Dash K.S.Puhan N.B.Panda G.2025-02-1720146http://dx.doi.org/1109/SPCOM.2014.6983924https://idr.iitbbs.ac.in/handle/2008/537Feature extraction is an important stage which decides the accuracy of any character recognition system. The state-of-the-Art feature extraction can be categorized to be either spatial domain based, transform domain based or a hybrid combination of both. We propose a new feature extraction method based on the non-redundant Stockwell Transform (ST), which takes care of the redundancy as well as computational complexity of original ST. We applied the proposed method on Odia numerals with k-Nearest Neighbor (k-NN) classifier, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) classifier and Modified Quadratic Discriminant Function (MQDF) classifier. The highest recognition accuracy is found to be 98.80% for the Odia numeral database, which outperforms the previous reported classification results. � 2014 IEEE.enclassificationFeature extractionOptical character recognitionStockwell transformConference Paper