Search Results (2)

This study aims to assess the deterioration aspects of a historical manuscript dating back to the 14th century that was deposited in the Library of the Arabic Language Academy, Cairo, Egypt. The study aims at the exploration of the role of various fungal strains that had colonized this deteriorated manuscript in its biodeterioration through their efficacy in the secretion of various hydrolytic enzymes. To evaluate the deterioration, various techniques, including visual inspection, attenuated total reflectance Fourier transform infrared (ATR-FTIR), scanning electron microscopy (SEM), X-Ray diffraction analysis (XRD), color change, and pH value, were utilized. The fungal strains linked to the historical document were isolated, identified, and evaluated for their deterioration activities. The findings demonstrate that the manuscript exhibits a variety of deterioration signs including color change, brittleness and weakness, erosion, and removal of the grain surface pattern in leather binding. According to the ATR-FTIR, the chemical composition of the historical paper and leather underwent some alterations. The historical paper has a lower level of cellulose crystallinity than the control sample. Penicillium chrysogenum (two isolates), P. citrinum (four isolates), Aspergillus ustus (three isolates), A. terreus (two isolates), A. chinensis (one isolate), Paecilomyces sp. (one isolate), and Induratia sp. (one isolate) were among the fourteen fungal strains identified as being associated with the historical manuscript. These fungal strains produced several hydrolytic enzymes with high activity, such as cellulase, amylase, gelatinase, and pectinase, which play a key role in biodegradation. Full article

Automatic dating tools for historical documents can greatly assist paleographers and save them time and effort. This paper describes a novel method for estimating the date of historical Arabic documents that employs hierarchical fusions of multiple features. A set of traditional features and features extracted by a residual network (ResNet) are fused in a hierarchical approach using joint sparse representation. To address noise during the fusion process, a new approach based on subsets of multiple features is being considered. Following that, supervised and unsupervised classifiers are used for classification. We show that using hierarchical fusion based on subsets of multiple features in the KERTAS dataset can produce promising results and significantly improve the results. Full article