One-Stage-TFS: Thai One-Stage Fingerspelling Dataset for Fingerspelling Recognition Frameworks

TL;DR

The paper introduces the One-Stage-TFS dataset, a comprehensive collection of Thai sign language hand gestures aimed at advancing end-to-end recognition frameworks using deep learning techniques.

Contribution

It provides a new, publicly available dataset with diverse gestures and backgrounds to facilitate research in Thai sign language recognition and deep learning methods.

Findings

Dataset includes 7,200 images of 15 gestures.

Supports development of end-to-end recognition models.

Encourages exploration of deep learning techniques for gesture recognition.

Abstract

The Thai One-Stage Fingerspelling (One-Stage-TFS) dataset is a comprehensive resource designed to advance research in hand gesture recognition, explicitly focusing on the recognition of Thai sign language. This dataset comprises 7,200 images capturing 15 one-stage consonant gestures performed by undergraduate students from Rajabhat Maha Sarakham University, Thailand. The contributors include both expert students from the Special Education Department with proficiency in Thai sign language and students from other departments without prior sign language experience. Images were collected between July and December 2021 using a DSLR camera, with contributors demonstrating hand gestures against both simple and complex backgrounds. The One-Stage-TFS dataset presents challenges in detecting and recognizing hand gestures, offering opportunities to develop novel end-to-end recognition frameworks. Researchers can utilize this dataset to explore deep learning methods, such as YOLO, EfficientDet, RetinaNet, and Detectron, for hand detection, followed by feature extraction and recognition using techniques like convolutional neural networks, transformers, and adaptive feature fusion networks. The dataset is accessible via the Mendeley Data repository and supports a wide range of applications in computer science, including deep learning, computer vision, and pattern recognition, thereby encouraging further innovation and exploration in these fields.