  Handwritten document-image binarization is a semantic segmentation process to
differentiate ink pixels from background pixels. It is one of the essential
steps towards character recognition, writer identification, and script-style
evolution analysis. The binarization task itself is challenging due to the vast
diversity of writing styles, inks, and paper materials. It is even more
difficult for historical manuscripts due to the aging and degradation of the
documents over time. One of such manuscripts is the Dead Sea Scrolls (DSS)
image collection, which poses extreme challenges for the existing binarization
techniques. This article proposes a new binarization technique for the DSS
images using the deep encoder-decoder networks. Although the artificial neural
network proposed here is primarily designed to binarize the DSS images, it can
be trained on different manuscript collections as well. Additionally, the use
of transfer learning makes the network already utilizable for a wide range of
handwritten documents, making it a unique multi-purpose tool for binarization.
Qualitative results and several quantitative comparisons using both historical
manuscripts and datasets from handwritten document image binarization
competition (H-DIBCO and DIBCO) exhibit the robustness and the effectiveness of
the system. The best performing network architecture proposed here is a variant
of the U-Net encoder-decoders.
