![]() They also note the shortcomings of computer software in recommending matching scores without providing persuasive and satisfactory reasoning for researchers, as the ground truth is itself the subject of study and active research. Firstly, they consider a tool to match handwritings which is applied to documents that are fragmented and collected across tens of libraries. However, as they state, researchers are still left with their traditional tools and limitations, and that is why they propose two new tools designed to address the need for document analysis at scale. In, the authors discuss the recent availability of large-scale digital libraries, where historians and other scientists can find the information they need to help with answering their research questions. 6 concludes the paper with a final discussion. Section 4 covers the inner details of the proposed method. Section 3 present our data cleaning process and image pre-processing tailored to the specific data domain, i.e., ancient documents. Section 2 summarizes prior work on document analysis and digital paleography tools, metric learning, and graphic symbols spotting. Here, we show how automatic detection of symbols can benefit from feature auto-encoding, showing how detection performance improves with respect to trivial template matching. Moreover, the first tool has shown an high number of false positive, which are difficult to filter out. So, the pipeline can control the number of predictions over different types of graphic symbols. In addition, our proposed pipeline implements an algorithm that sorts the objects of a cluster by confidence scores and selects the top match. In contrast, DBSCAN considers only a floor for cluster density and filters noise by identifying those objects that are not contained in any cluster. OPTICS uses the hyper-parameters Ma圎ps, and MinPts almost the same way as DBSCAN, but it distinguishes cluster densities on a more continuous basis. First of all, in this new version of the tool, we rely on OPTICS, instead of DBSCAN, for clustering purposes. The approach proposed in this paper has several differences with the original one. Before this operation, the user is required to decide the binarization threshold from a prepared selection. It detects symbols and reduces noise in the output by clustering the identified symbols. The symbol engine takes images as input, then uses the database objects as queries. Here, the authors have created a graphic symbols database and an identification pipeline to assist the curators. Researchers are then allowed to revise the annotations in order to improve the performance of the tool in the long run.Ī method for symbol detection has already been proposed in the context of the NOTAE project . This paper proposes a system that helps curators to identify potential candidates for different categories of symbols. Unfortunately, this task does not scale up well considering the high number of documents. ![]() ![]() Labeling document pictures with positions of graphic symbols even in an unsupervised manner requires the knowledge of domain experts, paleographers in particular. This task is very different though from processing words and letters in natural language as the symbols that we look for can be orthogonal to the content, making contextual analysis useless. The NOTAE project (NOT A writtEn word but graphic symbols) is meant to study graphic symbols, which were added by authors of these documents with several different meanings. A huge number of historical documents from Late Antiquity to early medieval Europe do exist in public databases. ![]()
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