Cuneiform Stroke Recognition and Vectorization in 2D Images

A vital part of the publication process of ancient cuneiform tablets is creating hand-copies, which are 2D line art representations of the 3D cuneiform clay tablets, created manually by scholars. This research provides an innovative method using Convolutional Neural Networks (CNNs) to identify strokes, the constituent parts of cuneiform characters, and display them as vectors — semi-automatically creating cuneiform hand-copies. This is a major step in optical character recognition (OCR) for cuneiform texts, which would contribute significantly to their digitization and create efficient tools for dealing with the unique challenges of Mesopotamian cultural heritage. Our research has resulted in the successful identification of horizontal strokes in 2D images of cuneiform tablets, some of them from very different periods, separated by hundreds of years from each other. With the Detecto algorithm, we achieved an F-measure of 81.7% and an accuracy of 90.5%. The data and code of the project are available on GitHub.