While academics in archaeology and epigraphy have made formidable efforts over 100 years to decipher the writings of the Ancient Maya culture, located in a variety of places in Mexico and Central America, and imprinted in multiple media types and artifacts, a significant proportion of the Maya hieroglyphic corpus remains open for scholarly interpretation. This is a long-standing goal that can be accelerated by the rapid progress of computer science, and more specifically of multimedia analysis and information management methods, which today are capable, with varying degrees of success, of organizing, analyzing, and visualizing visual and multimedia collections.
The aim of this bi-disciplinary project is to tightly integrate the work of Maya epigraphists and computer scientists to (1) jointly design, develop, and assess new computational tools that robustly and effectively support the work of Maya hieroglyphics experts; (2) advance the state of Maya epigraphy through the combination of expert knowledge and the advanced use of these tools; and (3) make these new resources and knowledge available to the scholar community through the creation of an online system (which to our knowledge would be one of a kind) that would allow for search, comparison, annotation, and visualization tasks as part of new investigations worldwide.
Our project takes a holistic view to this problem, acknowledging that scholar and technological progress needs to be made at various levels (including data preparation and modeling; additional work in epigraphy; semi-automated visual processing; pattern analysis; and advanced forms of information access and visualization) to achieve qualitative shifts towards deciphering of known (and yet to be discovered) hieroglyphic vestiges of the Maya civilization. At the same time, the computational methodologies that will be investigated in the project are based on principled approaches (probabilistic machine learning, computer vision, and information retrieval), and so the resulting technologies will be applicable to other areas in multimedia retrieval.