Line-level layout recognition of historical documents with background knowledge
Please always quote using this URN: urn:nbn:de:bvb:20-opus-310938
- Digitization and transcription of historic documents offer new research opportunities for humanists and are the topics of many edition projects. However, manual work is still required for the main phases of layout recognition and the subsequent optical character recognition (OCR) of early printed documents. This paper describes and evaluates how deep learning approaches recognize text lines and can be extended to layout recognition using background knowledge. The evaluation was performed on five corpora of early prints from the 15th and 16thDigitization and transcription of historic documents offer new research opportunities for humanists and are the topics of many edition projects. However, manual work is still required for the main phases of layout recognition and the subsequent optical character recognition (OCR) of early printed documents. This paper describes and evaluates how deep learning approaches recognize text lines and can be extended to layout recognition using background knowledge. The evaluation was performed on five corpora of early prints from the 15th and 16th Centuries, representing a variety of layout features. While the main text with standard layouts could be recognized in the correct reading order with a precision and recall of up to 99.9%, also complex layouts were recognized at a rate as high as 90% by using background knowledge, the full potential of which was revealed if many pages of the same source were transcribed.…
Author: | Norbert Fischer, Alexander Hartelt, Frank Puppe |
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URN: | urn:nbn:de:bvb:20-opus-310938 |
Document Type: | Journal article |
Faculties: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Language: | English |
Parent Title (English): | Algorithms |
ISSN: | 1999-4893 |
Year of Completion: | 2023 |
Volume: | 16 |
Issue: | 3 |
Article Number: | 136 |
Source: | Algorithms (2023) 16:3, 136. https://doi.org/10.3390/a16030136 |
DOI: | https://doi.org/10.3390/a16030136 |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Tag: | background knowledge; baseline detection; fully convolutional neural networks; historical document analysis; layout recognition; text line detection |
Release Date: | 2024/03/07 |
Date of first Publication: | 2023/03/03 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |