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Visualization of Customized Convolutional Neural Network for Natural Language Recognition
For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026827/ https://www.ncbi.nlm.nih.gov/pubmed/35458866 http://dx.doi.org/10.3390/s22082881 |
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author | Singh, Tajinder Pal Gupta, Sheifali Garg, Meenu Gupta, Deepali Alharbi, Abdullah Alyami, Hashem Anand, Divya Ortega-Mansilla, Arturo Goyal, Nitin |
author_facet | Singh, Tajinder Pal Gupta, Sheifali Garg, Meenu Gupta, Deepali Alharbi, Abdullah Alyami, Hashem Anand, Divya Ortega-Mansilla, Arturo Goyal, Nitin |
author_sort | Singh, Tajinder Pal |
collection | PubMed |
description | For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset. |
format | Online Article Text |
id | pubmed-9026827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90268272022-04-23 Visualization of Customized Convolutional Neural Network for Natural Language Recognition Singh, Tajinder Pal Gupta, Sheifali Garg, Meenu Gupta, Deepali Alharbi, Abdullah Alyami, Hashem Anand, Divya Ortega-Mansilla, Arturo Goyal, Nitin Sensors (Basel) Article For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset. MDPI 2022-04-08 /pmc/articles/PMC9026827/ /pubmed/35458866 http://dx.doi.org/10.3390/s22082881 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Singh, Tajinder Pal Gupta, Sheifali Garg, Meenu Gupta, Deepali Alharbi, Abdullah Alyami, Hashem Anand, Divya Ortega-Mansilla, Arturo Goyal, Nitin Visualization of Customized Convolutional Neural Network for Natural Language Recognition |
title | Visualization of Customized Convolutional Neural Network for Natural Language Recognition |
title_full | Visualization of Customized Convolutional Neural Network for Natural Language Recognition |
title_fullStr | Visualization of Customized Convolutional Neural Network for Natural Language Recognition |
title_full_unstemmed | Visualization of Customized Convolutional Neural Network for Natural Language Recognition |
title_short | Visualization of Customized Convolutional Neural Network for Natural Language Recognition |
title_sort | visualization of customized convolutional neural network for natural language recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026827/ https://www.ncbi.nlm.nih.gov/pubmed/35458866 http://dx.doi.org/10.3390/s22082881 |
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