Automated Multilingual Translation of Documents and Question Papers using Generative AI Techniques

Ms.S SHAHEEN 1
Assistant Professor, Department of AI&DSAnnamacharya Institute ofTechnology and Sciences, Tirupati – 517520, A.P.
shaheenshaalu@gmail.com
R SAI KISHAN VARMA 4
UG Scholar, Department ofAI&DSAnnamacharya Institute ofTechnology and Sciences, Tirupati – 517520, A.P.
saikishanvarma.5@gmail.com

P RAHUL REDDY 3
S YUGANDHAR KUMAR 2
UG Scholar, Department ofAI&DSAnnamacharya Institute ofTechnology and Sciences, Tirupati – 517520, A.P.
yugandharsirigiraju8@gmail.com
P BALA VAMSI KRISHNA 5
UG Scholar, Department ofAI&DSAnnamacharya Institute ofTechnology and Sciences,
Tirupati – 517520, A.P.krishnabalavamsi8@gmail.com
UG Scholar, Department ofAI&DS
Annamacharya Institute ofTechnology and Sciences, Tirupati – 517520, A.P.
rahulreddyreddy789@gmail.com

Abstract— The increased demand for multilingual education materials has posed a major challenge for institutions that require translating examination question papers in various regional languages. Traditional translation methods involve a significant amount of time and resources and may also introduce inconsistencies in the translation process, affecting the clarity of the examination process. This paper proposes an automated multilingual document translation system using Generative AI and Neural Machine Translation (NMT) approaches for the efficient translation of examination question papers in various languages. In the proposed system, the translation document can be in either PDF or DOCX formats. Text content is extracted using the pdfplumber and python-docx libraries for PDF,Text and Word documents, respectively. A Google Neural Machine Translation model is employed using the Deep Translator API for translation. This system is implemented using Python and the Django web development framework. This provides a simple and effective interface for uploading the document and generating the translation output. Observations made on the proposed system indicate the efficient translation of documents in various contexts, particularly in the field of education. This makes the proposed approach more suitable and effective in the field of education. Keywords: Document Translation, Generative AI, Neural Machine Translation, Natural Language Processing, Deep Translator API, Document Processing, Text Extraction, pdfplumber, python-docx, Django Framework,Educational Technology, Automated Question PaperTranslation.