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Language transliteration is one of the important area in natural language processing. Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes. The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible. We have developed hybrid (statistical +rules) approach based transliteration system of person names; from a person name written in Punjabi (Gurumukhi Script), the system produces its English (Roman Script) transliteration. Experiments have shown that the performance is sufficiently high. The overall accuracy of system comes out to be 95.23%.

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In this paper we present a multimodal biometric system based on ECG and Palmprint biometrics. In the proposed system we used MFCC approach in order to extract features of ECG biometric and PCA due to extract features of Palmprint. After feature extraction as illustrated by the portions given in this document.

Today, Biometric systems are considered superior in technological developments, because they provide a nontransferable means of identifying people not just cards or badges. The image enhancement step is designed to reduce noise in this area. The key point about an identification method that is "nontransferable" means it cannot be given or lent to another individual so nobody can get around the system they personally have to go through the control point. The image enhancement before feature extraction system can be very efficient. In this paper a new method is proposed to raise the performance of an ear verification system, since at first, using hybrid denoising method, the noises removed from ear image and then the next step denoisy image is used for verification system. Experimental results in this study show that Gaussian noises well removed from the ear images and has acceptable affect on verification accuracy.