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Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images

Braille character detection helps communication between normal and visually impaired people. The existing Braille detection methods are all aimed at scanning Braille document images while ignoring natural scene Braille images and CNN shining in the field of pattern recognition is rarely used for Bra...

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Detalles Bibliográficos
Autores principales: Lu, Liqiong, Wu, Dong, Xiong, Jianfang, Liang, Zhou, Huang, Faliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377889/
https://www.ncbi.nlm.nih.gov/pubmed/35978899
http://dx.doi.org/10.1155/2022/7201775
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author Lu, Liqiong
Wu, Dong
Xiong, Jianfang
Liang, Zhou
Huang, Faliang
author_facet Lu, Liqiong
Wu, Dong
Xiong, Jianfang
Liang, Zhou
Huang, Faliang
author_sort Lu, Liqiong
collection PubMed
description Braille character detection helps communication between normal and visually impaired people. The existing Braille detection methods are all aimed at scanning Braille document images while ignoring natural scene Braille images and CNN shining in the field of pattern recognition is rarely used for Braille detection. Firstly, a natural scene Braille image data set named NSBD was constructed. Then, an anchor-free Braille character detection based on the edge feature was proposed by analyzing that Braille characters in natural scene images that are relatively small in size, and a Braille character is composed of Braille dots that werelocated at the edge region of Braille character. Finally, the performance of the proposed method and other classic methods based on CNN was compared on NSBD. The experimental results show that the proposed method has good performance.
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spelling pubmed-93778892022-08-16 Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images Lu, Liqiong Wu, Dong Xiong, Jianfang Liang, Zhou Huang, Faliang Comput Intell Neurosci Research Article Braille character detection helps communication between normal and visually impaired people. The existing Braille detection methods are all aimed at scanning Braille document images while ignoring natural scene Braille images and CNN shining in the field of pattern recognition is rarely used for Braille detection. Firstly, a natural scene Braille image data set named NSBD was constructed. Then, an anchor-free Braille character detection based on the edge feature was proposed by analyzing that Braille characters in natural scene images that are relatively small in size, and a Braille character is composed of Braille dots that werelocated at the edge region of Braille character. Finally, the performance of the proposed method and other classic methods based on CNN was compared on NSBD. The experimental results show that the proposed method has good performance. Hindawi 2022-08-08 /pmc/articles/PMC9377889/ /pubmed/35978899 http://dx.doi.org/10.1155/2022/7201775 Text en Copyright © 2022 Liqiong Lu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lu, Liqiong
Wu, Dong
Xiong, Jianfang
Liang, Zhou
Huang, Faliang
Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images
title Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images
title_full Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images
title_fullStr Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images
title_full_unstemmed Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images
title_short Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images
title_sort anchor-free braille character detection based on edge feature in natural scene images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377889/
https://www.ncbi.nlm.nih.gov/pubmed/35978899
http://dx.doi.org/10.1155/2022/7201775
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