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Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN
The semantic information presents in the scene images may be the useful information for the viewers who is searching for a specific location or any specific shop and address. This type of information can also be useful in licenseplate detection, controlling the vehicle on the road, robot navigation,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340934/ http://dx.doi.org/10.1007/978-3-030-51935-3_26 |
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author | Islam, Rashedul Islam, Md Rafiqul Talukder, Kamrul Hasan |
author_facet | Islam, Rashedul Islam, Md Rafiqul Talukder, Kamrul Hasan |
author_sort | Islam, Rashedul |
collection | PubMed |
description | The semantic information presents in the scene images may be the useful information for the viewers who is searching for a specific location or any specific shop and address. This type of information can also be useful in licenseplate detection, controlling the vehicle on the road, robot navigation, and assisting visually impaired persons. An efficient method is presented in this paper to detect and extract Bangla texts from scene images based on a connected component approach along with rule-based filtering and vertical scanning scheme. Next, extracted characters are recognized by using Convolutional Neural Network (CNN). The method consists of the four basic consecutive steps such as detection and extraction of the Region of Interest (ROI), segmentation of the words, extraction of characters, and recognition of the extracted characters. After extracting the ROI from the input image, connected component(CC) analysis and bounding box technology are used for segmentation of Bangla words. To separate and extract Bangla characters from the segmented Bangla words, vertical scanning based method along with a dynamic threshold value has been applied. Finally, character recognition is carried out using CNN. The proposed algorithm is applied to 600 scene images of different writing styles and colors, and we have obtained 89.25% accuracy in text detection and 94.50% accuracy in the extraction of characters. We have achieved an accuracy of 99.30% and 95.76% in recognition of Bangla digits and characters respectively. By combining both the digits and characters, obtained recognition accuracy is 95.39%. |
format | Online Article Text |
id | pubmed-7340934 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73409342020-07-08 Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN Islam, Rashedul Islam, Md Rafiqul Talukder, Kamrul Hasan Image and Signal Processing Article The semantic information presents in the scene images may be the useful information for the viewers who is searching for a specific location or any specific shop and address. This type of information can also be useful in licenseplate detection, controlling the vehicle on the road, robot navigation, and assisting visually impaired persons. An efficient method is presented in this paper to detect and extract Bangla texts from scene images based on a connected component approach along with rule-based filtering and vertical scanning scheme. Next, extracted characters are recognized by using Convolutional Neural Network (CNN). The method consists of the four basic consecutive steps such as detection and extraction of the Region of Interest (ROI), segmentation of the words, extraction of characters, and recognition of the extracted characters. After extracting the ROI from the input image, connected component(CC) analysis and bounding box technology are used for segmentation of Bangla words. To separate and extract Bangla characters from the segmented Bangla words, vertical scanning based method along with a dynamic threshold value has been applied. Finally, character recognition is carried out using CNN. The proposed algorithm is applied to 600 scene images of different writing styles and colors, and we have obtained 89.25% accuracy in text detection and 94.50% accuracy in the extraction of characters. We have achieved an accuracy of 99.30% and 95.76% in recognition of Bangla digits and characters respectively. By combining both the digits and characters, obtained recognition accuracy is 95.39%. 2020-06-05 /pmc/articles/PMC7340934/ http://dx.doi.org/10.1007/978-3-030-51935-3_26 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Islam, Rashedul Islam, Md Rafiqul Talukder, Kamrul Hasan Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN |
title | Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN |
title_full | Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN |
title_fullStr | Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN |
title_full_unstemmed | Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN |
title_short | Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN |
title_sort | extraction and recognition of bangla texts from natural scene images using cnn |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340934/ http://dx.doi.org/10.1007/978-3-030-51935-3_26 |
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