DOI: 10.57760/sciencedb.28304
title: Sign4all: a Spanish Sign Language Dataset
title: Sign4all: a Spanish Sign Language Dataset

description: DescriptionThe Sign4all dataset is designed for research in Isolated Sign Language Recognition (ISLR), with a focus on Spanish Sign Language (Lengua de Signos Española, LSE). It includes high-resolution RGB video recordings and corresponding skeletal keypoints for 24 signs related to daily activities, particularly within the context of dining and catering. The dataset captures both right-handed and left-handed sign executions, offering a balanced and diverse collection aimed at developing inclusive SLR systems. In total, the dataset includes 7,756 manually segmented video samples and skeletal annotations, with an augmented version expanding to 61,409 samples to support deep learning applications.Data generation proceduresEight participants (4 male, 4 female) recorded signs using an Azure Kinect DK camera at 2560×1440 resolution and 30 fps. Each participant performed all signs with both their dominant and non-dominant hands to simulate variability in signer handedness. Signs were performed from a neutral starting position and followed a structured protocol for consistency. Recordings were conducted in a controlled indoor environment with stable lighting and no clothing restrictions, ensuring realistic visual diversity.All signs are dynamic (in-motion) gestures. Each was recorded approximately 20 times per hand. The camera was fixed at a height of 117 cm, and participants stood at variable distances (100–170 cm) to account for individual height differences.Data structure and formatThe dataset is organized into four versions:RGB Original: Raw segmented videos without background removal or normalization.RGB Normalized: Background-cropped, square videos temporally normalized to 48 frames.RGB Normalized + Augmentation: Additional visual augmentations applied.Skeletal Keypoints: 48×100 matrices per sample, representing 2D keypoints in HDF5 format.The RGB samples are distributed using AVI format.Use cases and reusabilityThe Sign4all dataset supports a variety of applications in Sign Language Technology:Isolated Sign Language RecognitionSkeletal-based gesture recognitionSigner-independent recognition model trainingResearchers can use RGB or skeletal data directly with deep learning models in PyTorch, TensorFlow or other frameworks. Due to privacy concerns, dataset access is restricted and requires a Data Usage Agreement (DUA) request.
description: DescriptionThe Sign4all dataset is designed for research in Isolated Sign Language Recognition (ISLR), with a focus on Spanish Sign Language (Lengua de Signos Española, LSE). It includes high-resolution RGB video recordings and corresponding skeletal keypoints for 24 signs related to daily activities, particularly within the context of dining and catering. The dataset captures both right-handed and left-handed sign executions, offering a balanced and diverse collection aimed at developing inclusive SLR systems. In total, the dataset includes 7,756 manually segmented video samples and skeletal annotations, with an augmented version expanding to 61,409 samples to support deep learning applications.Data generation proceduresEight participants (4 male, 4 female) recorded signs using an Azure Kinect DK camera at 2560×1440 resolution and 30 fps. Each participant performed all signs with both their dominant and non-dominant hands to simulate variability in signer handedness. Signs were performed from a neutral starting position and followed a structured protocol for consistency. Recordings were conducted in a controlled indoor environment with stable lighting and no clothing restrictions, ensuring realistic visual diversity.All signs are dynamic (in-motion) gestures. Each was recorded approximately 20 times per hand. The camera was fixed at a height of 117 cm, and participants stood at variable distances (100–170 cm) to account for individual height differences.Data structure and formatThe dataset is organized into four versions:RGB Original: Raw segmented videos without background removal or normalization.RGB Normalized: Background-cropped, square videos temporally normalized to 48 frames.RGB Normalized + Augmentation: Additional visual augmentations applied.Skeletal Keypoints: 48×100 matrices per sample, representing 2D keypoints in HDF5 format.The RGB samples are distributed using AVI format.Use cases and reusabilityThe Sign4all dataset supports a variety of applications in Sign Language Technology:Isolated Sign Language RecognitionSkeletal-based gesture recognitionSigner-independent recognition model trainingResearchers can use RGB or skeletal data directly with deep learning models in PyTorch, TensorFlow or other frameworks. Due to privacy concerns, dataset access is restricted and requires a Data Usage Agreement (DUA) request.