A Survey on Cloud Based Document Translation

Abstract: The increasing globalization of education has led to a rising demand for scalable and efficient cloud-based translation solutions for academic materials. This survey paper explores the development of DocuLingo, an AI-powered document translation system leveraging AWS cloud services, particularly AWS Translate, to enhance accessibility in education. The study investigates the limitations of generic translation tools in handling domain-specific academic terminologies and proposes cloud-based customization strategies to improve translation accuracy. The primary objective is to evaluate how cloud-native AI translation can optimize academic content processing while maintaining cost-efficiency and scalability. Additionally, instead of building a standalone translation model, we assess methods to fine-tune and optimize existing cloud-native AI solutions for educational and technical documents. This survey highlights how cloud-native AI translation, specifically AWS Translate, can be optimized for academic use, ensuring higher accuracy and accessibility. By enhancing existing cloud-based AI models, we demonstrate how institutions can leverage scalable and cost-efficient translation solutions to break language barriers in research and education.

Keywords: Cloud Computing, AI-Powered Translation, Neural Machine Translation (NMT), AWS Translate, Academic Content Processing, Domain-Specific Translation, Language Accessibility, Cost-Efficiency, Scalability, Educational Technology., Employability.