Is That a Jaguar?: Segmenting Ancient Maya Glyphs via Crowdsourcing.

Crowdsourcing is popular in multimedia research to obtain image annotation and segmentation data at scale. In the context of analysis of cultural heritage materials, we propose a novel crowdsourced task, namely the segmentation of ancient Maya hieroglyph-blocks by non-experts. This is a task that is highly perceptual and thus potentially feasible even though the crowd is not likely to have prior specialized knowledge about hieroglyphics. Based on a new data set of glyph-block line drawings for which ground-truth segmentation exists, we study how non-experts perceive glyph blocks (e.g. whether they see closed contours as a separate glyph, or how they combine visual components under plausible hypotheses of the number of glyphs present in a block.) Using Amazon Mechanical Turk as platform, we perform block-based and worker-based objective analyses to assess the difficulty of glyph blocks and the performance of workers. The results suggest that a crowdsourced approach is promising for glyph-blocks of moderate degrees of complexity.