• DOI:10.1007/978-3-030-57058-3_21
• Corpus ID: 211987831

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

@inproceedings{Wolf2020AnnotationfreeLO,
  title={Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling},
  author={Fabian Wolf and Gernot A. Fink},
  booktitle={International Workshop on Document Analysis Systems},
  year={2020}
}

• Fabian Wolf, G. Fink
• Published in
  International Workshop on…
  4 March 2020
• Computer Science

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.