Authors:
Hussein Mohammed
and
Mahdi Jampour
Affiliation:
Cluster of Excellence, Understanding Written Artefacts, Universität Hamburg, Hamburg, Germany
Keyword(s):
Pattern Detection, Deep Learning, Historical Manuscripts, Datasets.
Abstract:
Historical manuscripts can be challenging for computer vision tasks such as writer identification, style classification and layout analysis due to the degradation of the artefacts themselves and the poor quality of digitization, thereby limiting the scope of analysis. However, recent advances in machine learning have shown promising results in enabling the analysis of vast amounts of data from digitised manuscripts. Nevertheless, the task of detecting patterns in these manuscripts is further complicated by the lack of annotations and the small size of many patterns, which can be smaller than 0.1% of the image size. In this study, we propose to explore the possibility of detecting small patterns in digitised manuscripts using only a few annotated examples. We also propose three detection datasets featuring three types of patterns commonly found in manuscripts: words, seals, and drawings. Furthermore, we employed two state-of-the-art deep learning models on these novel datasets: the FA
STER ResNet and the EfficientDet, along with our general approach for standard evaluations as a baseline for these datasets.
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