Navigational aids play a vital role in enhancing the mobility and independence of blind and visually impaired (VI) individuals. However, existing solutions often present challenges related to discomfort, complexity, and limited ability to provide detailed environmental awareness. To address these limitations, we introduce SnapStick, an innovative assistive technology designed to improve spatial perception and navigation. SnapStick integrates a Bluetooth-enabled smart cane, bone-conduction headphones, and a smartphone application powered by the Florence-2 Vision Language Model (VLM) to deliver real-time object recognition, text reading, bus route detection, and detailed scene descriptions. To assess the system’s effectiveness and user experience, eleven blind participants evaluated SnapStick, and usability was measured using the System Usability Scale (SUS). In addition to the 94% accuracy, the device received an SUS score of 84.7%, indicating high user satisfaction, ease of use, and comfort. Participants reported that SnapStick significantly improved their ability to navigate, recognize objects, identify text, and detect landmarks with greater confidence. The system’s ability to provide accurate and accessible auditory feedback proved essential for real-world applications, making it a practical and user-friendly solution. These findings highlight SnapStick’s potential to serve as an effective assistive device for blind individuals, enhancing autonomy, safety, and navigation capabilities in daily life. Future work will explore further refinements to optimize user experience and adaptability across different environments.

Vision is a critical component of daily life, and its loss significantly hinders an individual’s ability to navigate, particularly when using public transportation systems. To address this challenge, this paper introduces a novel approach for accurately identifying bus route numbers and destinations, designed to assist visually impaired individuals in navigating urban transit networks. Our system integrates object detection, image enhancement, and Optical Character Recognition (OCR) technologies to achieve reliable and precise recognition of bus information. We employ a custom-trained You Only Look Once version 8 (YOLOv8) model to isolate the front portion of buses as the region of interest (ROI), effectively eliminating irrelevant text and advertisements that often lead to errors. To further enhance accuracy, we utilize the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to improve image resolution, significantly boosting the confidence of the OCR process. Additionally, a post-processing step involving a pre-defined list of bus routes and the Levenshtein algorithm corrects potential errors in text recognition, ensuring reliable identification of bus numbers and destinations. Tested on a dataset of 120 images featuring diverse bus routes and challenging conditions such as poor lighting, reflections, and motion blur, our system achieved an accuracy rate of 95%. This performance surpasses existing methods and demonstrates the system’s potential for real-world application. By providing a robust and adaptable solution, our work aims to enhance public transit accessibility, empowering visually impaired individuals to navigate cities with greater independence and confidence.