The neural network used for the sketch is a basic 784 x 30 x 10 network, trained on the 50.000 handwritten digits from the MNIST dataset (http://yann.lecun.com/exdb/mnist/). The performance of the network is optimized by L2 regularization and squashed weights initialization. This makes the network be 96% accurate. Even though neural networks built on MNIST can now achieve nearly 100% classification accuracy, I chose to stick to this simple architecture to test my hypothesis.

DONE

• Draw a digit
• Run the canvas image on network -> make a guess
• Ask for feedback (get correct digit)
• See the list of guesses according to their probabilities

Ideas to improve on:

1. Sensitivity to the position/size of digit

   • as the network is trained on the preprocessed MNIST digits, the inputted drawing won't necessarily look the same as the training data
   • this makes the network see other digits based on the position/size of your drawing

2. Better data manipulation techniques

   • see the manipulated data file for my attempt to modify the MNIST data and re-train my network.
   • accuracy went down to 50% when data manipulation was carried out in both training and validation
   • to figure out: what ratio of manipulated/original data should be used

3. Store the feedback image + label in database

4. Re-train network by the inputted digits (automatically?)