BosphorusSign22k Sign Language Recognition Dataset

Abstract

Sign Language Recognition is a challenging research domain. It has recently seen several advancements with the increased availability of data. In this paper, we introduce the BosphorusSign22k, a publicly available large scale sign language dataset aimed at computer vision, video recognition and deep learning research communities. The primary objective of this dataset is to serve as a new benchmark in Turkish Sign Language Recognition for its vast lexicon, the high number of repetitions by native signers, high recording quality, and the unique syntactic properties of the signs it encompasses. We also provide state-of-the-art human pose estimates to encourage other tasks such as Sign Language Production. We survey other publicly available datasets and expand on how BosphorusSign22k can contribute to future research that is being made possible through the widespread availability of similar Sign Language resources. We have conducted extensive experiments and present baseline results to underpin future research on our dataset.

8 pages

References in corpus (3)

• Two-Stream Convolutional Networks for Action Recognition in Videos
• OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
• Learning to Compute Word Embeddings On the Fly

Cited by in corpus (7)

• AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods
• Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition
• Quantitative Survey of the State of the Art in Sign Language Recognition
• Survey on Hand Gesture Recognition from Visual Input
• AzSLD: Azerbaijani Sign Language Dataset for Fingerspelling, Word, and Sentence Translation with Baseline Software
• E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods
• SkelCap: Automated Generation of Descriptive Text from Skeleton Keypoint Sequences