Sign Language Images Dataset

The Sign Language MNIST is presented here and follows the jpeg image format with labels. The American Sign Language letter database of hand gestures represent a multi-class problem with 24 classes of letters (excluding J and Z which require motion). This dataset has been adopted from Sign Language MNIST, converting csv file into images also decreasing the overall size of database.

There are a. sign-language human-pose-estimation mano vqvae smpl-x smplx sign-language-datasets motion-generation eccv2024 Updated Jul 17, 2025 Python. The following table contains a extended list of existing datasets including a variety of different sign languages and data content.

The table is grouped by three types of language segmentation -- Finger spelling, Isolated (single) signs and Continuous Sign Language. Our dataset contains a series of different folders containing different format of images, like the root images, augmented images, preprocessed images (grayscale, histogram equalization, binarized images), and skeleton mediapipe landmark images for various classes of the alphabets. Each folder is divided into two types of folders, each containing a different set of images which were segregated.

Dataset Card for ASL-MNIST This is a FiftyOne dataset with 34,627 samples of American Sign Language (ASL) alphabet images, converted from the original Kaggle Sign Language MNIST dataset into a format optimized for computer vision workflows. Installation If you haven't already, install FiftyOne. 180,717 Images - Sign Language Gestures Recognition Data.

The data diversity includes multiple scenes, 41 static gestures, 95 dynamic gestures, multiple photographic angles, and multiple light conditions. In terms of data annotation, 21 landmarks, gesture types, and gesture attributes were annotated. This dataset can be used for tasks such as gesture recognition and sign language translation.

Description: The Signclusive Mediapipe dataset is a comprehensive collection designed for the development and training of machine learning models in recognizing sign language. This dataset encompasses images representing the 26 letters of the English alphabet, as well as the "space" sign, making a total of 27 distinct classes. Each class is represented by images from five different signers.

The American Sign Language Letters dataset is an object detection dataset of each ASL letter with a bounding box. David Lee, a data scientist focused on accessibility, curated and released the dataset for public use. The American Sign Language (ASL) dataset, which contains 26,000 high-quality images, fills key gaps in current datasets by providing better data diversity, annotation quality, and usability for real-world applications.

In contrast to datasets such as Kaggle's ASL dataset (2515 images, 2 participants, very limited skin tone diversity) and Roboflow's dataset (1728 images, very limited metadata. This dataset contains images of American Sign Language (ASL) gestures. It contains a total of 210,000 images of 28 classes representing various ASL signs (A-Z, Del, Space).

The images are captured from various angles against different backgrounds, which enhances the dataset's diversity and suitability for real.