Classification of handwritten Gujarati numerals

Abstract

This paper addresses the problem of recognizing handwritten numerals for Gujarati Language. Three methods are presented for feature extraction. One belongs to the spatial domain and other two belongs to the transform domain. In first technique, a new method has been proposed for spatial domain which is based on Freeman chain code. This method obtains the global direction by considering n x n neighbourhood and thus eliminates the noise which occurs due to local direction. In second and third method, 85 dimensional Fourier descriptors and Discrete Cosine Transform coefficients were computed and treated as feature vectors. Comparative analysis has been done for these three methods. These methods are tested with three different classifiers namely K-Nearest Neighbour, Support Vector Machine and Back Propagation NeuralNetwork. Experimental results were evaluated using 10 fold cross validation. The highest recognition rates obtained for full data set of 3000 digits are 85.67%, 93.60% and 93.00% using modified chain code, DFT and DCT respectively.

Online Handwritten Gujarati Character Recognition: Two-Layer Classification Approach

“…The system obtained an accuracy of 98.13%. In [10] the authors have compared and experiment with K-NN, SVM and Back Propagation ANN classifiers with different possible options. The feature set included spatial and transform domain features like freeman chain code, Fourier descriptors, discrete cosine transform coefficient.…”