Real Time Machine Translation System for English to Indian language

2020 ◽  
Author(s):  
Raj Vyas ◽  
Kirti Joshi ◽  
Hitesh Sutar ◽  
Tatwadarshi P. Nagarhalli
Keyword(s):  
Real Time ◽  
Machine Translation ◽  
Translation System ◽  
Time Machine ◽  
Indian Language ◽  
Machine Translation System

The Application and Study on Intelligent Real-Time Machine Translation Technology

2014 ◽  
Vol 687-691 ◽  
pp. 1695-1699
Author(s):  
Yan He ◽  
Shu Jin Wang
Keyword(s):  
Real Time ◽  
Machine Translation ◽  
Translation System ◽  
Correlation Technique ◽  
Time Machine ◽  
Source Language ◽  
Time Translation ◽  
Patent Documents ◽  
At Home

Based on the research on correlation technique at home and abroad, this thesis implements a term automatic intelligent real-time translation system in patent documents translation of English-Chinese. Through the analysis of noun terms in a large number of bilingualism corpuses, the source language terms need to be reordered firstly. And then the n-best candidates can be obtained after being translated by Moses. At last, the best result is chosen according to scoring the n-best result again.

Conversion of Higher into Lower Language Using Machine Translation

2017 ◽  
pp. 92-107
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Machine Translation ◽  
Central Government ◽  
Translation System ◽  
Indian Language ◽  
Vast Number ◽  
Indian People ◽  
Regional Language ◽  
Related Language ◽  
Machine Translation System ◽  
Free Open Source

This chapter addresses an exclusive approach to expand a machine translation system beginning higher language to lower language. Since we all know that population of India is 1.27 billion moreover there are more than 30 language and 2000 dialects used for communication of Indian people. India has 18 official recognized languages similar to Assamese, Bengali, English, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Malayalam, Manipuri, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Tamil, Telugu, and Urdu. Hindi is taken as regional language and is used for all types of official work in central government offices. Commencing such a vast number of people 80% of people know Hindi. Though Hindi is also regional language of Jabalpur, MP, India, still a lot of people of Jabalpur are unable to speak in Hindi. So for production those people unswerving to know Hindi language we expand a machine translation system. For growth of such a machine translation system, used apertium platform as it is free/open source. Using apertium platform a lot of language pairs more specifically Indian language pairs have already been developed. In this chapter, develop a machine translation system for strongly related language pair i.e Hindi to Jabalpuriya language (Jabalpur, MP, India).

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

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