Computing Similarity Networks of Maya Glyphs from 3D Data
Channel: Recording Archaeology
Date: 16/10/2020

Motivated by the demands for compilation of a text-database and digital dictionary of Classic Mayan (http://mayawoerterbuch.de), we are developing an approach to compute and visual-ize  similarity  of  3D-digitized  Maya  hieroglyphs.  The  glyphs  are  typically  arranged  within  a  squared block consisting of three or four signs arbitrarily deformed and attached to a larger main  sign.  These  signs  vary  in  shape  and  design  considerably,  preventing  an  easy  and  com-plete decipherment. Instead of photographs and manual drawings we compute visualizations of  3D-measurement  data  using  the  GigaMesh  Software  Framework  (https://gigamesh.eu)  to  maximize readability. Those images serve as input for a machine learning pipeline. Maya glyphs and parts thereof are segmented with projection profiles and a random walker approach based on the curvature of the 3D surface. The retrieved sub-glyphs are (i) firstly clustered into a set of common sizes and (ii) secondly clustered hierarchically based on a Histogram of Gradients similarity descriptor. Distances and connections derived from the clusters are visualized in a force-directed network. The results correspond very well to the hitherto manual established sign classifications within the traditional research and enable a critical discussion about glyph sim-ilarity. The resultant quantitative measure of similarity will support the objective compilation, indexing and sorting of about 1.000 known signs based on their morphology. Future work will integrate more recent methods like convolutional neural networks, which is challenging task due to the relative few and short texts distributed over time and geographical area.


Hubert Mara, Bartosz Bogacz, Felix Feldmann, Christian Prager