Handwritten Carbon Form Preprocessing Based on Markov Random Field

This paper proposes a statistical approach to degraded handwritten form image preprocessing including binarization and form line removal. The degraded image is modeled by a Markov random field (MRF) where the prior is learnt from a training set of high quality binarized images, and the probabilistic density is learnt on-the-fly from the gray-level histogram of input image. We also modified the MRF model to implement form line removal. Test results of our approach show excellent performance on the data set of handwritten carbon form images.