Authors :
Ankit Kumar
Volume/Issue :
Volume 10 - 2025, Issue 12 - December
Google Scholar :
https://tinyurl.com/3xh6rv2f
Scribd :
https://tinyurl.com/2yr7jdd9
DOI :
https://doi.org/10.38124/ijisrt/25dec226
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
The rapid expansion of global communication, digital education, and cross-cultural collaboration has created a
strong need for fast, accessible, and user-friendly translation tools. Traditional translation systems often require significant
computing resources or rely heavily on cloud-based solutions. This research focuses on the development of a lightweight,
client-side web-based language translator created using HTML, CSS, and JavaScript. The system is designed to support
real-time multilingual translation through an external translation API, providing simplicity and accessibility across devices
and platforms. This paper examines existing literature on machine translation approaches, including rule-based, statistical,
and neural machine translation systems, and evaluates how these technologies have influenced modern translation APIs. A
detailed explanation of the system’s structure, design methodology, implementation strategy, and performance evaluation
is presented.
The study demonstrates that a browser-based translator can meet the needs of everyday users without requiring a
backend server. Limitations such as API dependency, translation accuracy issues for low-resource languages, and the lack
of ofline support are discussed. Finally, the paper highlights potential improvements, such as integrating ofline AI models,
speech support, and enhanced user personalization.
References :
- Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate.
- Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need.
- Koehn, P. (2010). Statistical Machine Translation. Cambridge University Press.
- Google Cloud Translation API Documentation.
- LibreTranslate API Documentation.
- DeepL Translation System Reports.
The rapid expansion of global communication, digital education, and cross-cultural collaboration has created a
strong need for fast, accessible, and user-friendly translation tools. Traditional translation systems often require significant
computing resources or rely heavily on cloud-based solutions. This research focuses on the development of a lightweight,
client-side web-based language translator created using HTML, CSS, and JavaScript. The system is designed to support
real-time multilingual translation through an external translation API, providing simplicity and accessibility across devices
and platforms. This paper examines existing literature on machine translation approaches, including rule-based, statistical,
and neural machine translation systems, and evaluates how these technologies have influenced modern translation APIs. A
detailed explanation of the system’s structure, design methodology, implementation strategy, and performance evaluation
is presented.
The study demonstrates that a browser-based translator can meet the needs of everyday users without requiring a
backend server. Limitations such as API dependency, translation accuracy issues for low-resource languages, and the lack
of ofline support are discussed. Finally, the paper highlights potential improvements, such as integrating ofline AI models,
speech support, and enhanced user personalization.
