ALGSL89: An Algerian Sign Language Dataset

Year 2023, Volume: 7 Issue: 2, 128 - 141, 30.12.2023

Ahmed Kheldoun Imene Kouar El Bachir Kouar

https://doi.org/10.33461/uybisbbd.1339892

Abstract

Automatic Sign Language Recognition (ASLR) is an area of active current research that aims to facilitate communication between deaf and hearing people. Recognizing sign language, particularly in the context of Algerian Sign Language (ALGSL), presents unique challenges that have yet to be comprehensively explored. So far, to the best of our knowledge, no study has considered the ALGSL Recognition. This is mainly due to the lack of available datasets. To overcome this challenge, we propose the ALGSL89 dataset, a pioneering effort in ALGSL research. The ALGSL89 dataset encompasses 4885 videos, capturing 89 distinct ALGSL signs, recorded by 10 subjects. This dataset serves as a foundational resource for advancing ASLR research specific to the Algerian signing community. In addition, we provide a comprehensive analysis of its characteristics, including statistical insights and detailed information on handshapes, positions, trajectories, and the dynamic aspects of sign movements. These details are crucial for researchers to gain a nuanced understanding of the dataset, ensuring its effective utilization in ASLR studies. In order to test the validity of our dataset, we provide the results obtained by applying a set of deep learning models. Finally, we present SignAtlas, an innovative ALGSL recognition system based on Autoencoder model.

Keywords

Algerian Sign Language, Sign language recognition, handshape recognition, Autoencoder, Deep learning

ALGSL89: Bir Cezayir İşaret Dili Veri Seti

Abstract

Otomatik İşaret Dili Tanıma (ASLR), sağır ve işiten insanlar arasında iletişimi kolaylaştırmayı amaçlayan aktif bir araştırma alanıdır. Özellikle Cezayir İşaret Dili (ALGSL) bağlamında işaret dili tanıma, henüz kapsamlı bir şekilde incelenmemiş benzersiz zorluklar sunmaktadır. Bildiğimiz kadarıyla, şimdiye kadar ALGSL Tanıma üzerine bir çalışma yapılmamıştır. Bu durum, büyük ölçüde mevcut veri setlerinin eksikliğinden kaynaklanmaktadır. Bu zorluğun üstesinden gelmek için, ALGSL araştırmalarında öncü bir çaba olarak ALGSL89 veri setini öneriyoruz. ALGSL89 veri seti, 10 konu tarafından kaydedilen 89 farklı ALGSL işaretini kapsayan 4885 video içermektedir. Bu veri seti, Cezayir işaret dili topluluğuna özgü ASLR araştırmalarını ilerletmek için temel bir kaynak olarak hizmet etmektedir. Ek olarak, el şekilleri, pozisyonlar, yörüngeler ve işaret hareketlerinin dinamik yönleri dahil olmak üzere, karakteristiklerinin kapsamlı bir analizini sunuyoruz. Bu detaylar, araştırmacıların veri setini nüanslı bir şekilde anlamalarını ve ASLR çalışmalarında etkili bir şekilde kullanmalarını sağlamak için hayati öneme sahiptir. Veri setimizin geçerliliğini test etmek amacıyla, derin öğrenme modelleri uygulayarak elde ettiğimiz sonuçları sunuyoruz. Son olarak, Otoenkoder modeline dayanan yenilikçi bir ALGSL tanıma sistemi olan SignAtlas'ı sunuyoruz.

Keywords

Cezayir İşaret Dili, İşaret Dili Tanıma, El Şekli Tanıma, Otoenkoder, Derin Öğrenme.

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