A deep learning model that recognizes sign language alphabets.
Number of Hidden Layers : 3
A deep neural network is used to gather train on more features. Currently, we have 25 classes, thus a deep NN is needed to distinguish each alphabet from the other and achieve higher accuracy.
Number of training examples = 27455 Number of test examples = 7172
X_train shape: (784, 27455), Y_train shape: (25, 27455), X_test shape: (784, 7172), Y_test shape: (25, 7172)
Number of channels in each image : 1 (Grayscale), Size of each image vector = 28 x 28 x 1 = 784
Dataset : https://www.kaggle.com/datamunge/sign-language-mnist
Pull requests are welcome.