FSboard: Over 3 million characters of ASL fingerspelling collected via smartphones

Abstract

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 finger-spelling 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. Fin-gerspelling recognition is an incomplete solution that comprises only a small part of sign language translation, but it could provide some immediate benefit to Deaf/Hard of Hearing signers while 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 with on-device performance—but falls short of human performance measured at 2.2% CER.1