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 binarization is a key process in many document processing platforms. This paper assesses the quality, file size and time performance of sixty-eight binarization algorithms using five different versions of the input images. The evaluation dataset is composed of deskjet, laser and offset printed documents, photographed using six widely-used mobile devices with the strobe flash off and on, under two different angles and four shots with small variations in the position. Besides that, this paper also pinpoints the algorithms per device that may provide the best visual quality-time, document transcription accuracy-time, and size-time trade-offs. Furthermore, an indication is also given on the “overall winner” that would be the algorithm of choice if one has to use one algorithm for a smartphone-embedded application.

The intrinsic features of documents, such as paper color, texture, aging, translucency, the kind of printing, typing or handwriting, etc., are important with regard to how to process and enhance their image. Image binarization is the process of producing a monochromatic image having its color version as input. It is a key step in the document processing pipeline. The recent Quality-Time Binarization Competitions for documents have shown that no binarization algorithm is good for any kind of document image. This paper uses a sample of the texture of the scanned historical documents as the main document feature to select which of the 63 widely used algorithms, using five different versions of the input images, totaling 315 document image-binarization schemes, provides a reasonable quality-time trade-off.

Composites with natural lignocellulosic fillers are being cited as a viable and sustainable alternative to conventional materials, as they combine lower costs with lower weight. In many tropical countries, such as Brazil, there is a considerable amount of lignocellulosic waste that is improperly discarded, which causes pollution of the environment. The Amazon region has huge deposits of clay silicate materials in the Negro River basin, such as kaolin, which can be used as fillers in polymeric composite materials. This work investigates a new composite material (ETK) made of epoxy resin (ER), powdered tucumã endocarp (PTE), and kaolin (K), without coupling agents, with the aim of producing a composite with lower environmental impact. The ETK samples, totaling 25 different compositions, were prepared by cold molding. Characterizations of the samples were performed using a scanning electron microscope (SEM) and a Fourier-transform infrared spectrometer (FTIR). In addition, the mechanical properties were determined via tensile, compressive, three-point flexural and impact tests. The FTIR and SEM results showed an interaction between ER, PTE, and K, and the incorporation of PTE and K reduced the mechanical properties of the ETK samples. Nonetheless, these composites can be considered potential materials to be used for sustainable engineering applications in which high mechanical strength is not a main requirement of the material.

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.

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.