An Image Analysis Approach to the Calligraphy of Books
This work addresses authorship attribution in calligraphy analysis, but it is incremental as it builds on previous topological network studies.
The authors tackled authorship attribution by quantifying geometrical properties of visualized networks using image analysis techniques, achieving performance similar to topological measurements and improving it when combined.
Text network analysis has received increasing attention as a consequence of its wide range of applications. In this work, we extend a previous work founded on the study of topological features of mesoscopic networks. Here, the geometrical properties of visualized networks are quantified in terms of several image analysis techniques and used as subsidies for authorship attribution. It was found that the visual features account for performance similar to that achieved by using topological measurements. In addition, the combination of these two types of features improved the performance.