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Authors: Clément Peyrard 1 ; Franck Mamalet 2 and Christophe Garcia 3

Affiliations: 1 Orange Labs and INSA Lyon, France ; 2 Orange Labs, France ; 3 INSA Lyon, France

ISBN: 978-989-758-089-5

Keyword(s): Super-Resolution, Text Image, Multi-Layer Perceptron, Convolutional Neural Network, OCR.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing

Abstract: We compare the performances of several Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (ConvNets) for single text image Super-Resolution. We propose an example-based framework for both MLP and ConvNet, where a non-linear mapping between pairs of patches and high-frequency pixel values is learned. We then demonstrate that for equivalent complexity, ConvNets are better than MLPs at predicting missing details in upsampled text images. To evaluate the performances, we make use of a recent database (ULR-textSISR-2013a) along with different quality measures. We show that the proposed methods outperforms sparse coding-based methods for this database.

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Paper citation in several formats:
Peyrard, C.; Mamalet, F. and Garcia, C. (2015). A Comparison between Multi-Layer Perceptrons and Convolutional Neural Networks for Text Image Super-Resolution.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 84-91. DOI: 10.5220/0005297200840091

@conference{visapp15,
author={Clément Peyrard. and Franck Mamalet. and Christophe Garcia.},
title={A Comparison between Multi-Layer Perceptrons and Convolutional Neural Networks for Text Image Super-Resolution},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={84-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005297200840091},
isbn={978-989-758-089-5},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - A Comparison between Multi-Layer Perceptrons and Convolutional Neural Networks for Text Image Super-Resolution
SN - 978-989-758-089-5
AU - Peyrard, C.
AU - Mamalet, F.
AU - Garcia, C.
PY - 2015
SP - 84
EP - 91
DO - 10.5220/0005297200840091

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