Classification of Medieval Writings by New Statistical Measures

This paper presents new techniques of medieval manuscript text discrimination in order to assist paleographers to understand the ancient manuscripts. One of the purposes of paleography is to cluster medieval writings into families, to find relations between them, and find their historical period and/or location. This work aims to confirm paleographers’ classification of medieval writings. It also explores the occasion to show the viability to discriminate medieval writings by using image analysis. In this paper, we define the e-paleography as the assistance of the paleography science by computer vision. Our foremost idea is to select writing features which do not require image segmentation and layout analysis. Our method is based on the Spatial Grey-Level Dependence (SGLD) which measures the join probability between grey level values of pixels for each spatial relation. We prose a statistical measure witch generalizes SGLD, and we also propose the Spatial Curvature Dependence (SCD), the Spatial Orientation Dependence (SOD) and the Spatial Curvature/Orientation Dependence (SCOD), which describe local spatial orientation and curvature of writing shapes. The experimental results are very hopeful and confirm the classifications of medieval writings specified by paleographers.