“Hands On” Visual Recognition for Visually Impaired Users

Blind or visually impaired (BVI) individuals are capable of identifying an object in their hands by combining the available visual cues (if available) with manipulation. It is harder for them to associate the object with a specific brand, a model, or a type. Starting from this observation, we propose a collaborative system designed to deliver visual feedback automatically and to help the user filling this semantic gap. Our visual recognition module is implemented by means of an image retrieval procedure that provides real-time feedback, performs the computation locally on the device, and is scalable to new categories and instances. We carry out a thorough experimental analysis of the visual recognition module, which includes a comparative analysis with the state of the art. We also present two different system implementations that we test with the help of BVI users to evaluate the technical soundness, the usability, and the effectiveness of the proposed concept.