  This paper presents a deep learning approach for image retrieval and pattern
spotting in digital collections of historical documents. First, a region
proposal algorithm detects object candidates in the document page images. Next,
deep learning models are used for feature extraction, considering two distinct
variants, which provide either real-valued or binary code representations.
Finally, candidate images are ranked by computing the feature similarity with a
given input query. A robust experimental protocol evaluates the proposed
approach considering each representation scheme (real-valued and binary code)
on the DocExplore image database. The experimental results show that the
proposed deep models compare favorably to the state-of-the-art image retrieval
approaches for images of historical documents, outperforming other deep models
by 2.56 percentage points using the same techniques for pattern spotting.
Besides, the proposed approach also reduces the search time by up to 200x and
the storage cost up to 6,000x when compared to related works based on
real-valued representations.
