OcrV1, Main, Exploration, bibRecord, 000025

A Comparison between Multi-Layer Perceptrons and Convolutional Neural Networks for Text Image Super-Resolution

Identifieur interne : 000025 ( Main/Exploration ); précédent : 000024; suivant : 000026

A Comparison between Multi-Layer Perceptrons and Convolutional Neural Networks for Text Image Super-Resolution

Auteurs : Clément Peyrard [France] ; Franck Mamalet [France] ; Christophe Garcia [France]

Source :

RBID : Hal:hal-01260671

English descriptors

mix :
- Convolutional Neural Network, Multi-Layer Perceptron, OCR, Text Image, Super-Resolution.

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.

Url:

https://hal.archives-ouvertes.fr/hal-01260671

DOI: 10.5220/0005297200840091

Affiliations:

Links toward previous steps (curation, corpus...)

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to stream Main, to step Merge: 000025
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Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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A Comparison between Multi-Layer Perceptrons and Convolutional Neural Networks for Text Image Super-Resolution

A Comparison between Multi-Layer Perceptrons and Convolutional Neural Networks for Text Image Super-Resolution

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri