# SignDATA: Data Pipeline for Sign Language Translation

arXiv: https://arxiv.org/abs/2604.20357 · HTML: https://mlsanity.com/paper/2604.20357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 21 of 21)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [BlazePose: On-device Real-time Body Pose tracking](https://mlsanity.com/paper/2006.10204/) (2020)
- [Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining](https://mlsanity.com/paper/2307.14768/) (2023)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation](https://mlsanity.com/paper/2203.04287/) (2022)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Towards Privacy-Aware Sign Language Translation at Scale](https://mlsanity.com/paper/2402.09611/) (2024)
- [SLTUNET: A Simple Unified Model for Sign Language Translation](https://mlsanity.com/paper/2305.01778/) (2023)
- [ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition](https://mlsanity.com/paper/2304.05934/) (2023)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [High Performance I/O For Large Scale Deep Learning](https://mlsanity.com/paper/2001.01858/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Compact Sign Language Translation: Frame Rate and Model Size Trade-offs](https://mlsanity.com/paper/2605.09554/) (2026)

## Abstract (arXiv)

Sign-language datasets are difficult to preprocess consistently because they vary in annotation schema, clip timing, signer framing, and privacy constraints. Existing work usually reports downstream models, while the preprocessing pipeline that converts raw video into training-ready pose or video artifacts remains fragmented, backend-specific, and weakly documented. We present SignDATA, a config-driven preprocessing toolkit that standardizes heterogeneous sign-language corpora into comparable outputs for learning. The system supports two end-to-end recipes: a pose recipe that performs acquisition, manifesting, person localization, clipping, cropping, landmark extraction, normalization, and WebDataset export, and a video recipe that replaces pose extraction with signer-cropped video packaging. SignDATA exposes interchangeable MediaPipe and MMPose backends behind a common interface, typed job schemas, experiment-level overrides, and per-stage checkpointing with config- and manifest-aware hashes. We validate the toolkit through a research-oriented evaluation design centered on backend comparison, preprocessing ablations, and privacy-aware video generation on datasets. Our contribution is a reproducible preprocessing layer for sign-language research that makes extractor choice, normalization policy, and privacy tradeoffs explicit, configurable, and empirically comparable.Code is available at https://github.com/balaboom123/signdata-slt.