American Sign-languages-datasets-Classification

First Dataset

• The data set is a collection of images of alphabets from the American Sign Language, separated in 29 folders which represent the various classes.

• 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.

• Dataset: https://www.kaggle.com/datasets/grassknoted/asl-alphabet

• Model file: ASL_ResNet50.ipynb

Model

• Using ResNet50: weight: imageNet, Include_top: False with 99% accuracy

Second dataset

• This dataset have 36 classes with 70 images each folder

• Dataset: https://www.kaggle.com/datasets/ayuraj/asl-dataset?fbclid=IwAR11k3oHQv13ABctZh7LoF41BDggAMbPIx4InPKuB-83VhWHvRa1fDWr3vo

Model

• Using basic CNN with 90% accuracy

• Model File: HandSignCNN.ipynb