be 94.7% using 5-fold cross validation scheme, which is quite impressive considering the complexities and shape variations of the said scripts. This is an extended version of the paper described in (Singh et al., 2014).

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