  Classification techniques for images of handwritten characters are
susceptible to noise. Quadtrees can be an efficient representation for learning
from sparse features. In this paper, we improve the effectiveness of
probabilistic quadtrees by using a pixel level classifier to extract the
character pixels and remove noise from handwritten character images. The pixel
level denoiser (a deep belief network) uses the map responses obtained from a
pretrained CNN as features for reconstructing the characters eliminating noise.
We experimentally demonstrate the effectiveness of our approach by
reconstructing and classifying a noisy version of handwritten Bangla Numeral
and Basic Character datasets.
