The goal of this project was to build a GAN to solve the problem of regenerating missing parts/regions of handwritten digits. In the initial chapters, we applied deep learning to enable customers of a restaurant chain to write their phone numbers in a simple iPad application to get a text notification that their party could be seated. The use case of this chapter was to apply deep learning to generate missing parts of the digits of the phone number so that a text notification can be sent to the right person.
The CNN digit classifier model accuracy hit 98.84% on the MNIST validation data. With the data we generated to simulate missing parts of a digit when fed to the CNN digit classifier, the model was only 74.90% accurate.
The same dataset with missing sections of the digit was passed to the generator to recover the missing parts. The resulting digits...