Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents
Abstract
This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning. The first approach uses a pre-trained CNN model to cope with the lack of training data, which is fine-tuned to achieve a compact yet discriminant representation of queries and image candidates. The second approach uses a Siamese Convolution Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset to provide the similarity-based feature maps. In both methods, the learned representation scheme considers feature maps of different sizes which are evaluated in terms of retrieval performance. A robust experimental protocol using two public datasets (Tobacoo-800 and DocExplore) has shown that the proposed methods compare favorably against state-of-the-art document image retrieval and pattern spotting methods.
Cite
@article{arxiv.1907.09404,
title = {Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents},
author = {Kelly Lais Wiggers and Alceu de Souza Britto Junior and Alessandro Lameiras Koerich and Laurent Heutte and Luiz Eduardo Soares de Oliveira},
journal= {arXiv preprint arXiv:1907.09404},
year = {2019}
}
Comments
The paper is under consideration at Pattern Recognition Letters