Date of Completion

2020

Document Type

Honors College Thesis

Department

CEMS

Thesis Type

Honors College

First Advisor

James Eddy

Second Advisor

Davis Darais

Keywords

Computer-Vision ASL Machine-learning Object-Detection

Abstract

Over the past several years there have been incredible advancements in machine learning and object detection. They’re now being used in everything from security systems to self-driving cars, to automated sorting facilities. One area that has had little benefit from the advancements in breaking down the communication barriers between the Deaf and those who are unable to understand sign language. This paper examines one attempt to start to address those barriers and how that attempt took an unexpected turn that looked deeper at the differences and challenges among various object detection algorithms, how computing power affects how fast and efficiently code can run, and how difficult it can be to work with people.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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