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This paper describes two approaches for content-based image retrieval andpattern spotting in document images using deep learning. The first approachuses a pre-trained CNN model to cope with the lack of training data, which isfine-tuned to achieve a compact yet discriminant representation of queries andimage candidates. The second approach uses a Siamese Convolution Neural Networktrained on a previously prepared subset of image pairs from the ImageNetdataset to provide the similarity-based feature maps. In both methods, thelearned representation scheme considers feature maps of different sizes whichare evaluated in terms of retrieval performance. A robust experimental protocolusing two public datasets (Tobacoo-800 and DocExplore) has shown that theproposed methods compare favorably against state-of-the-art document imageretrieval and pattern spotting methods.

Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents