Published Paper Details:

Abstract: This project aims to develop a robust system for automated language detection leveraging the power of Natural Language Processing (NLP) techniques. Language detection is a fundamental task with applications ranging from content filtering and information retrieval to multilingual user interfaces. Our approach involves the utilization of advanced machine learning algorithms and linguistic features to accurately identify the language of a given text. The system will employ a combination of statistical methods, such as n-gram analysis and frequency-based models, along with machine learning algorithms trained on diverse multilingual datasets. Pre-processing techniques will be applied to handle variations in spelling, grammar, and character encoding. Additionally, the model will be designed to efficiently handle short and noisy text inputs. The project's significance lies in its potential to enhance the efficiency of multilingual applications, improve content classification, and contribute to the development of more inclusive and accessible digital interfaces. The effectiveness of the proposed system will be evaluated through comprehensive testing on a diverse set of texts in various languages, ensuring its adaptability and accuracy.