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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM35177

Language Translator Using Deep Learning Techniques

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM35177

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM35177

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

M

M.Sanjana

G

G.Sandeep

M

M.Sandeep

A

A.Sanjana

R

R.Sanjay

PCM

Preethi C M

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Abstract

In this deep learning project, we propose the development of a Language Translator using neural machine translation techniques. Leveraging the Google Translate API for language translation functionality, our project aims to create an intuitive graphical user interface (GUI) using Tkinter , a Python library for building interactive applications. The application allows users to input text in one language and select a target language for translation from a dropdown menu. Upon clicking the “Translate” button, the input text is processed using a deep learning-based translation model, providing instant translations in the desired language. The translation model is implemented using recurrent neural networks (RNNs) or transformer architectures, which have shown remarkable performance in natural language processing tasks. We also incorporate error handling mechanisms to ensure robustness and provide meaningful feedback to users in case of translation failures. Through this project, we aim to demonstrate the power and versatility of deep learning techniques in building practical and userfriendly language translation applications.

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Cite This Article

M.Sanjana, G.Sandeep, M.Sandeep, A.Sanjana, R.Sanjay, Preethi C M, "Language Translator Using Deep Learning Techniques," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM35177

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12/31/45442

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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