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

• URL: http://arxiv.org/abs/2003.01989v4
• Date: Mon, 25 May 2020 08:58:17 GMT
• Title: Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling
• Authors: Fabian Wolf and Gernot A. Fink
• Abstract summary: We present an annotation-free method that still employs machine learning techniques. We achieve state-of-the-art query-by-example performances. Our method allows to perform query-by-string, which is usually not the case for other annotation-free methods.
• Score: 4.111899441919165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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