SignDATA: Data Pipeline for Sign Language Translation

TL;DR

SignDATA is a configurable, standardized preprocessing toolkit for sign language datasets that facilitates consistent data preparation, supports multiple backends, and enhances reproducibility in sign language research.

Contribution

The paper introduces SignDATA, a flexible, reproducible preprocessing pipeline for sign language data that unifies heterogeneous datasets and supports multiple extraction backends.

Findings

Validated through backend comparison and preprocessing ablations.

Demonstrated privacy-aware video generation capabilities.

Provided a reproducible, configurable preprocessing layer for sign-language research.

Abstract

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.