Performance Comparison of Online Handwritten Telugu Character Recognition Using Various Local and Global Features

Srilakshmi Inuganti and R. Rajeshwararao ()
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Srilakshmi Inuganti: Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada 533003, Andhra Pradesh, India
R. Rajeshwararao: ��Department of Computer Science and Engineering, JNTU-GV College of Engineering, Vizianagaram 535003, Andhra Pradesh, India

International Journal of Innovation and Technology Management (IJITM), 2024, vol. 21, issue 08, 1-25

Abstract: As the text is written on a special digitizer or Personal Digital Assistance (PDA) in which a sensor picks up the pen-tip movements along with the pen-up/pen-down switching, its automatic conversion is performed in the online Handwriting Recognition (HR). There are several works related to the online recognition of Devanagari as well as Tamil scripts. Meanwhile, the online recognition works associated with other Indian languages, specifically Telugu, which is complex in its structure together with style, are very few. Our work emphasizes the development of an online handwritten Telugu character recognition system using dominant points with the combination of SVM and performance analysis of various other features. Three classifiers namely, SVM, K-NN and MLP are used to examine the performance of the feature vectors. The proposed research is verified with HP-Lab data available in the UNIPEN format.

Keywords: Online handwritten character recognition; dominant points; feature extraction; support vector machines; stroke recognition (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219877024400029

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