• Ashwin Udmale
• Vaibhav Wandhekar
• Bhushan Gunjal
• Abhishek Vyapari
• Prof. Shraddha Rachcha

The visually impaired community faces unique challenges in perceiving and interacting with the physical world, relying heavily on touch and auditory cues for navigation. This paper presents a novel solution that harnesses the power of deep learning for real-time object detection and speech synthesis to bridge the accessibility gap for the blind. Our system captures and processes images from a camera, identifies objects within the images, and conveys the results audibly to the user. By providing auditory feedback, this technology enhances the user's awareness of their surroundings, enabling them to independently navigate, shop, use public transportation, and gain environmental awareness. We explore the architecture, design, and methodologies of the system, underscoring its potential to transform the lives of visually impaired individuals and foster inclusivity. This research not only serves as a critical step towards a more accessible and inclusive world but also highlights the power of technology to empower and improve the quality of life for those with visual impairments.

object detection, blind, visually impaired, speech output, accessibility, deep learning, real-time, technology, inclusivity, auditory feedback, independence, visual impairment, assistive technology, navigation, deep neural networks, deep learning models.

"Enhancing Accessibility for the Visually Impaired: Real-time Object Detection with Speech Output", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.b418-b422, November-2023, Available :http://www.jetir.org/papers/JETIR2311150.pdf

"Enhancing Accessibility for the Visually Impaired: Real-time Object Detection with Speech Output", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppb418-b422, November-2023, Available at : http://www.jetir.org/papers/JETIR2311150.pdf