Offline Handwritten Devnagari Digit Recognition

This paper presents a study on the performance of transformed domain features in Devnagari digit recognition. In this research the recognition performance is measured from features obtained in direct pixel value, Fourier Transform, Discrete Cosine Transform, Gaussian Pyramid, Laplacian Pyramid, Wavelet Transform and Curvelet Transform using classification schemes: Feed Forward, Function Fitting, Pattern Recognition, Cascade Neural Networks and K-Nearest Neighbor (KNN). The Gaussian Pyramid based feature with KNN classifier yielded the best accuracy of 96.93% on the test set. The recognition accuracy was increased to 98.02% by using a majority voting classification scheme at expense of 0.26 % rejection rate. The majority voting classifiers are based on features: Gaussian pyramid, Laplacian pyramid, wavelet pyramid and direct pixel value using KNN classifiers.