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In an era where technology fosters inclusion, sign language remains underrepresented in linguistic datasets, especially for low-resource languages such as Kenyan Sign Language (KSL). This paper presents a novel dataset created using MediaPipe's pose estimation technology, designed to address the scarcity of resources for KSL. The dataset includes 20,000 video recordings of KSL gestures, converted into anonymized stickman representations alongside detailed 3D pose coordinates stored in .npy files. The data collection process focused on preserving participant privacy while ensuring the integrity of gesture data. By utilizing pose estimation, the dataset captures manual and non-manual features of KSL while maintaining the anonymity of signers. Stickman representations abstract human features, mitigating ethical concerns associated with traditional video datasets and aligning with privacy-preserving practices. The dataset spans a diverse range of themes relevant to KSL, including daily interactions, cultural expressions, and educational contexts, providing comprehensive coverage of the KSL lexicon. This dataset is designed for reuse across multiple domains. Researchers can leverage it to train machine learning models for sign language recognition, while educators can utilize it to develop interactive language learning tools. Additionally, it supports the development of virtual sign language interpreters and 3D avatars for accessibility applications. By enabling seamless integration into machine learning frameworks, the dataset facilitates advancements in KSL-related technologies and contributes to bridging communication gaps within the Deaf community.
इस प्रकाशन की सामग्री के आधार पर, हम निम्नलिखित संसाधनों की अनुशंसा करते हैं।
निम्नलिखित संसाधन उद्धरण द्वारा इस प्रकाशन से संबंधित हैं।