Binarization of digital documents is the task of classifying each pixel in an image of the document as belonging to the background (parchment/paper) or foreground (text/ink). Historical documents are often subject to degradations, that make the task challenging. In the current work a deep neural network architecture is proposed that combines a fully convolutional network with an unrolled primal-dual network that can be trained end-to-end in order to achieve state of the art binarization on four out of seven datasets. Document binarization is formulated as a energy minimization problem. A fully convolutional neural network is trained for semantic labeling of pixels to provide class labeling cost associated with each pixel. This cost estimate is refined along the edges to compensate for any over or under estimation of the under represented fore-ground class using a primal-dual approach. We provide necessary overview on proximal operator that facilitates theoretical underpinning in order to train a primal-dual network using a gradient descent algorithm. Numerical instabilities encountered due to the recurrent nature of primal-dual approach are handled. We provide experimental results on document binarization competition dataset along with network changes and hyperparameter tuning required for stability and performance of the network. The network when pre-trained on synthetic dataset performs better as per the competition metrics.
PDNet REF is based on network architecture that uses FCNs with an unrolled primal-dual network.
PDNet: Semantic Segmentation integrated with a Primal-Dual Network for Document binarization
ArXiv
ArXiv
Mathematics Computer Science