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Handwritten document-image binarization is a semantic segmentation process todifferentiate ink pixels from background pixels. It is one of the essentialsteps towards character recognition, writer identification, and script-styleevolution analysis. The binarization task itself is challenging due to the vastdiversity of writing styles, inks, and paper materials. It is even moredifficult for historical manuscripts due to the aging and degradation of thedocuments over time. One of such manuscripts is the Dead Sea Scrolls (DSS)image collection, which poses extreme challenges for the existing binarizationtechniques. This article proposes a new binarization technique for the DSSimages using the deep encoder-decoder networks. Although the artificial neuralnetwork proposed here is primarily designed to binarize the DSS images, it canbe trained on different manuscript collections as well. Additionally, the useof transfer learning makes the network already utilizable for a wide range ofhandwritten documents, making it a unique multi-purpose tool for binarization.Qualitative results and several quantitative comparisons using both historicalmanuscripts and datasets from handwritten document image binarizationcompetition (H-DIBCO and DIBCO) exhibit the robustness and the effectiveness ofthe system. The best performing network architecture proposed here is a variantof the U-Net encoder-decoders.

BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks