  The study of Maya hieroglyphic writing unlocks the rich history of cultural
and societal knowledge embedded within this ancient civilization's visual
narrative. Artificial Intelligence (AI) offers a novel lens through which we
can translate these inscriptions, with the potential to allow non-specialists
access to reading these texts and to aid in the decipherment of those
hieroglyphs which continue to elude comprehensive interpretation. Toward this,
we leverage a foundational model to segment Maya hieroglyphs from an
open-source digital library dedicated to Maya artifacts. Despite the initial
promise of publicly available foundational segmentation models, their
effectiveness in accurately segmenting Maya hieroglyphs was initially limited.
Addressing this challenge, our study involved the meticulous curation of image
and label pairs with the assistance of experts in Maya art and history,
enabling the fine-tuning of these foundational models. This process
significantly enhanced model performance, illustrating the potential of
fine-tuning approaches and the value of our expanding dataset. We plan to
open-source this dataset for encouraging future research, and eventually to
help make the hieroglyphic texts legible to a broader community, particularly
for Maya heritage community members.
