Date of Award:
5-2013
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Computer Science
Advisor/Chair:
Vladimir Kulyukin
Abstract
Visually impaired (VI) individuals struggle with grocery shopping and have to rely on either friends, family or grocery store associates for shopping. ShopMobile 2 is a proof-of-concept system that allows VI shoppers to shop independently in a grocery store using only their smartphone. Unlike other assistive shopping systems that use dedicated hardware, this system is a software only solution that relies on fast computer vision algorithms. It consists of three modules - an eyes free barcode scanner, an optical character recognition (OCR) module, and a tele-assistance module. The eyes-free barcode scanner allows VI shoppers to locate and retrieve products by scanning barcodes on shelves and on products. The OCR module allows shoppers to read nutrition facts on products and the tele-assistance module allows them to obtain help from sighted individuals at remote locations. This dissertation discusses, provides implementations of, and presents laboratory and real-world experiments related to all three modules.
Checksum
d921b1843134b17431f9a627eff7b00b
Recommended Citation
Kutiyanawala, Aliasgar, "Eyes-Free Vision-Based Scanning of Aligned Barcodes and Information Extraction from Aligned Nutrition Tables" (2013). All Graduate Theses and Dissertations. 1522.
https://digitalcommons.usu.edu/etd/1522
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