In this article, we aim at reducing the error rate of the Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary Support Vector Machine (SVM) classiﬁer. Motivated by the relatively the primary Support Vector Machine (SVM) classiﬁer. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants Secondly, a dynamic time warping method is proposed to automatically extract Secondly, a dynamic time warping method is proposed to automatically extract tures derived from these regions aid in reducing the degree of confusions. Thirdly statistics of speciﬁc features are proposed for resolving any confusions in vowel modiﬁers.
The reevaluation approaches are tested on two databases (a) the isolated Tamil symbols in the IWFHR test set, and (b) the symbols segmented from a set of 10000 Tamil words. The recognition rate of the isolated test symbols of the IWFHR database improves by 1.9%. For the word database, the incoration of the reevaluation step improves the symbol recognition rate by 3.5% (from 88.4% to 91.9 %) This, in turn boosts the word recognition rate by 11.9% (from 65.0% to 76.9%) The reduction in the word error rate has been achieved using a generic approach, without the incorporation of language models.