Offline handwritten pre-segmented character recognition of Gurmukhi script

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Published:
Dec 20, 2016
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Vol. 25 No. 1/4 (2016)
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Original research papers
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Munish Kumar
Maharaja Ranjit Singh Punjab Technical University, Department of Computer Applications, Bathinda, India
Manish Jindal
Panjab University Regional Centre, Department of Computer Science & Applications, Muktsar, India
Simpel Jindal
Yadavindra College of Engineering, Talwandi Sabo, Computer Science & Engineering Section, Bathinda, India
Rajendra Sharma
Thapar University, Department of Computer Science & Engineering, Patiala, India

DOI: https://doi.org/10.22630/MGV.2016.25.1.5
Keywords : feature extraction, classification, PCA, k-NN, SVM, MLP
Abstract
In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offline handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classifiers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed.

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How to Cite
Kumar, M., Jindal, M., Jindal, S., & Sharma, R. (2016). Offline handwritten pre-segmented character recognition of Gurmukhi script. Machine Graphics and Vision, 25(1/4), 45–55. https://doi.org/10.22630/MGV.2016.25.1.5
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