Comparative Analysis of Language Translation and Detection System Using Machine Learning

Words are the meaty component which can be expressed through speech, writing or signals. It is important that the actual message or meaning of the words sent must conveys the same meaning to the one receives. The evolution from manual language translator to the digital machine translation have helped us a lot for finding the exact meaning such that each word must give at least close to exact actual meaning. To make machine translator more human-friendly feeling, natural language processing (NLP) with machine learning (ML) can make the best combination. The main challenges in machine translated sentence can involve ambiguities, lexical divergence, syntactic, lexical mismatches, semantic issues, etc. which can be seen in grammar, spellings, punctuations, spaces, etc. After analysis on different algorithms, we have implemented a two different machine translator using two different Long Short-Term Memory (LSTM) approaches and performed the comparative study of the quality of the translated text based on their respective accuracy. We have used two different training approaches of encodingdecoding techniques using same datasets, which translates the source English text to the target Hindi text. To detect the text entered is English or Hindi language, we have used Sequential LSTM training model for which the analysis has been performed based on its accuracy. As the result, the first LSTM trained model is 84% accurate and the second LSTM trained model is 71% accurate in its translation from English to Hindi text, while the detection LSTM trained model is 78% accurate in detecting English text and 81% accurate in detecting Hindi text. This study has helped us to analyze the appropriate machine translation based on its accuracy. Keywords: Accuracy, Decoding, Machine Learning (ML), Detection System, Encoding, Long Short-Term Memory (LSTM), Machine Translation, Natural Language Processing (NLP), Sequential