Serbian Sign Language Dataset

Data Processing and Keypoint Extraction

The videos were capture using a high-definition webcam, ensuring clarity and precision in the record gestures. The raw video data was then processed using the OpenCV library, a robust tool for computer vision tasks, and Mediapipe, a powerful library for real-time hand and face keypoint detection.

The keypoints of the hand in each frame were meticulously extract using Mediapipe, resulting in numpy arrays that map the coordinates of critical hand landmarks. These keypoints are essential for analyzing the hand’s pose, orientation, and movement across frames, allowing for detail gesture interpretation.

Applications

This dataset is a vital asset for various applications, particularly in the fields of gesture recognition, sign language translation, and human-computer interaction. It offers an extensive resource for training machine learning models, enabling advancements in AI-driven sign language recognition systems. Researchers can leverage this dataset to develop algorithms that accurately recognize and translate Serbian Sign Language, contributing to more inclusive communication technologies.