  Due to the sparsity of features, noise has proven to be a great inhibitor in
the classification of handwritten characters. To combat this, most techniques
perform denoising of the data before classification. In this paper, we
consolidate the approach by training an all-in-one model that is able to
classify even noisy characters. For classification, we progressively train a
classifier generative adversarial network on the characters from low to high
resolution. We show that by learning the features at each resolution
independently a trained model is able to accurately classify characters even in
the presence of noise. We experimentally demonstrate the effectiveness of our
approach by classifying noisy versions of MNIST, handwritten Bangla Numeral,
and Basic Character datasets.
