Abstract: The "Vision For Blind Using YOLOv8 Algorithm" project is an innovative approach designed to enhance the autonomy and accessibility of visually impaired individuals. In India, where a significant portion of the population faces visual impairment, daily tasks such as currency identification and reading printed materials pose considerable challenges. This project addresses these issues through an Android application that leverages advanced deep learning techniques to provide multiple functionalities. The app employs the YOLOv8 algorithm for accurate and real-time detection of currency notes, enabling users to identify denominations through simple gestures. Additionally, the app includes features for summing the total value of multiple notes, detecting counterfeit currency using a Convolutional Neural Network (CNN), and converting printed text to speech for accessibility. This comprehensive tool not only empowers visually impaired individuals to manage their finances more independently but also offers them a means to access written information. The project's development involved meticulous planning, including data collection, model training, and extensive testing to ensure high accuracy and reliability. The use of intuitive swipe gestures makes the app user-friendly and accessible, enhancing its practical utility. By addressing critical daily challenges, this project represents a significant advancement in inclusive technology, providing visually impaired individuals with greater independence and confidence in their daily activities
Keywords: Machine learning, deep learning, NLP, you only look once, Convolution neural network Mobile, Android.
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DOI:
10.17148/IJARCCE.2024.13814
