Handwritten Digit Recognition
Optical character recognition has been researched and used since the early 1900s
to convert printed text into machine processable data, see
Optical Character Recognition (OCR).
The ability to process fixed font characters is well established but accuracy is hampered by the amount of noise in the image.
Hand written, hand printed actually, characters are a much more difficult problem than fixed font due to the noise and lack of consistency.
Our project is to train an autoencoder neural network to recognize hand written digits from the NMIST database
with an accuracy greater than that achieved in a previous work,
"Use Neural Networks to analyze handwriting Format", by Julia Nomee, Avery Leider, Stephanie Haughton, and Yahia Saeed,
May, 2016. Our goal is to exceed the accuracy obtained in that work of 96.4%.