  Smartphones have enabled effortless capturing and sharing of documents in
digital form. The documents, however, often undergo various types of
degradation due to aging, stains, or shortcoming of capturing environment such
as shadow, non-uniform lighting, etc., which reduces the comprehensibility of
the document images. In this work, we consider the problem of document image
cleanup on embedded applications such as smartphone apps, which usually have
memory, energy, and latency limitations due to the device and/or for best human
user experience. We propose a light-weight encoder decoder based convolutional
neural network architecture for removing the noisy elements from document
images. To compensate for generalization performance with a low network
capacity, we incorporate the perceptual loss for knowledge transfer from
pre-trained deep CNN network in our loss function. In terms of the number of
parameters and product-sum operations, our models are 65-1030 and 3-27 times,
respectively, smaller than existing state-of-the-art document enhancement
models. Overall, the proposed models offer a favorable resource versus accuracy
trade-off and we empirically illustrate the efficacy of our approach on several
real-world benchmark datasets.
