  The indexing and searching of historical documents have garnered attention in
recent years due to massive digitization efforts of important collections
worldwide. Pure textual search in these corpora is a problem since optical
character recognition (OCR) is infamous for performing poorly on such
historical material, which often suffer from poor preservation. We propose a
novel text-based method for searching through noisy text. Our system represents
words as vectors, projects queries and candidates obtained from the OCR into a
common space, and ranks the candidates using a metric suited to
nearest-neighbor search. We demonstrate the practicality of our method on
typewritten German documents from the WWII era.
