Are you tired of grappling with the complexities of the MNIST dataset? If so, I have a refreshing solution for you! Enter the ASL Sign Language dataset—a captivating alternative that is sure to pique your interest. With a training data set boasting 87,000 images at 200×200 pixels, this dataset offers a unique challenge and exciting opportunities for exploration.
The ASL Sign Language dataset comprises 29 classes, including the letters A-Z and special classes for SPACE, DELETE, and NOTHING. But here’s the twist—I didn’t stop at just using raw pixel values as features. Instead, I delved into the realm of feature engineering, opening up a world of possibilities for creativity and innovation in model development.
Embark on a journey with Mediapipe, Google’s innovative computer vision library that showcases incredible hand-tracking capabilities through landmark points. These landmarks serve as invaluable input features for refining your models, offering a myriad of benefits compared to conventional image-based approaches.
1. Dimensionality Reduction:
Simplify your data processing woes by leveraging landmark coordinates to streamline your input size. With just 63 values encapsulating the normalized x, y, and z positions of 21 essential hand landmarks, you can enhance computational efficiency and expedite model training and performance.
2. Enhanced Pose Representation:
Immerse yourself in the intricate details of hand gestures as the landmark-based features meticulously capture the nuances of hand pose geometry. Say goodbye to distractions from the background, allowing your models to focus solely on deciphering hand shape and movement intricacies.
3. Adaptive to Input Variations:
Embrace engineered features like landmarks for robust generalization across diverse scenarios, including fluctuations in lighting, backgrounds, and hand sizes. Bid farewell to noise sensitivity common in raw images, and welcome structural information that empowers your models to discern relevant patterns efficiently.
Discover the transformative power of Mediapipe as each image undergoes meticulous processing to extract 21 pivotal landmarks. Normalized x, y, and z values pave the way for consistent data representation, culminating in a refined dataset with 63 normalized values per sample. Brace yourself for a revolutionary approach that enriches your model’s precision and credibility.
So, are you ready to take the plunge and explore the realms of XGBoost and CNN models using the ASL dataset? Uncover a world brimming with possibilities