# A Recursive Encoding for Cuneiform Signs

arXiv: https://arxiv.org/abs/2406.17283 · HTML: https://mlsanity.com/paper/2406.17283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [Recognizing Cuneiform Signs Using Graph Based Methods](https://mlsanity.com/paper/1802.05908/) (2018)
- [Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1610.04057/) (2016)
- [Investigating Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts](https://mlsanity.com/paper/2009.10794/) (2020)

## Built on by (most cited first, 1 of 1)

- [Zero-Shot SAM2 Segmentation and Vision Transformer-Based Recognition of Elamite Cuneiform Symbols from Degraded Tablet Images](https://mlsanity.com/paper/2608.18544/) (2026)

## Abstract (arXiv)

One of the most significant problems in cuneiform pedagogy is the process of looking up unknown signs, which often involves a tedious page-by-page search through a sign list. This paper proposes a new "recursive encoding" for signs, which represents the arrangement of strokes in a way a computer can process. A series of new algorithms then offers students a new way to look up signs by any distinctive component, as well as providing new ways to render signs and tablets electronically.