A Raspberry Pi-Based Text Reader & Object Detection System

This research introduces a novel application of the raspberry pi platform in the development of an integrated text reader and object detection system. The system aims to assist visually impaired individuals and enhance overall accessibility for users with diverse needs. Leveraging the power of computer vision and edge computing, the proposed solution employs a raspberry pi, a low-cost, compact, and energy-efficient single-board computer. The text reader component utilizes optical character recognition (ocr) algorithms to convert printed or handwritten text into digital format. This feature enables users to obtain audible information from text-based sources, such as books, documents, or signage. The implementation of real-time processing on the raspberry pi ensures prompt and on-device text recognition, reducing dependence on external servers and enhancing privacy. Furthermore, the system incorporates an object detection module to identify and describe objects in the user's surroundings. This functionality enhances the user's spatial awareness by providing audio cues about the presence and characteristics of objects. The object detection system employs a pre-trained deep neural network, making it adaptable to various object recognition tasks. The entire system is designed with a user-friendly interface that facilitates interaction through speech and audio feedback. Additionally, the portability and affordability of the raspberry pi make the solution accessible to a broad user base. Preliminary testing of the prototype has shown promising results in terms of accuracy, speed, and usability. The raspberry pi-based text reader & object detection system holds potential for improving the quality of life for individuals with visual impairments and can contribute to the advancement of assistive technologies with its cost-effective and scalable approach. Future work involves refining the system, expanding the object detection capabilities, and conducting extensive user trials to gather feedback for further improvements.