  Binarization of degraded historical manuscript images is an important
pre-processing step for many document processing tasks. We formulate
binarization as a pixel classification learning task and apply a novel Fully
Convolutional Network (FCN) architecture that operates at multiple image
scales, including full resolution. The FCN is trained to optimize a continuous
version of the Pseudo F-measure metric and an ensemble of FCNs outperform the
competition winners on 4 of 7 DIBCO competitions. This same binarization
technique can also be applied to different domains such as Palm Leaf
Manuscripts with good performance. We analyze the performance of the proposed
model w.r.t. the architectural hyperparameters, size and diversity of training
data, and the input features chosen.
