An Improved Online Tamil Character Recognition Engine Using Post-Processing Methods

We propose script-specific post processing schemes for improving the recognition rate of online Tamil characters. At the first level, features derived at each sample point of the preprocessed character are used to construct a subspace using the 2DPCA algorithm. Recognition of the test sample is performed using a nearest neighbor classifier. Based on the analysis of the confusion matrix, multiple pairs of confused characters are identified. At the second level, we use script specific cues to sort out the ambiguities among the confused characters. This strategy reduces the recognition error among the confused character sets handled, by more than 4%. This approach can be applied irrespective of the nature of the classifier used for the first level of recognition, though the nature of the confusion set might vary.