Automatic Classification of Fiber Composition in Digital Macro Images Using VGG-16 for Nondestructive Paper Analysis

Abstract

Paper is made up of fibers with different characteristics based on the raw material used, the conditions in which the paper was stocked, and the process by which it was made. Various studies have been conducted on the structure and formation of paper layers. The fiber of paper used for ancient writings is often analyzed using subjective organoleptic assessment. Furthermore, objective methods to analyze paper stains fibers with chemicals and cause the destruction of the paper. Therefore, depending on the analysis target, nondestructive methods for paper analysis are desired. In this study, we propose an automatic classification of the fibers contained in paper using images obtained with macro photography using a commercially available digital camera with VGG-16. Results show that the average overall accuracy between the predicted classification result and the fiber analysis in three experiments was 94.2%. However, some blurry images taken with a digital camera caused misclassification because determining the structures of fiber layers, such as their thickness and direction, was difficult. In the future, our proposed method could be used to qualify the types of papers in historical and cultural artefacts for example Samarkand paper.