  Progress in machine understanding of sign languages has been slow and
hampered by limited data. In this paper, we present FSboard, an American Sign
Language fingerspelling dataset situated in a mobile text entry use case,
collected from 147 paid and consenting Deaf signers using Pixel 4A selfie
cameras in a variety of environments. Fingerspelling recognition is an
incomplete solution that is only one small part of sign language translation,
but it could provide some immediate benefit to Deaf/Hard of Hearing signers as
more broadly capable technology develops. At >3 million characters in length
and >250 hours in duration, FSboard is the largest fingerspelling recognition
dataset to date by a factor of >10x. As a simple baseline, we finetune 30 Hz
MediaPipe Holistic landmark inputs into ByT5-Small and achieve 11.1% Character
Error Rate (CER) on a test set with unique phrases and signers. This quality
degrades gracefully when decreasing frame rate and excluding face/body
landmarks: plausible optimizations to help models run on device in real time.
