  We address the problem of segmenting and retrieving word images in
collections of historical manuscripts given a text query. This is commonly
referred to as "word spotting". To this end, we first propose an end-to-end
trainable model based on deep neural networks that we dub Ctrl-F-Net. The model
simultaneously generates region proposals and embeds them into a word embedding
space, wherein a search is performed. We further introduce a simplified version
called Ctrl-F-Mini. It is faster with similar performance, though it is limited
to more easily segmented manuscripts. We evaluate both models on common
benchmark datasets and surpass the previous state of the art. Finally, in
collaboration with historians, we employ the Ctrl-F-Net to search within a
large manuscript collection of over 100 thousand pages, written across two
centuries. With only 11 training pages, we enable large scale data collection
in manuscript-based historical research. This results in a speed up of data
collection and the number of manuscripts processed by orders of magnitude.
Given the time consuming manual work required to study old manuscripts in the
humanities, quick and robust tools for word spotting has the potential to
revolutionise domains like history, religion and language.
