End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i>
The growth of digital libraries has yielded a large number of handwritten historical documents in the form of images, often accompanied by a digital transcription of the content. The ability to track the position of the words of the digital transcription in the images can be important both for the s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/1/17 |
| _version_ | 1797440480706822144 |
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| author | Giuseppe De Gregorio Giuliana Capriolo Angelo Marcelli |
| author_facet | Giuseppe De Gregorio Giuliana Capriolo Angelo Marcelli |
| author_sort | Giuseppe De Gregorio |
| collection | DOAJ |
| description | The growth of digital libraries has yielded a large number of handwritten historical documents in the form of images, often accompanied by a digital transcription of the content. The ability to track the position of the words of the digital transcription in the images can be important both for the study of the document by humanities scholars and for further automatic processing. We propose a learning-free method for automatically aligning the transcription to the document image. The method receives as input the digital image of the document and the transcription of its content and aims at linking the transcription to the corresponding images within the page at the word level. The method comprises two main original contributions: a line-level segmentation algorithm capable of detecting text lines with curved baseline, and a text-to-image alignment algorithm capable of dealing with under- and over-segmentation errors at the word level. Experiments on pages from a 17th-century Italian manuscript have demonstrated that the line segmentation method allows one to segment 92% of the text line correctly. They also demonstrated that it achieves a correct alignment accuracy greater than 68%. Moreover, the performance achieved on widely used data sets compare favourably with the state of the art. |
| first_indexed | 2024-03-09T12:07:49Z |
| format | Article |
| id | doaj.art-8e129c16140045e5a2a5901de3550db9 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T12:07:49Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-8e129c16140045e5a2a5901de3550db92023-11-30T22:55:33ZengMDPI AGJournal of Imaging2313-433X2023-01-01911710.3390/jimaging9010017End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i>Giuseppe De Gregorio0Giuliana Capriolo1Angelo Marcelli2Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, ItalyDepartment of Cultural Heritage, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, ItalyDepartment of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, ItalyThe growth of digital libraries has yielded a large number of handwritten historical documents in the form of images, often accompanied by a digital transcription of the content. The ability to track the position of the words of the digital transcription in the images can be important both for the study of the document by humanities scholars and for further automatic processing. We propose a learning-free method for automatically aligning the transcription to the document image. The method receives as input the digital image of the document and the transcription of its content and aims at linking the transcription to the corresponding images within the page at the word level. The method comprises two main original contributions: a line-level segmentation algorithm capable of detecting text lines with curved baseline, and a text-to-image alignment algorithm capable of dealing with under- and over-segmentation errors at the word level. Experiments on pages from a 17th-century Italian manuscript have demonstrated that the line segmentation method allows one to segment 92% of the text line correctly. They also demonstrated that it achieves a correct alignment accuracy greater than 68%. Moreover, the performance achieved on widely used data sets compare favourably with the state of the art.https://www.mdpi.com/2313-433X/9/1/17historical handwritten document processingtext-line segmentationword segmentationtranscript alignment |
| spellingShingle | Giuseppe De Gregorio Giuliana Capriolo Angelo Marcelli End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i> Journal of Imaging historical handwritten document processing text-line segmentation word segmentation transcript alignment |
| title | End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i> |
| title_full | End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i> |
| title_fullStr | End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i> |
| title_full_unstemmed | End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i> |
| title_short | End-to-End Transcript Alignment of 17th Century Manuscripts: The Case of <i>Moccia Code</i> |
| title_sort | end to end transcript alignment of 17th century manuscripts the case of i moccia code i |
| topic | historical handwritten document processing text-line segmentation word segmentation transcript alignment |
| url | https://www.mdpi.com/2313-433X/9/1/17 |
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