TITLE: ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition 

ABSTRACT: Sign languages are used as a primary language by approximately 70 million
D/deaf people world-wide. However, most communication technologies operate in
spoken and written languages, creating inequities in access. To help tackle
this problem, we release ASL Citizen, the first crowdsourced Isolated Sign
Language Recognition (ISLR) dataset, collected with consent and containing
83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of
environments. We propose that this dataset be used for sign language dictionary
retrieval for American Sign Language (ASL), where a user demonstrates a sign to
their webcam to retrieve matching signs from a dictionary. We show that
training supervised machine learning classifiers with our dataset advances the
state-of-the-art on metrics relevant for dictionary retrieval, achieving 63%
accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who
are not present in the training or validation sets. An accessible PDF of this
article is available at the following link:
https://aashakadesai.github.io/research/ASLCitizen_arxiv_updated.pdf
Sign languages are used as a primary language by approximately 70 million
D/deaf people world-wide. However, most communication technologies operate in
spoken and written languages, creating inequities in access. To help tackle
this problem, we release ASL Citizen, the first crowdsourced Isolated Sign
Language Recognition (ISLR) dataset, collected with consent and containing
83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of
environments. We propose that this dataset be used for sign language dictionary
retrieval for American Sign Language (ASL), where a user demonstrates a sign to
their webcam to retrieve matching signs from a dictionary. We show that
training supervised machine learning classifiers with our dataset advances the
state-of-the-art on metrics relevant for dictionary retrieval, achieving 63%
accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who
are not present in the training or validation sets. An accessible PDF of this
article is available at the following link:
https://aashakadesai.github.io/research/ASLCitizen_arxiv_updated.pdf
ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition
http://arxiv.org/abs/2304.05934v2