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 1 , a library where we take four key ideas from the NLP community for low-resource languages and apply them to sign languages for wordlevel recognition.First, we propose using pose extracted through pretrained models as the standard modality of data in this work to reduce training time and enable efficient inference, and we release standardized pose datasets for different existing sign language datasets.Second, we train and release checkpoints of 4 posebased isolated sign language recognition models across 6 languages (American, Argentinian, Chinese, Greek, Indian, and Turkish), 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 reproducible and more accessible.

There are over 300 sign languages in the world, many of which have very limited or no labelled sign-to-text datasets. To address low-resource data scenarios, self-supervised pretraining and multilingual finetuning have been shown to be effective in natural language and speech processing. In this work, we apply these ideas to sign language recognition.We make three contributions.- First, we release SignCorpus, a large pretraining dataset on sign languages comprising about 4.6K hours of signing data across 10 sign languages. SignCorpus is curated from sign language videos on the internet, filtered for data quality, and converted into sequences of pose keypoints thereby removing all personal identifiable information (PII).- Second, we release Sign2Vec, a graph-based model with 5.2M parameters that is pretrained on SignCorpus. We envisage Sign2Vec as a multilingual large-scale pretrained model which can be fine-tuned for various sign recognition tasks across languages.- Third, we create MultiSign-ISLR -- a multilingual and label-aligned dataset of sequences of pose keypoints from 11 labelled datasets across 7 sign languages, and MultiSign-FS -- a new finger-spelling training and test set across 7 languages. On these datasets, we fine-tune Sign2Vec to create multilingual isolated sign recognition models. With experiments on multiple benchmarks, we show that pretraining and multilingual transfer are effective giving significant gains over state-of-the-art results.All datasets, models, and code has been made open-source via the OpenHands toolkit.