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Preparing multi-layered visualisations of Old Babylonian cuneiform tablets for a machine learning OCR training model towards automated sign recognition

(2024) IT-INFORMATION TECHNOLOGY. 65(6). p.229-242
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Abstract
In the framework of the CUNE-IIIF-ORM project the aim is to train an Artificial Intelligence Optical Character Recognition (AI-OCR) model that can automatically locate and identify cuneiform signs on photorealistic representations of Old Babylonian texts (c. 2000-1600 B.C.E.). In order to train the model, c. 200 documentary clay tablets have been selected. They are manually annotated by specialist cuneiformists on a set of 12 still raster images generated from interactive Multi-Light Reflectance images. This image set includes visualisations with varying light angles and simplifications based on the dept information on the impressed signs in the surface. In the Cuneur Cuneiform Annotator, a Gitlab-based web application, the identified cuneiform signs are annotated with polygons and enriched with metadata. This methodology builds a qualitative annotated training corpus of approximately 20,000 cropped signs (i.e. 240,000 visualizations), all with their unicode codepoint and conventional sign name. It will act as a multi-layerd core dataset for the further development and fine-tuning of a machine learning OCR training model for the Old Babylonian cuneiform script. This paper discusses how the physical nature of handwritten inscribed Old Babylonian documentary clay tablets challenges the annotation and metadating task, and how these have been addressed within the CUNE-IIIF-ORM project to achieve an effective training corpus to support the training of a machine learning OCR model.ACM CCS Applied computing -> Document management and text processing -> Document capture -> Optical character recognition; Applied computing -> Arts and humanities -> Language translation.
Keywords
handwritten text recognition (HTR), optical character recognition (OCR), data reuse, machine learning training data, cuneiform, Old Babylonian

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MLA
Hameeuw, Hendrik, et al. “Preparing Multi-Layered Visualisations of Old Babylonian Cuneiform Tablets for a Machine Learning OCR Training Model towards Automated Sign Recognition.” IT-INFORMATION TECHNOLOGY, vol. 65, no. 6, 2024, pp. 229–42, doi:10.1515/itit-2023-0063.
APA
Hameeuw, H., De Graef, K., Smidt, G. R., Goddeeris, A., Homburg, T., & Thirukokaranam Chandrasekar, K. K. (2024). Preparing multi-layered visualisations of Old Babylonian cuneiform tablets for a machine learning OCR training model towards automated sign recognition. IT-INFORMATION TECHNOLOGY, 65(6), 229–242. https://doi.org/10.1515/itit-2023-0063
Chicago author-date
Hameeuw, Hendrik, Katrien De Graef, Gustav Ryberg Smidt, Anne Goddeeris, Timo Homburg, and Krishna Kumar Thirukokaranam Chandrasekar. 2024. “Preparing Multi-Layered Visualisations of Old Babylonian Cuneiform Tablets for a Machine Learning OCR Training Model towards Automated Sign Recognition.” IT-INFORMATION TECHNOLOGY 65 (6): 229–42. https://doi.org/10.1515/itit-2023-0063.
Chicago author-date (all authors)
Hameeuw, Hendrik, Katrien De Graef, Gustav Ryberg Smidt, Anne Goddeeris, Timo Homburg, and Krishna Kumar Thirukokaranam Chandrasekar. 2024. “Preparing Multi-Layered Visualisations of Old Babylonian Cuneiform Tablets for a Machine Learning OCR Training Model towards Automated Sign Recognition.” IT-INFORMATION TECHNOLOGY 65 (6): 229–242. doi:10.1515/itit-2023-0063.
Vancouver
1.
Hameeuw H, De Graef K, Smidt GR, Goddeeris A, Homburg T, Thirukokaranam Chandrasekar KK. Preparing multi-layered visualisations of Old Babylonian cuneiform tablets for a machine learning OCR training model towards automated sign recognition. IT-INFORMATION TECHNOLOGY. 2024;65(6):229–42.
IEEE
[1]
H. Hameeuw, K. De Graef, G. R. Smidt, A. Goddeeris, T. Homburg, and K. K. Thirukokaranam Chandrasekar, “Preparing multi-layered visualisations of Old Babylonian cuneiform tablets for a machine learning OCR training model towards automated sign recognition,” IT-INFORMATION TECHNOLOGY, vol. 65, no. 6, pp. 229–242, 2024.
@article{01HK765PMHSM7ZKEB1XF4FA5K4,
  abstract     = {{In the framework of the CUNE-IIIF-ORM project the aim is to train an Artificial Intelligence Optical Character Recognition (AI-OCR) model that can automatically locate and identify cuneiform signs on photorealistic representations of Old Babylonian texts (c. 2000-1600 B.C.E.). In order to train the model, c. 200 documentary clay tablets have been selected. They are manually annotated by specialist cuneiformists on a set of 12 still raster images generated from interactive Multi-Light Reflectance images. This image set includes visualisations with varying light angles and simplifications based on the dept information on the impressed signs in the surface. In the Cuneur Cuneiform Annotator, a Gitlab-based web application, the identified cuneiform signs are annotated with polygons and enriched with metadata. This methodology builds a qualitative annotated training corpus of approximately 20,000 cropped signs (i.e. 240,000 visualizations), all with their unicode codepoint and conventional sign name. It will act as a multi-layerd core dataset for the further development and fine-tuning of a machine learning OCR training model for the Old Babylonian cuneiform script. This paper discusses how the physical nature of handwritten inscribed Old Babylonian documentary clay tablets challenges the annotation and metadating task, and how these have been addressed within the CUNE-IIIF-ORM project to achieve an effective training corpus to support the training of a machine learning OCR model.ACM CCS Applied computing -> Document management and text processing -> Document capture -> Optical character recognition; Applied computing -> Arts and humanities -> Language translation.}},
  author       = {{Hameeuw, Hendrik and De Graef, Katrien and Smidt, Gustav Ryberg and Goddeeris, Anne and Homburg, Timo and Thirukokaranam Chandrasekar, Krishna Kumar}},
  issn         = {{1611-2776}},
  journal      = {{IT-INFORMATION TECHNOLOGY}},
  keywords     = {{handwritten text recognition (HTR),optical character recognition (OCR),data reuse,machine learning training data,cuneiform,Old Babylonian}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{229--242}},
  title        = {{Preparing multi-layered visualisations of Old Babylonian cuneiform tablets for a machine learning OCR training model towards automated sign recognition}},
  url          = {{http://doi.org/10.1515/itit-2023-0063}},
  volume       = {{65}},
  year         = {{2024}},
}

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