  We propose a deep-learning-based classification of data pages used in
holographic memory. We numerically investigated the classification performance
of a conventional multi-layer perceptron (MLP) and a deep neural network, under
the condition that reconstructed page data are contaminated by some noise and
are randomly laterally shifted. The MLP was found to have a classification
accuracy of 91.58%, whereas the deep neural network was able to classify data
pages at an accuracy of 99.98%. The accuracy of the deep neural network is two
orders of magnitude better than the MLP.
