Cuneiform Reading Using Computer Vision Algorithms

Adela Hamplova, David Franc, Josef Pavlicek, Avital Romach, Shai Gordin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


This paper presents a new method for computer-assisted recognition of horizontal strokes in photographs of cuneiform tablets with 90,52 % accuracy. The cuneiform script is the oldest attested writing system in the world, used for over three thousand years throughout the ancient Near East, primarily by the cultures of Mesopotamia (modern Iraq). It was impressed on clay tablets and engraved on stone slabs by writing strokes. Researchers have been trying to speed up the process of reading the tablets using different methods, as manual copying of the tablets and their transliteration is time consuming. This research, therefore, aims to recognize the elementary components, i.e., the strokes, of cuneiform signs from photographs of ancient cuneiform tablets, in order to enable effective OCR using the latest computer vision algorithms. The main difference between other approaches and ours is that we work directly with the two-dimensional photographs, instead of three-dimensional models, as there are many more 2D images available in public online repositories. The goal is to partly automate the process of identifying and reading cuneiform signs, thus speeding up the process of rediscovering these ancient texts and civilizations.

Original languageEnglish
Title of host publicationSPML 2022 - Proceedings of 2022 5th International Conference on Signal Processing and Machine Learning
Number of pages4
ISBN (Electronic)9781450396912
StatePublished - 4 Aug 2022
Event5th International Conference on Signal Processing and Machine Learning, SPML 2022 - Dalian, China
Duration: 4 Aug 20226 Aug 2022

Publication series

NameACM International Conference Proceeding Series


Conference5th International Conference on Signal Processing and Machine Learning, SPML 2022


  • cuneiform
  • logo-syllabic script
  • pattern recognition


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