Printed Odia Numeral Recognition Using Stacked Autoencoder
Subhashree Satpathy, Ajit Ku. Nayak, Mamata Nayak, Srikanta Patnaik · Procedia Computer Science · 2019
Automatic recognition of both printed and handwritten characters is the most progressive research area since last few periods. The printed character recognition rate still desires concentration of researchers because of variations in shape, size and design of characters. Odia is the native language of Odisha. Less works have been accounted in Odia script classification. Complex printed character recognition constantly demands a better feature extraction method for such diversified characters. Purpose of this manuscript is to apply autoencoder technique considering a suitable classifier for Odia printed numeral recognition. Autoencoder proved to be a better method in performance for dimensionality reduction as well as classification of Odia numerals.