Abstract
Various Optical Character Recognition (OCR) methods, especially, machine learning models, work towards the solution of recognizing patterns in intelligent ways from data that is originally not available in digital format. These patterns are converted into data that a machine can recognize (by the proper algorithm) and can further manipulate for various manipulations. The basic characteristics of the implementation presented here are based on a balance between the complexity of the algorithm applied and the highest precision that can be obtained. In this paper, we attempt to recognize precise patterns from a set of handwritten characters without making the implementation intractably complex. Specifically, in the context of the Urdu language spoken and written by more than a hundred million people around the world, very little exploration has been carried out. This paper proposes the application of an established machine learning model, namely, using the Support Vector Machine (SVM) algorithm; and investigates the efficiency of its application for recognizing handwritten characters of the Urdu language, a subject that has never been investigated before.
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This research was supported by the National Research, Development, and Innovation Office (Hungary), grant nr. K124055.
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Zargar, H., Almahasneh, R., Kóczy, L.T. (2022). Automatic Recognition of Handwritten Urdu Characters. In: Harmati, I.Á., Kóczy, L.T., Medina, J., Ramírez-Poussa, E. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems 3. Studies in Computational Intelligence, vol 959. Springer, Cham. https://doi.org/10.1007/978-3-030-74970-5_19
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