DocumentCode :
3659626
Title :
Classification of handwritten Gujarati numerals
Author :
Archana N. Vyas;Mukesh M. Goswami
Author_Institution :
Information Technology Dept., Dharmsinh Desai University, Nadiad, Gujarat, India
fYear :
2015
Firstpage :
1231
Lastpage :
1237
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 × 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 Neural Network. 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.
Keywords :
"Feature extraction","Support vector machines","Discrete cosine transforms","Handwriting recognition","Artificial neural networks","Kernel","Accuracy"
Publisher :
ieee
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on
Print_ISBN :
978-1-4799-8790-0
Type :
conf
DOI :
10.1109/ICACCI.2015.7275781
Filename :
7275781
Link To Document :
بازگشت