ReCog: Supporting Blind People in Recognizing Personal Objects

Abstract

We present ReCog, a mobile app that enables blind users to recognize objects by training a deep network with their own photos of such objects. This functionality is useful to differentiate personal objects, which cannot be recognized with pre-trained recognizers and may lack distinguishing tactile features. To ensure that the objects are well-framed in the captured photos, ReCog integrates a camera-aiming guidance that tracks target objects and instructs the user through verbal and sonification feedback to appropriately frame them. We report a two-session study with 10 blind participants using ReCog for object training and recognition, with and without guidance. We show that ReCog enables blind users to train and recognize their personal objects, and that camera-aiming guidance helps novice users to increase their confidence, achieve better accuracy, and learn strategies to capture better photos.