A Virtual Assistance for Visually Impaired People to Recognize Fabric Pattern and Color Using Human Computer Interaction Logic

Modern advancements in technology have paved the way for innovative solutions to assist visually impaired individuals in their daily lives. This study presents a virtual assistance system designed to recognize fabric patterns and colors using a combination of deep learning and human-computer interaction techniques. The system employs high-quality image acquisition methods, bilateral filtering, and histogram equalization to preprocess images, enhancing their quality for subsequent analysis. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are integrated to form a Hybrid Fabric Pattern Recognition Network (HFPRN) that accurately identifies fabric patterns. Additionally, the system utilizes RGB color space conversion for precise color detection. Voice command capabilities via Amazon Alexa and tactile feedback mechanisms further enhance user interaction. Experimental results demonstrate the proposed system's high accuracy, with the HFPRN achieving 97% accuracy in fabric pattern recognition and 95% accuracy in color detection. This comprehensive solution significantly improves the autonomy and quality of life for visually impaired individuals by providing real-time, accurate feedback on fabric patterns and colors.