Image binarization, the conversion of a color image into its monochromatic version, plays a key role in many document processing pipelines. The technical literature presents over a hundred different algorithms for document image binarization yielding different quality results, depending on the intrinsic features of the document image. In addition, their processing times vary significantly, influencing their applicability. The fundamental question here is: Which binarization scheme should one choose, from all the available ones, to provide a reasonable quality-time monochromatic image? A recently published paper shows that the analysis of the texture of the paper background of a scanned document may offer elements to select an appropriate binarization algorithm. This work extends such results by analyzing twelve texture descriptors using three distinct distance measures to evaluate 315 binarization schemes, providing solid evidence that the analysis of the paper texture may guide the selection of a suitable binarization algorithm for a given scanned document image.

Document image binarization is a fundamental step in many document processes. No binarization algorithm performs well on all types of document images, as the different kinds of digitalization devices and the physical noises present in the document and acquired in the digitalization process alter their performance. Besides that, the processing time is also an important factor that may restrict its applicability. This competition on binarizing photographed documents assessed the quality, time, space, and performance of five new algorithms and sixty-four "classical" and alternative algorithms. The evaluation dataset is composed of laser and deskjet printed documents, photographed using six widely-used mobile devices with the strobe flash on and off, under two different angles and places of capture.

Today, over eighty percent of the world's population owns a smart-phone with an in-built camera, and they are very often used to photograph documents. Document binarization is a key process in many document processing platforms. This competition on binarizing photographed documents assessed the quality, time, space, and performance of five new algorithms and sixty-four "classical" and alternative algorithms. The evaluation dataset is composed of offset, laser, and deskjet printed documents, photographed using six widely-used mobile devices with the strobe flash on and off, under two different angles and places of capture.

Most of the best known binarization algorithms have grayscale conversion as a pre-processing step, before applying the binarization strategy itself. Many algorithms produce equally good or even better quality images if fed with only one component of the image, instead of its gray-scale/luminance equivalent. The time-gain here is obtained in avoiding the several floating-point calculations in converting a RGB-color image into grayscale. More than 60 binarization algorithms were tested using "real-world" images.

Smartphones with cameras are omnipresent in today's world and are very often used to photograph documents. Document binarization is a key process in many document processing platforms. This competition on binarizing photographed documents assessed the quality and time performance of 13 new algorithms and 50 existing algorithms. The evaluation dataset is composed of offset, laser, and deskjet printed documents, photographed using four widely-used mobile devices with the strobe flash on and off, under two different angles and places of capture.

Document image binarization is a key process in many document processing platforms. The DocEng'2020 Time-Quality Competition on Binarizing Photographed Documents assessed the performance of eight new algorithms and also 41 other "classical" algorithms. Besides the quality of the binary image, the execution time of the algorithms was assessed. The evaluation dataset is composed of 32 documents photographed using four widely-used mobile devices with the strobe flash on and off, under several different angles of capture.

Binarization algorithms are an important step in most document analysis and recognition applications. Many aspects of the document affect the performance of binarization algorithms, such as paper texture and color, noises such as the back-to-front interference, stains, and even the type and color of the ink. This work focuses on determining how each document characteristic impacts the time to process and the quality of the binarized image. This paper assesses thirty of the most widely used document binarization algorithms.

The ICDAR 2019 Time-Quality Binarization Competition assessed the performance of seventeen new together with thirty previously published binarization algorithms. The quality of the resulting two-tone image and the execution time were assessed. Comparisons were on both in "real-world" and synthetic scanned images, and in documents photographed with four models of widely used portable phones. Most of the submitted algorithms employed machine learning techniques and performed best on the most complex images. Traditional algorithms provided very good results at a fraction of the time.

Image binarization is a technique widely used for documents as monochromatic documents claim for far less space for storage and computer bandwidth for network transmission than their color or even grayscale equivalent. Paper color, texture, aging, translucidity, kind and color of ink used in handwritting, printing process, digitalization process, etc., are some of the factors that affect binarization. No algorithm is good enough to be a winner in the binarization of all kinds of documents. This paper presents a methodology to assess the performance of binarization algorithms for a wide variety of text documents, allowing a judicious quantitative choice of the best algorithms and their parameters.