HTR e papiri opportunità e prospettive
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| Format: | Recurso digital |
| Sprache: | Italienisch |
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Zenodo
2024
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| _version_ | 1866902104310808576 |
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| author | Boschetti, Federico |
| author_facet | Boschetti, Federico |
| contents | <p>Over the past two decades, techniques for acquiring digital text from images have achieved very high performance, not only for printed texts but also for manuscripts. In the case of Ancient Greek, the results obtained on printed materials are now comparable to those available for modern languages, while the first significant advances are beginning to emerge for handwritten documents as well. Against this backdrop, this contribution presents the development and integration of various text recognition techniques – OCR (Optical Character Recognition), HTR (Handwritten Text Recognition), ICR (Intelligent Character Recognition), and ATR (Automatic Text Recognition) – within collaborative scholarly editing platforms. The aim is to enhance the accuracy of digitizing ancient texts and complex manuscripts, thereby opening new perspectives for the creation of digital critical editions and for the collaborative sharing of results within the international scholarly community.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16921693 |
| institution | Zenodo |
| language | ita |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HTR e papiri opportunità e prospettive Boschetti, Federico Handwritten Text Recognition Optical Character Recognition Digital humanities Digital Scholarly Editing Digital Classics <p>Over the past two decades, techniques for acquiring digital text from images have achieved very high performance, not only for printed texts but also for manuscripts. In the case of Ancient Greek, the results obtained on printed materials are now comparable to those available for modern languages, while the first significant advances are beginning to emerge for handwritten documents as well. Against this backdrop, this contribution presents the development and integration of various text recognition techniques – OCR (Optical Character Recognition), HTR (Handwritten Text Recognition), ICR (Intelligent Character Recognition), and ATR (Automatic Text Recognition) – within collaborative scholarly editing platforms. The aim is to enhance the accuracy of digitizing ancient texts and complex manuscripts, thereby opening new perspectives for the creation of digital critical editions and for the collaborative sharing of results within the international scholarly community.</p> |
| title | HTR e papiri opportunità e prospettive |
| topic | Handwritten Text Recognition Optical Character Recognition Digital humanities Digital Scholarly Editing Digital Classics |
| url | https://doi.org/10.5281/zenodo.16921693 |