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The data set is a collection of images of alphabets from the American Sign Language, separated in 29 folders which represent the various classes.
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The training data set contains 87,000 images which are 200x200 pixels. There are 29 classes, of which 26 are for the letters A-Z and 3 classes for SPACE, DELETE and NOTHING. These 3 classes are very helpful in real-time applications, and classification. The test data set contains a mere 29 images, to encourage the use of real-world test images.
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Dataset: https://www.kaggle.com/datasets/grassknoted/asl-alphabet
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Model file: ASL_ResNet50.ipynb
- Using ResNet50: weight: imageNet, Include_top: False with 99% accuracy
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This dataset have 36 classes with 70 images each folder
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Using basic CNN with 90% accuracy
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Model File: HandSignCNN.ipynb