• Sagar Sharma
• Saransh Aggrawal

Object detection is used in almost all real-world applications, such as autonomous navigation, visual systems, face recognition, and many more. Instead of using object detection techniques, it aids in identifying an image's contrasting features and generates a knowledgeable and competent comprehension, much like how human vision functions. This study begins with a succinct introduction to deep learning, which has produced noteworthy performance as a result of these visual representations. This clarifies the function of deep learning on convolutional neural networks (RCNNs) for object detection algorithms that assist the blind by identifying objects by name. EYEMATE's primary role is to record live visual input utilizing a smartphone or portable camera. A complex object detection algorithm then processes this input. This algorithm is capable of identifying a broad variety of items that are frequently seen in daily life, including obstacles, traffic signs, products on store shelves, and more. Modern machine learning techniques are used by the system to increase its accuracy and extend its object detection capabilities. EYEMATE incorporates a natural language processing component that allows users to give voice commands in order to promote smooth interaction. Users can ask the system to describe their surroundings, identify specific things, or offer assistance with activities like navigating unfamiliar places by speaking commands to it. Users are guaranteed timely and pertinent information by means of synthesized speech or haptic feedback as the system's reaction

Object Detection, RCNN, Neural Network, Deep Learning

"EYEMATE – Object Detection With Voice Commands for Visually Impaired", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.c729-c736, April-2024, Available :http://www.jetir.org/papers/JETIR2404280.pdf

"EYEMATE – Object Detection With Voice Commands for Visually Impaired", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppc729-c736, April-2024, Available at : http://www.jetir.org/papers/JETIR2404280.pdf