Tejinder Singh Saini

A Transliteration Based Word Segmentation System for Shahmukhi Script

Word Segmentation is an important prerequisite for almost all Natural Language Processing (NLP) applications. Since word is a fundamental unit of any language, almost every NLP system first needs to segment input text into a sequence of words before further processing. In this paper, Shahmukhi word segmentation has been discussed in detail. The presented word segmentation module is part of Shahmukhi-Gurmukhi transliteration system. Shahmukhi script is usually written without short vowels leading to ambiguity. Therefore, we have designed a novel approach for Shahmukhi word segmentation in which we used target Gurmukhi script lexical resources instead of Shahmukhi resources. We employ a combination of techniques to investigate an effective algorithm by applying syntactical analysis process using Shahmukhi Gurmukhi dictionary, writing system rules and statistical methods based on n-grams models.

Urdu to Hindi and Reverse Transliteration System

In spoken form Hindi and Urdu are mutually comprehensible languages but they are written in mutually incomprehensible scripts. This research work aims to bring the Urdu and Hindi speaking people closer by developing a transliteration tool for the two languages. Even though the language is same, but still developing a high accuracy transliteration system is not a trivial job. The accuracies of statistical and rule based Urdu-Hindi and reverse transliteration systems are 97.12% and 99.46% at word level.