International Conference on Frontiers in Handwriting Recognition
Cuneiform scripts constitute an immense source of information about ancient history, dating back almost four thousand years. Documents were written by imprinting wedgeshaped impressions into wet clay tablets, and current scholarly practice typically transcribes the resulting markings by hand with ink on paper. This work develops algorithmic methods for cuneiform script, combining feature extraction for cuneiform wedges with prior work on segmentation-free word spotting using part-structured models. We adapt the inkball model used for word spotting to treat wedge features as individual parts arranged in a tree structure. The geometric relationship between query and target is measured by the energy necessary to deform the tree structure. We also introduce an optimizing method for wedge feature extraction based on optimally assigning tablet structuring elements to hypothesized wedge models. Finally, we evaluate the method on a real-world dataset, and show that it outperforms the state of the art in cuneiform character spotting.
Cuneiform script, Word spotting, Symbol spotting, Spatial pattern recognition, Part structured models, Optimal assignment, Feature extraction, Gaussian mixture models, Hidden Markov models
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
“Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.”
Bogacz, Bartosz; Howe, Nicholas; and Mara, Mara, "Segmentation-Free Spotting of Cuneiform using Part-Structured Models" (2016). Computer Science: Faculty Publications, Smith College, Northampton, MA.