A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is
End-to-End Convolutional Sequence Learning for ASL Fingerspelling Recognition
Although fingerspelling is an often overlooked component of sign languages, it has great practical value in the communication of important context words that lack dedicated signs. In this paper we consider the problem of fingerspelling recognition in videos, introducing an end-to-end lexicon-free model that consists of a deep auto-encoder image feature learner followed by an attention-based encoder-decoder for prediction. The feature extractor is a vanilla auto-encoder variant, employing adoi:10.21437/interspeech.2019-2422 dblp:conf/interspeech/PapadimitriouP19 fatcat:nenzyye6vbe65ga7xl4mnrslyi