  Word spotting is a popular tool for supporting the first exploration of
historic, handwritten document collections. Today, the best performing methods
rely on machine learning techniques, which require a high amount of annotated
training material. As training data is usually not available in the application
scenario, annotation-free methods aim at solving the retrieval task without
representative training samples. In this work, we present an annotation-free
method that still employs machine learning techniques and therefore outperforms
other learning-free approaches. The weakly supervised training scheme relies on
a lexicon, that does not need to precisely fit the dataset. In combination with
a confidence based selection of pseudo-labeled training samples, we achieve
state-of-the-art query-by-example performances. Furthermore, our method allows
to perform query-by-string, which is usually not the case for other
annotation-free methods.
