Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.24197 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909978708672512 |
|---|---|
| author | Fuentes-Jimenez, David Pizarro, Daniel Hernández, Álvaro Bartoli, Adin Méndez, César Guerra de Diego-Otón, Laura Palazuelos-Cagigas, Sira Zamacona, Carlos Gracia |
| author_facet | Fuentes-Jimenez, David Pizarro, Daniel Hernández, Álvaro Bartoli, Adin Méndez, César Guerra de Diego-Otón, Laura Palazuelos-Cagigas, Sira Zamacona, Carlos Gracia |
| contents | Digital humanities are significantly transforming how Egyptologists study ancient Egyptian texts. The OCR-PT-CT project proposes a recognition method for hieroglyphs based on images of Coffin Texts (CT) from Adriaan de Buck (1935-1961) and Pyramid Texts (PT) from Middle Kingdom coffins (James Allen, 2006). The system identifies hieroglyphs and transcribes them into Gardiner's codes. A web tool organizes them by spells and witnesses, storing the data in CSV format for integration with the MORTEXVAR dataset, which collects Coffin Texts with metadata, transliterations, and translations for research. Recognition has been addressed in two ways: a Mobilenet neural network trained on 140 hieroglyph classes achieved 93.87 \% accuracy but struggled with underrepresented classes. A novel Deep Metric Learning approach improves flexibility for new or data-limited signs, achieving 97.70 \% accuracy and recognizing more hieroglyphs. Due to its superior performance under class imbalance and adaptability, the final system adopts Deep Metric Learning as the default classifier. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The OCR-PT-CT Project: Semi-Automatic Recognition of Ancient Egyptian Hieroglyphs Based on Metric Learning Fuentes-Jimenez, David Pizarro, Daniel Hernández, Álvaro Bartoli, Adin Méndez, César Guerra de Diego-Otón, Laura Palazuelos-Cagigas, Sira Zamacona, Carlos Gracia Image and Video Processing Digital humanities are significantly transforming how Egyptologists study ancient Egyptian texts. The OCR-PT-CT project proposes a recognition method for hieroglyphs based on images of Coffin Texts (CT) from Adriaan de Buck (1935-1961) and Pyramid Texts (PT) from Middle Kingdom coffins (James Allen, 2006). The system identifies hieroglyphs and transcribes them into Gardiner's codes. A web tool organizes them by spells and witnesses, storing the data in CSV format for integration with the MORTEXVAR dataset, which collects Coffin Texts with metadata, transliterations, and translations for research. Recognition has been addressed in two ways: a Mobilenet neural network trained on 140 hieroglyph classes achieved 93.87 \% accuracy but struggled with underrepresented classes. A novel Deep Metric Learning approach improves flexibility for new or data-limited signs, achieving 97.70 \% accuracy and recognizing more hieroglyphs. Due to its superior performance under class imbalance and adaptability, the final system adopts Deep Metric Learning as the default classifier. |
| title | The OCR-PT-CT Project: Semi-Automatic Recognition of Ancient Egyptian Hieroglyphs Based on Metric Learning |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2512.24197 |