TY  - JOUR
A1  - Fischer, Norbert
A1  - Hartelt, Alexander
A1  - Puppe, Frank
T1  - Line-level layout recognition of historical documents with background knowledge
T2  - Algorithms
N2  - 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 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.
KW  - layout recognition
KW  - background knowledge
KW  - historical document analysis
KW  - fully convolutional neural networks
KW  - baseline detection
KW  - text line detection
Y1  - 2023
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/31093
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-310938
SN  - 1999-4893
VL  - 16
IS  - 3
ER  -