An algorithm that facilitates communication between a speech-impaired person and someone who doesn't understand sign language using convolution neural networks

Training set: 1080 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (180 pictures per number).

Test set: 120 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (20 pictures per number).

Here are examples for each number, and corresponding labels converted to one-hot.

Architecture:

1. Input is an image of size 64 x 64 x 3 (RGB), which is normalized by dividing 255
2. Model:
3. The output of last hidden layer gives a probability of the image belonging to one of the six classes
4. RELU activation function. Cross entropy cost. Adam optimizer
5. Mini-batch gradient descent with minibatch_size of 64

The model is CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED

Outcome:

1. Training cost graph-

2. Train Accuracy - 0.92963
   Test Accuracy - 0.791667
3. TODO- to overcome overfitting, add L2 or dropout regularization