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Research Paper | Computer Science & Engineering | India | Volume 3 Issue 6, June 2014

Tamil Sign Language to Speech Translation

S. Sudha ^[6] | S. Jothilakshmi | R. Rajasoundramani

Abstract: Sign Language Recognition is one of the most growing fields of research today. Most researches on hand gesture recognition for HCI rely on either Artificial Neural Networks (ANN) or Hidden Markov Model (HMM). There are many effective algorithms for segmentation; classification; pattern matching and recognition. The main goal of this paper is to compare the classifiers for translating Tamil sign language to speech; which will definitely help the researchers to attain an optimal solution. The most important thing in hand gesture recognition system is the input features and the selection of classifiers. To increase the recognition rate and make the recognition system resilient to view-point variations; the concept of shape descriptors from the available feature set is introduced. K-Nearest Neighbor (KNN) ; Proximal Support Vector Machine (PSVM) and Nave Bayesian are used as classifiers to recognize static Tamil words. The performance analysis of the proposed approach is presented along with the experimental results. Comparative analysis of these methods with other popular techniques shows that the real time efficiency and robustness are better. Experimental results demonstrate the effectiveness of the proposed work for recognizing efficiency 78 % for KNN classifier; 91 % for PSVM classifier and 93 % for Nave Bayesian classifier.

Keywords: Artificial Neural Networks ANN, Hidden Markov Model HMM, K-Nearest Neighbor KNN, Nave Bayes, Proximal Support Vector PSVM

Edition: Volume 3 Issue 6, June 2014,

Pages: 2823 - 2828

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