@inproceedings{10.1145/3597638.3614497,
author = {Bohacek, Matyas and Hassan, Saad},
title = {Sign Spotter: Design and Initial Evaluation of an Automatic Video-Based American Sign Language Dictionary System},
year = {2023},
isbn = {9798400702204},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3597638.3614497},
doi = {10.1145/3597638.3614497},
abstract = {Searching unfamiliar American Sign Language (ASL) words in a dictionary is challenging for learners, as it involves recalling signs from memory and providing specific linguistic details. Fortunately, the emergence of sign-recognition technology will soon enable users to search by submitting a video of themselves performing the word. Although previous research has independently addressed algorithmic enhancements and design aspects of ASL dictionaries, there has been limited effort to integrate both. This paper presents the design of an end-to-end sign language dictionary system, incorporating design recommendations from recent human–computer interaction (HCI) research. Additionally, we share preliminary findings from an interview-based user study with four ASL learners.},
booktitle = {Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility},
articleno = {92},
numpages = {5},
keywords = {American Sign Language (ASL), Dictionary, Search Evaluation, Search Interfaces, Search System Design, Sign Languages, User Satisfaction, Video Search},
location = {New York, NY, USA},
series = {ASSETS '23}
}