A segmentation-free method for image retrieval and pattern spotting in historical documents using convolutional features

This paper describes a new segmentation-free method for retrieving images and spotting patterns in historical document image collections. The proposed method needs no training on the target domain, characterizing a problem-independent approach. For this purpose, the query and the document image repr...

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Veröffentlicht in:	Multimedia tools and applications Jg. 84; H. 32; S. 39635 - 39665
Hauptverfasser:	Curi, Zacarias, Nicolas, Stéphane, Tranouez, Pierrick, M. Saavedra, Jose, de Souza Britto Jr, Alceu, Heutte, Laurent
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2025 Springer Nature B.V Springer Verlag
Schlagworte:	1250: Heritage Preservation in the Digital Age: Advances in Machine Learning Artificial Intelligence Computer Communication Networks Computer Science Computer Vision and Pattern Recognition Cross correlation Data Structures and Information Theory Datasets Deep learning Digitization Document and Text Processing Documents Feature maps Human-Machine Interaction Image retrieval Image segmentation Logos Metadata Monomodal and Multimodal Processing Multimedia Information Systems Queries Retrieval Special Purpose and Application-Based Systems Document retrieval Pattern spotting Fully-convolutional activation features Cross-correlation Historical documents Fully-convolutional Networks
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
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