International Research Journal of Engineering and Technology (IRJET) Volume: 13 Issue: 02 | Feb 2026
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Case-Based Reasoning for Vision Friend Sanika Pramod Shetti¹, Janhavi Ganesh Bhise², Anuja Dnyandev Suryawanshi³, Neha Yuvraj Powar4 1Student, Computer Science and Engineering, Dr. D. Y. Patil Polytechnic, Kolhapur, India 2Student, Computer Science and Engineering, Dr. D. Y. Patil Polytechnic, Kolhapur, India 3Student, Computer Science and Engineering, Dr. D. Y. Patil Polytechnic, Kolhapur, India 4Student, Computer Science and Engineering, Dr. D. Y. Patil Polytechnic, Kolhapur, India
--------------------------------------------------------------------***-------------------------------------------------------------------------------simultaneously, the system delivers fast, accurate, and Abstract: In the modern digital era, visually impaired personalized assistance. Unrecognized scenarios are stored and refined through adaptive learning, enabling continuous improvement. This approach significantly reduces response time, minimizes external dependency, and improves overall system efficiency and user satisfaction.
users encounter multiple challenges in performing daily activities independently. Tasks such as navigation, object identification, reading printed text, and recognizing people often require external assistance. Traditional assistive tools like white canes, basic voice assistants, or manual help are limited in intelligence, adaptability, and real-time responsiveness. This project proposes Vision Friend, an intelligent AI-powered mobile assistance system that integrates computer vision, speech recognition, natural language processing (NLP), and text-to-speech technologies to provide real-time, context-aware support. The system processes live camera feeds and voice inputs to detect objects, recognize faces, extract text from images, and deliver immediate audio feedback. Additionally, Vision Friend incorporates adaptive learning to improve accuracy over time by learning from repeated user interactions. The proposed solution reduces dependency on human assistance, enhances user confidence and independence, and offers a scalable, efficient, and practical accessibility solution.
2. LITERATURE SURVEY Several studies have contributed to the development of assistive technologies using AI and computer vision: Object detection models like YOLO enable real-time detection with high speed and accuracy, making them suitable for mobile applications. FaceNet and CNN-based face recognition systems provide reliable identification of known individuals, enhancing social interaction for visually impaired users. Deep learning-based speech recognition systems such as Deep Speech enable hands-free interaction and reduce user effort.
Keywords: Object Detection, Face Recognition, Speech Recognition, Text OCR, Computer Vision, Mobile Assistance System, AI-Powered Support, Accessibility Tools.
OCR engines like Tesseract allow extraction of text from images, making printed content accessible through audio output.
1. INTRODUCTION The rapid growth of smartphones and AI technologies has opened new possibilities for assistive systems aimed at improving the quality of life for visually impaired individuals. Despite these advancements, many existing solutions fail to provide comprehensive real-time assistance in dynamic environments. Users often struggle with identifying nearby objects, reading signboards or documents, recognizing familiar faces, and navigating unfamiliar surroundings. Traditional assistive systems are mostly audio-based or rely on physical tools that offer limited functionality and adaptability. Such systems lack intelligence to understand context, handle complex environments, or learn from user behavior. To overcome these limitations, intelligent assistive systems that combine multiple AI techniques are essential. Vision Friend is designed as an intelligent mobile-based solution that integrates object detection, face recognition, speech recognition, and OCR into a single application. By processing visual and auditory inputs
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Impact Factor value: 8.315
Research on adaptive learning and similarity measures highlights the importance of systems that improve over time by learning from user interactions. These studies collectively indicate that a multi-modal AIbased system can significantly enhance accessibility when integrated efficiently into mobile platforms..
3. PROPOSED SYSTEM The proposed system, Vision Friend, is an intelligent mobile assistance application that provides real-time support to visually impaired users. The system operates through voice commands or camera activation and processes inputs using multiple AI modules.
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