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Multi-Lingual Translation using Image
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I6-1712718
Abstract
Multi-lingual translation using images has emerged as a powerful approach to bridge linguistic barriers in real-time communication. This paper presents a system that automatically extracts text from images and translates it into multiple target languages using a combination of Optical Character Recognition (OCR) and Neural Machine Translation (NMT) models. The proposed framework captures an input image, preprocesses it to enhance text visibility, and applies OCR to accurately detect and extract textual content across diverse scripts. A deep learning?based translation engine then converts the extracted text into user-selected languages while preserving contextual meaning. The system supports multiple languages, including English and various regional Indian languages, enabling seamless cross-lingual understanding. Experimental results demonstrate high accuracy in text detection and translation, even under challenging conditions such as noisy backgrounds, varying fonts, and low illumination. This work contributes to the development of intelligent, user-friendly translation tools suitable for education, tourism, document digitization, and assistive technologies.
How to cite this paper
@article{1712718,
author = {R Lohith, Sangamesh D Chidri, Yashwanth K, Ganeshan},
title = {Multi-Lingual Translation using Image},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {432-439},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712718.pdf},
abstract = {Multi-lingual translation using images has emerged as a powerful approach to bridge linguistic barriers in real-time communication. This paper presents a system that automatically extracts text from images and translates it into multiple target languages using a combination of Optical Character Recognition (OCR) and Neural Machine Translation (NMT) models. The proposed framework captures an input image, preprocesses it to enhance text visibility, and applies OCR to accurately detect and extract textual content across diverse scripts. A deep learning?based translation engine then converts the extracted text into user-selected languages while preserving contextual meaning. The system supports multiple languages, including English and various regional Indian languages, enabling seamless cross-lingual understanding. Experimental results demonstrate high accuracy in text detection and translation, even under challenging conditions such as noisy backgrounds, varying fonts, and low illumination. This work contributes to the development of intelligent, user-friendly translation tools suitable for education, tourism, document digitization, and assistive technologies.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712718}
}