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A Holistic Approach for Handwritten Hindi Word Recognition

Samir Malakar, Pankaj Sharma, Pankaj Kumar Singh, Maitrayee Das, Ram Sarkar, Mita Nasipuri

Source Title: International Journal of Computer Vision and Image Processing (IJCVIP)7(1)

ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522514398|DOI: 10.4018/IJCVIP.2017010104

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MLA

Malakar, Samir, et al. "A Holistic Approach for Handwritten Hindi Word Recognition." IJCVIP vol.7, no.1 2017: pp.59-78. http://doi.org/10.4018/IJCVIP.2017010104

APA

Malakar, S., Sharma, P., Singh, P. K., Das, M., Sarkar, R., & Nasipuri, M. (2017). A Holistic Approach for Handwritten Hindi Word Recognition. International Journal of Computer Vision and Image Processing (IJCVIP), 7(1), 59-78. http://doi.org/10.4018/IJCVIP.2017010104

Chicago

Malakar, Samir, et al. "A Holistic Approach for Handwritten Hindi Word Recognition," International Journal of Computer Vision and Image Processing (IJCVIP) 7, no.1: 59-78. http://doi.org/10.4018/IJCVIP.2017010104

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Abstract

Holistic word recognition attempts to recognize the entire word image as a single pattern. In general, it performs better than segmentation based word recognition model for known, fixed and small sized lexicon. The present work deals with recognition of handwritten words in Hindi in holistic way. Features like area, aspect ratio, density, pixel ratio, longest run, centroid and projection length are extracted either from entire word image or from the hypothetically generated sub-images of the same. An 89-elements feature vector has been designed to represent each word in the feature space and five different classifiers have been used for measuring recognition performances. Considering the complexities of Hindi characters, the technique shows an impressive result using a Multilayer Perceptron (MLP) based classifier. Moreover, the technique shows scale and rotation invariant nature to a significant extent.

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A Holistic Approach for Handwritten Hindi Word Recognition

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