AI Assistive Tutor for Visually Impaired Students Using OCR, Voice Assistance and Object Detection
AI Assistive Tutor for Visually Impaired Students Using OCR, Voice Assistance and Object Detection
Authors:
Dr Surekha Byakod1, Ranjitha S Shetty2, Mohammed Sinan3, Bhumika L4, Manavi5
1Dr Surekha Byakod, Department of Computer Science and Design, K.S. Institute of Technology
2Ranjitha S Shetty, Department of Computer Science and Design, K.S. Institute of Technology
3Mohammed Sinan, Department of Computer Science and Design, K.S. Institute of Technology
4Bhumika L, Department of Computer Science and Design, K.S. Institute of Technology
5Manavi, Department of Computer Science and Design, K.S. Institute of Technology
Abstract - Visual impairment makes it difficult for individuals to access educational materials, recognize surrounding objects, and interact independently with digital systems. Recent advancements in Artificial Intelligence (AI), computer vision, Optical Character Recognition (OCR), object detection, and speech technologies have led to the development of intelligent assistive systems for visually impaired users. This review paper presents an analysis of various AI-based assistive technologies including YOLO, SSD, and Faster R-CNN object detection models, OCR-based text recognition systems, voice-assisted interfaces, and Digital Twin concepts for contextual support. The reviewed studies show that object detection improves environmental awareness, OCR systems convert printed text into speech, and voice-based interfaces enable easier interaction. The paper also discusses Raspberry Pi-based systems, TensorFlow frameworks, and deep learning approaches used in accessibility applications. In addition, the study identifies limitations in existing systems such as lack of integration, limited personalization, and insufficient educational support. Finally, the paper highlights the need for integrated AI Assistive Tutor systems that combine object detection, OCR, voice interaction, and contextual assistance for visually impaired students.
Key Words: Artificial Intelligence, Object Detection, OCR, Voice Assistance, Digital Twin, Visually Impaired Users.