Ancient Egyptian Hieroglyphs Segmentation and Classification with Convolutional Neural Networks

Barucci, Andrea; Canfailla, Chiara; Cucci, Costanza; Forasassi, Matteo; Franci, Massimiliano; Guarducci, Guido; Guidi, Tommaso; Loschiavo, Marco; Picollo, Marcello; Pini, Roberto; Python, Lorenzo; Valentini, Stefano; Argenti, Fabrizio

doi:10.1007/978-3-031-20302-2_10

Cited by 5 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, pioneering character recognition research on Ancient Greek relied on just 10,000 annotations spanning centuries of texts [1,31]. Studies of Egyptian hieroglyphs used 17,000 computer-generated images rather than true inscriptions [11,32,33]. Cuneiform classification experiments were conducted with only 1000 glyphs [10,34].…”

Section: Training Datasets For Ancient Scriptsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy

Aioanei,

Hunziker-Rodewald,

Klein

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Epigraphy is witnessing a growing integration of artificial intelligence, notably through its subfield of machine learning (ML), especially in tasks like extracting insights from ancient inscriptions. However, scarce labeled data for training ML algorithms severely limits current techniques, especially for ancient scripts like Old Aramaic. Our research pioneers an innovative methodology for generating synthetic training data tailored to Old Aramaic letters. Our pipeline synthesizes photo-realistic Aramaic letter datasets, incorporating textural features, lighting, damage, and augmentations to mimic real-world inscription diversity. Despite minimal real examples, we engineer a dataset of 250 000 training and 25 000 validation images covering the 22 letter classes in the Aramaic alphabet. This comprehensive corpus provides a robust volume of data for training a residual neural network (ResNet) to classify highly degraded Aramaic letters. The ResNet model demonstrates 95% accuracy in classifying real images from the 8th century BCE Hadad statue inscription. Additional experiments validate performance on varying materials and styles, proving effective generalization. Our results validate the model’s capabilities in handling diverse real-world scenarios, proving the viability of our synthetic data approach and avoiding the dependence on scarce training data that has constrained epigraphic analysis. Our innovative framework elevates interpretation accuracy on damaged inscriptions, thus enhancing knowledge extraction from these historical resources.

show abstract

Section: Training Datasets For Ancient Scriptsmentioning

confidence: 99%

“…Some promising applications of deep neural networks have recently emerged for analyzing ancient scripts such as Old Greek, Cuneiform, and Egyptian hieroglyphs [1,10,11]. However, a persistent barrier is the lack of sufficient training examples, with datasets often limited to 10,000s of images.…”

Section: Introductionmentioning

confidence: 99%

Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy

Aioanei,

Hunziker-Rodewald,

Klein

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The deployment of large neural network architectures capable of facing such tasks resulted in a horizontal spread of these technologies across many different fields, such as clinical imaging [12][13][14] or cultural heritage [15]. In the latter field, solutions were proposed, for example, for the recognition of the old Kuzushiji Japanese writing style [16,17], Maya [18] and Egyptian hieroglyphs classification [19,20]. These applications can be a very challenging test for CNNs, due to the complexity of the problem deriving from the artifacts' state of conservation or the high variability of documents [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks

et al. 2023

Self Cite

View full text Add to dashboard Cite

The objective of this work is to show the application of a Deep Learning algorithm able to operate the segmentation of ancient Egyptian hieroglyphs present in an image, with the ambition to be as versatile as possible despite the variability of the image source. The problem is quite complex, the main obstacles being the considerable amount of different classes of existing hieroglyphs, the differences related to the hand of the scribe as well as the great differences among the various supports, such as papyri, stone or wood, where they are written. Furthermore, as in all archaeological finds, damage to the supports are frequent, with the consequence that hieroglyphs can be partially corrupted. In order to face this challenging problem, we leverage on the well-known Detectron2 platform, developed by the Facebook AI Research Group, focusing on the Mask R-CNN architecture to perform segmentation of image instances. Likewise, for several machine learning studies, one of the hardest challenges is the creation of a suitable dataset. In this paper, we will describe a hieroglyph dataset that has been created for the purpose of segmentation, highlighting its pros and cons, and the impact of different hyperparameters on the final results. Tests on the segmentation of images taken from public databases will also be presented and discussed along with the limitations of our study.

show abstract