Development of a Multilingual Real-Time Translator Using NLP, OCR, and Deep Learning

Development of a Multilingual Real-Time Translator Using NLP, OCR, and Deep Learning

Shubham Patil1, Sneha Kshirsagar2, Bhavesh Sharma3, Prof. Prachi Hinge4

1,2,3,Department of Computer Engineering, Siddhant College of Engineering

4 Professor of Department of Computer Engineering, Siddhant College of Engineering

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Abstract
This study proposes a comprehensive Real-Time Language Translator incorporating four primary modules: speech/text translation, OCR-based image text recognition, video translation, and sign language recognition. It leverages cutting-edge techniques in Natural Language Processing (NLP), Optical Character Recognition (OCR), and Deep Learning to facilitate communication across multilingual and multimodal inputs. Each module in the system is developed using specialized technologies tailored to its functionality, including automatic speech recognition (ASR), neural machine translation (NMT), text- to-speech (TTS) synthesis, and computer vision-based gesture recognition frameworks. This paper presents the architectural design, operational flow, and implementation strategies of the proposed system, along with practical evaluations and performance metrics for each module.

Keywords: Natural Language Processing, Optical Character Recognition, Speech Recognition, Sign Language Detection, Language Translation, Deep Learning.