Languages help to unite the world socially, culturally and technologically. Different natives communicate in different languages; there is a tremendous requirement for inter-language information translation process to transfer and share information and ideas. Though Sanskrit is an ancient Indo-European language, a significant amount of work for processing the information is required to explore the full potential of this language to open vistas in computational linguistics and computer science domain. In this paper, we have proposed and presented the machine translation system for translating Sanskrit to the Hindi language. The developed technique uses linguistic features from rule-based feed to train neural machine translation system. The work is novel and applicable to any low-resource language with rich morphology. It is a generic system covering various domains with minimal human intervention. The performance analysis of work is performed on automatic and linguistic measures. The results show that proposed and developed approach outperforms earlier work for this language pair.

语言有助于在社会，文化和技术上团结世界。不同的本地人以不同的语言交流；跨语言信息翻译过程非常需要传输和共享信息和思想。尽管梵文是古老的印欧语系语言，但仍需要大量工作来处理信息，以探索这种语言在计算语言学和计算机科学领域中打开远景的全部潜力。在本文中，我们提出并提出了将梵语翻译成印地语的机器翻译系统。这项开发的技术使用了基于规则的提要的语言功能来训练神经机器翻译系统。该作品是新颖的，适用于任何形态丰富的低资源语言。它是一种通用系统，只需最少的人工干预即可涵盖各个领域。工作的绩效分析是通过自动和语言措施进行的。结果表明，提出和开发的方法优于该语言对的早期工作。