  AI technologies for Natural Languages have made tremendous progress recently.
However, commensurate progress has not been made on Sign Languages, in
particular, in recognizing signs as individual words or as complete sentences.
We introduce OpenHands, a library where we take four key ideas from the NLP
community for low-resource languages and apply them to sign languages for
word-level recognition. First, we propose using pose extracted through
pretrained models as the standard modality of data to reduce training time and
enable efficient inference, and we release standardized pose datasets for 6
different sign languages - American, Argentinian, Chinese, Greek, Indian, and
Turkish. Second, we train and release checkpoints of 4 pose-based isolated sign
language recognition models across all 6 languages, providing baselines and
ready checkpoints for deployment. Third, to address the lack of labelled data,
we propose self-supervised pretraining on unlabelled data. We curate and
release the largest pose-based pretraining dataset on Indian Sign Language
(Indian-SL). Fourth, we compare different pretraining strategies and for the
first time establish that pretraining is effective for sign language
recognition by demonstrating (a) improved fine-tuning performance especially in
low-resource settings, and (b) high crosslingual transfer from Indian-SL to few
other sign languages. We open-source all models and datasets in OpenHands with
a hope that it makes research in sign languages more accessible, available here
at https://github.com/AI4Bharat/OpenHands .
