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Pashto Characters Recognition Using Multi-Class Enabled Support Vector Machine

Computers, Materials & Continua 2021年第6期67卷 2831-2844页

作者： Sulaiman Khan Shah Nazir Habib Ullah Khan Anwar Hussain Department of Computer Science University of SwabiSwabiPakistan Department of Accounting and Information Systems College of Business and EconomicsQatar UniversityDohaQatar

During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian *** unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research *** slight change in the Pashto characters’shape is an additional challenge for *** paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction *** feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature *** research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto *** results are generated for the proposed model based on a manifold and hybrid feature *** overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps *** of the proposed model is also tested by comparing its results with a convolution neural network *** convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector *** highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.

关键词： Pashto multi-class support vector machine handwritten characters database zoning and histogram of oriented gradients

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