Enhancing Cross-Language Communication in Chatting Applications through Real-Time Translation: Algorithms, User Experience, and Practical Implementation.

Abstract

This paper explores the integration of machine learning techniques into social media platforms to facilitate real-time language translation within chat applications. Most people use social networking sites for their daily activities. Approximately 7,000 languages are spoken in the world today. Even so, less than a thousand people are fluent in about 2000 of these languages. People who travel to different cities or nations struggle with language barriers; even hotel signboards and menus contain regional languages. To assist with this issue, our program is quite useful and simple to use. We've done translations into almost all languages. Social networking involves using both text and graphics. We have successfully processed the image to perform expressions as well as face detection. With the help of our image processing, people may also search the internet using hashtags to organize data. We can safeguard the picture you give us or advise you about any authorized use among the billions of photographs available on the internet. We are also able to get landmark information with the same picture processing. With Google Cloud Platform API and the machine learning of Google, we were able to extract all these properties. The application is built in a hybrid environment with the Ionic Framework 3.6.0, which includes Angular 1.3 and Node.JS is used for the backend, with Firebase (MongoDB) for the database.