Visualizing Networks of Maya Glyphs by Clustering Subglyphs

Abstract

Deciphering the Maya writing is an ongoing process that has already started in the early 19th century. Among the reasons why Maya hieroglyphic script and language are still undeciphered are inexpertly-created drawings of Maya writing systems resulting in a large number of misinterpretations concerning the contents of these glyphs. As a consequence, the decipherment of Maya writing systems has experienced several setbacks. Modern research in the domain of cultural heritage requires a maximum amount of precision in capturing and analyzing artifacts so that scholars can work on - preferably - unmodified data as much as possible. This work presents an approach to visualize similar Maya glyphs and parts thereof and enable discovering novel connections between glyphs based on a machine learning pipeline. The algorithm is demonstrated on 3D scans from sculptured monuments, which have been filtered using a Multiscale Integral Invariant Filter (MSII) and then projected as a 2D image. Maya glyphs are segmented from 2D images using projection profiles to generate a grid of columns and rows. Then, the glyphs themselves are segmented using the random walker approach, where background and foreground is separated based on the surface curvature of the original 3D surface. The retrieved subglyphs are first clustered by their sizes into a set of common sizes. For each glyph a feature vector based on Histogram of Gradients (HOG) is computed and used for a subsequent hierarchical clustering. The resultant clusters of glyph parts are used to discover and visualize connections between glyphs using a force directed network layout.