A Hybrid Deep Learning Approach to Keyword Spotting in Vietnamese Stele Images

被引:0
|
作者
Scius-Bertrand A. [1 ]
Bui M. [2 ]
Fischer A. [1 ]
机构
[1] University of Fribourg and HES-SO, Fribourg
[2] Ecole Pratique des Hautes Etudes, Paris
来源
Informatica (Slovenia) | 2023年 / 47卷 / 03期
关键词
annotation-free; Chu Nom; document image analysis; Hausdorff edit distance; hybrid deep learning; keyword spotting; Vietnamese steles;
D O I
10.31449/inf.v47i3.4785
中图分类号
学科分类号
摘要
In order to access the rich cultural heritage conveyed in Vietnamese steles, automatic reading of stone engravings would be a great support for historians, who are analyzing tens of thousands of stele images. Approaching the challenging problem with deep learning alone is difficult because the data-driven models require large representative datasets with expert human annotations, which are not available for the steles and costly to obtain. In this article, we present a hybrid approach to spot keywords in stele images that combines data-driven deep learning with knowledge-based structural modeling and matching of Chu Nom characters. The main advantage of the proposed method is that it is annotation-free, i.e. no human data annotation is required. In an experimental evaluation, we demonstrate that keywords can be successfully spotted with a mean average precision of more than 70% when a single engraving style is considered. © 2023 Slovene Society Informatika. All rights reserved.
引用
收藏
页码:361 / 372
页数:11
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