  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.
