Online Handwritten Gujarati Numeral Recognition Using Support Vector Machine - Naik, VA; Desai, AA (International Journal of Computer Sciences and Engineering, 2025)
In this paper, online handwritten numeral recognition for Gujarati is proposed. Online handwritten character recognition is in trend for research due to a rapid growth of handheld devices. The authors have compared Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. The authors have used hybrid feature set. The authors have used zoning and chain code directional features which are extracted from each stroke. The dataset of the system is of 2000 samples and was collected by 200 writers and tested by 50 writers. The authors have achieved an accuracy of 92.60%, 95%, and 93.80% for linear, polynomial, RBF kernel and an average processing time of 0.13 seconds, 0.15seconds, and 0.18 seconds per stroke for linear, polynomial, RBF kernel.