A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

被引:1
|
作者
Molina, Adria [1 ]
Gomez, Lluis
Ramos Terrades, Oriol
Llados, Josep
机构
[1] Univ Autonoma Barcelona, Comp Vis Ctr, Bellaterra, Catalunya, Spain
来源
DOCUMENT ANALYSIS SYSTEMS, DAS 2022 | 2022年 / 13237卷
关键词
Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG;
D O I
10.1007/978-3-031-06555-2_39
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others.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.
引用
收藏
页码:583 / 597
页数:15
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