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Authors: Omar Mohammed 1 ; Gérard Bailly 2 and Damien Pellier 3

Affiliations: 1 Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France, Univ. Grenoble Alpes, CNRS, LIG, 38000 Grenoble and France ; 2 Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble and France ; 3 Univ. Grenoble Alpes, CNRS, LIG, 38000 Grenoble and France

ISBN: 978-989-758-350-6

ISSN: 2184-433X

Keyword(s): Generative Models, Deep Learning, Online Handwriting, Style Extraction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: How can we learn, transfer and extract handwriting styles using deep neural networks? This paper explores these questions using a deep conditioned autoencoder on the IRON-OFF handwriting data-set. We perform three experiments that systematically explore the quality of our style extraction procedure. First, We compare our model to handwriting benchmarks using multidimensional performance metrics. Second, we explore the quality of style transfer, i.e. how the model performs on new, unseen writers. In both experiments, we improve the metrics of state of the art methods by a large margin. Lastly, we analyze the latent space of our model, and we show that it separates consistently writing styles.

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Paper citation in several formats:
Mohammed, O.; Bailly, G. and Pellier, D. (2019). Transfer and Extraction of the Style of Handwritten Letters using Deep Learning.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, ISSN 2184-433X, pages 677-684. DOI: 10.5220/0007388606770684

@conference{icaart19,
author={Omar Mohammed. and Gérard Bailly. and Damien Pellier.},
title={Transfer and Extraction of the Style of Handwritten Letters using Deep Learning},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={677-684},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007388606770684},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Transfer and Extraction of the Style of Handwritten Letters using Deep Learning
SN - 978-989-758-350-6
AU - Mohammed, O.
AU - Bailly, G.
AU - Pellier, D.
PY - 2019
SP - 677
EP - 684
DO - 10.5220/0007388606770684

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