Cascading Modular UNets for Document Image Binarization In recent years, UNet has achieved good results in various image processing tasks. However, conventional UNets need to be retrained for individual tasks with enough amount of images with groundtruth. This requirement makes UNet not applicable to tasks with small amounts of data. In this paper, we propose to use modular UNets, each of which is pretrained to perform an existing image processing task, such as dilation, erosion, and histogram equalization. Then, to accomplish a specific image processing task, such as binarization of historical document images, the modular UNets are cascaded with intermodule skip connections and finetuned to the target task. We verified the proposed model using the Document Image Binarization Competition (DIBCO) 2017 dataset.

Learning 2D Morphological Network for Old Document Image Binarization Document image binarization, especially old handwritten documents, is a very important yet challenging task. There are various bottlenecks for binarizing historical documents due to different types of degradation present imultaneously such as back impression, ink bleed through, faded colours, and wear and tear of the writing media. We consider these degradation as various types of noise in the document image. Here we have proposed a 2D morphological network which consists of basic morphological operation like dilation and erosion to perform our targeted task. The network also includes linear combination of output from dilation and erosion operations. The aforementioned 2D morphological network is applied for image binarization, where the structuring elements (SEs) and the weights of the linear combination layer are learned through backpropagation. The proposed network has been evaluated on DIBCO 2017 and HDIBCO 2018 and ISILetter dataset. Our results show more convincing as compared to the results of other stateoftheart methods. Though the network is developed for old handwritten documents, it may be tuned to work for image processing task. The source code can be found here https:github.comranjanZICDAR_Binarization