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Word spotting is a popular tool for supporting the first exploration ofhistoric, handwritten document collections. Today, the best performing methodsrely on machine learning techniques, which require a high amount of annotatedtraining material. As training data is usually not available in the applicationscenario, annotation-free methods aim at solving the retrieval task withoutrepresentative training samples. In this work, we present an annotation-freemethod that still employs machine learning techniques and therefore outperformsother learning-free approaches. The weakly supervised training scheme relies ona lexicon, that does not need to precisely fit the dataset. In combination witha confidence based selection of pseudo-labeled training samples, we achievestate-of-the-art query-by-example performances. Furthermore, our method allowsto perform query-by-string, which is usually not the case for otherannotation-free methods.

Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling