论文标题
用于历史文档收集日期估计的通用图像检索方法
A Generic Image Retrieval Method for Date Estimation of Historical Document Collections
论文作者
论文摘要
历史文档图像的日期估计是一个具有挑战性的问题，文献中有几项贡献缺乏从一个数据集推广到其他数据集的能力。本文以检索方法为基础，在异质收集面前概述了一个健壮的日期估计系统。我们使用名为Smooth-NDCG的排名损失函数来培训一个卷积神经网络，该网络了解每个问题的文档顺序。提出的方法的主要用法之一是作为历史上下文检索的工具。这意味着学者可以根据生产的时期对大数据集的历史图像进行比较分析。我们从手稿和报纸图像的实际数据集中对不同类型的文档进行实验评估。
Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others.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.
