On Mobile Detection and Localization of Skewed Nutrition Facts Tables
Christopher Blay · Digital Commons - USU (Utah State University) · 2021
With about 3.6 million adults in the United States having visual impairment or blind- ness, assistive technology is essential to give these people grocery shopping independence. This thesis presents a new method to detect and localize nutrition facts tables (NFTs) on mobile devices more quickly and from less-ideal inputs than before. The method is a drop- in replacement for an existing NFT analysis pipeline and utilizes multiple image analysis methods which exploit various properties of standard NFTs.In testing, this method performs very well with no false-positives and 42% total recall. These results are ideal for real-world application where inputs are analyzed as quickly as possible. Additionally, this new method exposes many possibilities for future improvement.