A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

Autor:	Molina, Adrià, Gomez, Lluis, Terrades, Oriol Ramos, Lladós, Josep
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Digital Libraries Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. we use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images. Comment: Preprint of paper accepted at DAS2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.04028 Zobrazit plný text záznamu View this record from Arxiv