Delineating Indic Transliteration: Developing A Robust Model for Accurate Cross-Script Conversion

The aim of this study was to develop a reliable model of text change between two writing systems. " नमस्ते" (meaning "Hello") can be transliterated into Latin alphabet as "namaste". To achieve this goal, it use open data with multiple parallel reading samples in both languages. The dataset includes source language terms and their translations in the target writing process. This data set plays an important role in training the transliteration model, as it contains linguistic structures and variables that make the model flexible. The transliteration model was developed using a hybrid sequencing approach combined with deep learning models. This method trains an LSTM-based model on a dataset with carefully selected loss functions. Other conceptual techniques and developments can further help the model to capture the complex relationship between the source and target languages. To test our transliteration model, we created a custom test data set. This test data set contains only new samples, so that the accuracy of the samples can be assessed in terms of text texts that are not present in the training data. We measure the accuracy of the model's text conversion using carefully calculated metrics. And for testing purposes, I installed this on my local host as well. This study demonstrates that open-source data sets can be successfully used to develop transliteration models. The excellent performance of the model in the testing process highlights the ability to transfer information between two writing systems. This research advances the field by providing a framework for building script translation models using publicly available data, which can enhance natural language processing and facilitate cross-language communication in language and writing system.