Combining Self-Organizing Maps and Radial Basis Function Networks for Tamil handwritten Character Recognition

An exceptional effort has been extended in making a computer recognize both typed and handwritten characters automatically. Quite recent still today, the characters of English Language is been the main explore area for our researchers. Tamil under Asian languages has little or no attention is been given. The challenges posed by Indian languages are different from English. In addition, there has been very little research on machine recognition of Indian scripts. Consequently, exhaustive experimentation is necessary in order to get a good insight into the script from machine recognition point of view. Methods currently widely used for character recognition for these languages are mainly those which involve pattern matching using image processing techniques. One limitation of such is their inability to respond to variations. In this paper, we report the results of recognition of handwritten Tamil characters. We experimented with two different approaches. One is SOM based method wherein the interactions between the features in the classification are done using unsupervised learning. In the second approach, a combination of RBF and SOM has been taken to investigate its dynamic training principles in our classification network. The classification ability of RBF-SOM is compared to SOM Network. The comparison is based on the scanned database containing features extracted from preprocessing techniques. The assessment is in terms of average recognition accuracy and the number of training samples required in obtaining an acceptable performance. Here it also performs error analysis to determine the advisability of combining the classifiers. The conclusion obtained will support the recognizing progression in better approach.