Alabib-65: A realistic dataset for Algerian sign language recognition

What is it about?

Alabib-65 represents a meticulously curated, diverse, and authentic dataset that exhibits remarkable realistic attributes, making it an invaluable resource for researchers in the field of sign language recognition. This study not only addresses the scarcity of Algerian sign language datasets but also contributes to a profound understanding of signing content within real-world contexts, thus establishing a strong foundation for the development of Deaf-friendly solutions and tools.

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Photo by Aaron Burden on Unsplash

Photo by Aaron Burden on Unsplash

Why is it important?

The significance of this study extends beyond dataset availability, as it introduces a novel direction for dataset design—problem-specific datasets. While the ultimate goal of sign language recognition (SLR) is to deploy robust recognition models, the inherent complexity of the task necessitates addressing various problem classes. These classes encompass visual aspects as well as linguistic properties of signs. Unfortunately, the current evaluation approach heavily relies on general SLR datasets, which exhibit two major drawbacks. Firstly, they erroneously assume that the dataset represents the full spectrum of real-world problems, while overlooking the linguistic related problem classes. Secondly, overall performance evaluation fails to provide nuanced feedback for algorithmic improvement, as it neglects individual problem responses. Thus, advocating for a paradigm shift, we propose a comprehensive multi-problem performance evaluation approach wherein each dataset is meticulously designed to target specific SLR problem classes expected to emerge in real-world scenarios. This approach enables a detailed assessment of algorithmic sensitivity to each problem individually, unraveling crucial insights into the algorithm's strengths and limitations. By adopting this perspective, we establish a robust foundation for reliably verifying the algorithm's traits. Notably, our dataset, Alabib-65, facilitates the evaluation of algorithmic performance in similar sign classification, further exemplifying its value and relevance in this domain. Consequently, we strongly recommend that stakeholders involved in the design and deployment of SLR datasets consider the creation of problem-specific datasets or the restructuring of existing datasets to align with the addressed problems. In summary, beyond dataset availability, this study paves the way for a transformative shift in dataset design, urging the adoption of problem-specific datasets for comprehensive algorithm evaluation. By embracing this innovative approach, researchers can enhance the robustness and effectiveness of SLR models, ultimately driving advancements in the field and furthering the development of Deaf-friendly technology.