  We present a ghost handwritten digit recognition method for the unknown
handwritten digits based on ghost imaging (GI) with deep neural network, where
a few detection signals from the bucket detector, generated by the Cosine
Transform speckle, are used as the characteristic information and the input of
the designed deep neural network (DNN), and the classification is designed as
the output of the DNN. The results show that the proposed scheme has a higher
recognition accuracy (as high as 98.14% for the simulations, and 92.9% for the
experiments ) with a smaller sampling ratio (say 12.76%). With the increase of
the sampling ratio, the recognition accuracy is enhanced greatly. Compared with
the traditional recognition scheme using the same DNN structure, the proposed
scheme has a little better performance with a lower complexity and non-locality
property. The proposed scheme provides a promising way for remote sensing.
