This review provides a comprehensive view of the field of historical document image
binarization with a focus on the contributions made in the last decade. After the introduction …

Artificial neural networks have been shown significant performance in various image-to-
image conversion tasks. However, complex conversions often require a large number of …

Convolutional neural networks (CNNs) have previously been broadly utilized to binarize
document images. These methods have problems when faced with degraded historical …

Abstract The ICDAR 2021 Time-Quality Binarization Competition assessed the performance
of 12 new and 49 other previously published binarization algorithms for scanned document …

The intrinsic features of documents, such as paper color, texture, aging, translucency, the
kind of printing, ty** or handwriting, etc., are important with regard to how to process and …

Text is the most crucial element in a document image but is often disconnected in document
image binarization. Most of the previous methods based on deep learning do not focus on …

Smartphones with an in-built camera are omnipresent today in the life of over eighty percent
of the world's population. They are very often used to photograph documents. Document …

The ICDAR 2019 Time-Quality Binarization Competition assessed the performance of
seventeen new together with thirty previously published binarization algorithms. The quality …

Abstract The recent Time-Quality Binarization Competitions have shown that no single
binarization algorithm is good for all kinds of document images and that the time elapsed in …

Many document processing platforms have image binarization as a key step. The
performance of binarization algorithms depends on several factors that span from the quality …