Abstract: A thorough examination of the most recent cutting-edge technologies utilized to support live environments is provided in this survey report. The main technologies covered are Text-to-Speech (TTS) systems for auditory feedback; Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for object detection and live scene description; real-time processing techniques for continuous feedback; and Optical Character Recognition (OCR) for live text recognition. The significance of user-friendly interfaces in improving user experience and the relevance of different datasets in training these algorithms are also covered in the study. The purpose of this survey is to present a thorough summary of the body of literature, address the benefits and drawbacks of the approaches used now, and make recommendations for possible future research avenues. This work is an important tool for scholars who want to explore and improve existing technologies in live environment assistance systems.
Keywords: Object Detection, Optical Character Recognition, Convolutional Neural Network, Text-to-Speech, Computer Vision, Neural Networks.
Works Cited:
Prof. M. P. Shinde, Shreyash Dhurupe, Viraj Karanjavane, Sanna Shaikh, Abhishek Suryawanshi " A Comprehensive Survey on the Current State of the Art Technologies used for Live Environment Assistance ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 11, pp. 23-27, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.121103
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DOI:
10.17148/IJARCCE.2023.121103
