Characterization and recognition of handwritten digits using Julia Article Swipe
Md. Asifuzzaman Jishan
,
Md Shahabub Alam
,
A. K. M. Tauhidul Islam
,
Imran R. Mazumder
,
Khan Raqib Mahmud
,
Abul Kalam Al Azad
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.11994
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.11994
Automatic image and digit recognition is a computationally challenging task for image processing and pattern recognition, requiring an adequate appreciation of the syntactic and semantic importance of the image for the identification ofthe handwritten digits. Image and Pattern Recognition has been identified as one of the driving forces in the research areas because of its shifting of different types of applications, such as safety frameworks, clinical frameworks, diversion, and so on.In this study, for recognition, we implemented a hybrid neural network model that is capable of recognizing the digit of MNISTdataset and achieved a remarkable result. The proposed neural model network can extract features from the image and recognize the features in the layer by layer. To expand, it is so important for the neural network to recognize how the proposed modelcan work in each layer, how it can generate output, and so on. Besides, it also can recognize the auto-encoding system and the variational auto-encoding system of the MNIST dataset. This study will explore those issues that are discussed above, and the explanation for them, and how this phenomenon can be overcome.
Related Topics
Concepts
MNIST database
Computer science
Artificial intelligence
Identification (biology)
Artificial neural network
Encoding (memory)
Pattern recognition (psychology)
Image (mathematics)
Task (project management)
Layer (electronics)
Numerical digit
Digit recognition
Arithmetic
Engineering
Mathematics
Chemistry
Biology
Botany
Organic chemistry
Systems engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.11994
- https://arxiv.org/pdf/2102.11994
- OA Status
- green
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3131507766
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3131507766Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.11994Digital Object Identifier
- Title
-
Characterization and recognition of handwritten digits using JuliaWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-24Full publication date if available
- Authors
-
Md. Asifuzzaman Jishan, Md Shahabub Alam, A. K. M. Tauhidul Islam, Imran R. Mazumder, Khan Raqib Mahmud, Abul Kalam Al AzadList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.11994Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2102.11994Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2102.11994Direct OA link when available
- Concepts
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MNIST database, Computer science, Artificial intelligence, Identification (biology), Artificial neural network, Encoding (memory), Pattern recognition (psychology), Image (mathematics), Task (project management), Layer (electronics), Numerical digit, Digit recognition, Arithmetic, Engineering, Mathematics, Chemistry, Biology, Botany, Organic chemistry, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 21, 27, 30, 45, 49, 87, 105, 109, 112, 123, 129, 149, 153, 158, 172 |
| abstract_inverted_index.This | 161 |
| abstract_inverted_index.also | 146 |
| abstract_inverted_index.been | 40 |
| abstract_inverted_index.each | 134 |
| abstract_inverted_index.from | 104 |
| abstract_inverted_index.such | 61 |
| abstract_inverted_index.task | 9 |
| abstract_inverted_index.that | 82, 167 |
| abstract_inverted_index.this | 71, 178 |
| abstract_inverted_index.will | 163 |
| abstract_inverted_index.work | 132 |
| abstract_inverted_index.Image | 35 |
| abstract_inverted_index.MNIST | 159 |
| abstract_inverted_index.areas | 51 |
| abstract_inverted_index.digit | 3, 88 |
| abstract_inverted_index.image | 1, 11, 28, 106 |
| abstract_inverted_index.layer | 113 |
| abstract_inverted_index.model | 81, 99 |
| abstract_inverted_index.ofthe | 32 |
| abstract_inverted_index.on.In | 70 |
| abstract_inverted_index.study | 162 |
| abstract_inverted_index.them, | 175 |
| abstract_inverted_index.those | 165 |
| abstract_inverted_index.types | 58 |
| abstract_inverted_index.above, | 170 |
| abstract_inverted_index.forces | 47 |
| abstract_inverted_index.hybrid | 78 |
| abstract_inverted_index.issues | 166 |
| abstract_inverted_index.layer, | 135 |
| abstract_inverted_index.layer. | 115 |
| abstract_inverted_index.neural | 79, 98, 124 |
| abstract_inverted_index.safety | 63 |
| abstract_inverted_index.study, | 72 |
| abstract_inverted_index.system | 151, 156 |
| abstract_inverted_index.Pattern | 37 |
| abstract_inverted_index.because | 52 |
| abstract_inverted_index.capable | 84 |
| abstract_inverted_index.digits. | 34 |
| abstract_inverted_index.driving | 46 |
| abstract_inverted_index.expand, | 117 |
| abstract_inverted_index.explore | 164 |
| abstract_inverted_index.extract | 102 |
| abstract_inverted_index.network | 80, 100, 125 |
| abstract_inverted_index.output, | 140 |
| abstract_inverted_index.pattern | 14 |
| abstract_inverted_index.result. | 95 |
| abstract_inverted_index.Besides, | 144 |
| abstract_inverted_index.achieved | 92 |
| abstract_inverted_index.adequate | 18 |
| abstract_inverted_index.clinical | 65 |
| abstract_inverted_index.dataset. | 160 |
| abstract_inverted_index.features | 103, 110 |
| abstract_inverted_index.generate | 139 |
| abstract_inverted_index.modelcan | 131 |
| abstract_inverted_index.proposed | 97, 130 |
| abstract_inverted_index.research | 50 |
| abstract_inverted_index.semantic | 24 |
| abstract_inverted_index.shifting | 55 |
| abstract_inverted_index.Automatic | 0 |
| abstract_inverted_index.different | 57 |
| abstract_inverted_index.discussed | 169 |
| abstract_inverted_index.important | 121 |
| abstract_inverted_index.overcome. | 182 |
| abstract_inverted_index.recognize | 108, 127, 148 |
| abstract_inverted_index.requiring | 16 |
| abstract_inverted_index.syntactic | 22 |
| abstract_inverted_index.diversion, | 67 |
| abstract_inverted_index.identified | 41 |
| abstract_inverted_index.importance | 25 |
| abstract_inverted_index.phenomenon | 179 |
| abstract_inverted_index.processing | 12 |
| abstract_inverted_index.remarkable | 94 |
| abstract_inverted_index.Recognition | 38 |
| abstract_inverted_index.challenging | 8 |
| abstract_inverted_index.explanation | 173 |
| abstract_inverted_index.frameworks, | 64, 66 |
| abstract_inverted_index.handwritten | 33 |
| abstract_inverted_index.implemented | 76 |
| abstract_inverted_index.recognition | 4 |
| abstract_inverted_index.recognizing | 86 |
| abstract_inverted_index.variational | 154 |
| abstract_inverted_index.MNISTdataset | 90 |
| abstract_inverted_index.appreciation | 19 |
| abstract_inverted_index.recognition, | 15, 74 |
| abstract_inverted_index.applications, | 60 |
| abstract_inverted_index.auto-encoding | 150, 155 |
| abstract_inverted_index.identification | 31 |
| abstract_inverted_index.computationally | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |