  In this paper, we approach the problem of segmentation-free query-by-string
word spotting for handwritten documents. In other words, we use methods
inspired from computer vision and machine learning to search for words in large
collections of digitized manuscripts. In particular, we are interested in
historical handwritten texts, which are often far more challenging than modern
printed documents. This task is important, as it provides people with a way to
quickly find what they are looking for in large collections that are tedious
and difficult to read manually. To this end, we introduce an end-to-end
trainable model based on deep neural networks that we call Ctrl-F-Net. Given a
full manuscript page, the model simultaneously generates region proposals, and
embeds these into a distributed word embedding space, where searches are
performed. We evaluate the model on common benchmarks for handwritten word
spotting, outperforming the previous state-of-the-art segmentation-free
approaches by a large margin, and in some cases even segmentation-based
approaches. One interesting real-life application of our approach is to help
historians to find and count specific words in court records that are related
to women's sustenance activities and division of labor. We provide promising
preliminary experiments that validate our method on this task.
