An Integrated Model for Text to Text, Image to Text and Audio to Text Linguistic Conversion using Machine Learning Approach

Abstract

This paper presents an integrated model that uses machine learning techniques to perform text-to-text, image-to-text, and audio-to-text conversions, with particularly focus on Indian languages. The proposed model which can translate text, image, and voice has been tested on large datasets of various Indian languages and utilizes state-of-the-art techniques such as machine learning, computer vision, and speech recognition to accurately transcribe and translate the input data. The results obtained from the experiments demonstrate the effectiveness of the model by accurately converting text, images, and audio to text, and the potential applications of our proposed model range from language learning, accessibility for non-verbal or non-hearing individuals to cross-language communication. The proposed model is intended to bridge the language gap and facilitate communication among people from different linguistic backgrounds.