A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition
This paper presents an investigation that aims at comparing deep learning (DL) and traditional artificial neural networks (ANNs) in the application of hand-written digits recognition (HDR). In our study, convolution neural networks (CNNs) are a representative model for the DL models and the multi-la...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| منشور في: |
2023
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| الوصول للمادة أونلاين: | http://hdl.handle.net/11675/10928 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcvr http://www.scopus.com/inward/record.url?scp=85166402843&partnerID=8YFLogxK |
| الوسوم: |
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| _version_ | 1870679718107283457 |
|---|---|
| author | Rababaah, Aaron |
| author_facet | Rababaah, Aaron |
| author_role | author |
| dc.creator.none.fl_str_mv | Rababaah, Aaron |
| dc.date.none.fl_str_mv | 2023-01-01 2024-02-05T08:32:41Z 2024-02-05T08:32:41Z |
| dc.identifier.none.fl_str_mv | 10.1504/IJCVR.2023.131985 http://hdl.handle.net/11675/10928 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcvr http://www.scopus.com/inward/record.url?scp=85166402843&partnerID=8YFLogxK |
| dc.publisher.none.fl_str_mv | Inderscience Enterprises Ltd |
| dc.relation.none.fl_str_mv | Computer Science and Info Systems International Journal of Computational Vision and Robotics |
| dc.title.none.fl_str_mv | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition |
| dc.type.none.fl_str_mv | Other info:eu-repo/semantics/publishedVersion |
| description | This paper presents an investigation that aims at comparing deep learning (DL) and traditional artificial neural networks (ANNs) in the application of hand-written digits recognition (HDR). In our study, convolution neural networks (CNNs) are a representative model for the DL models and the multi-layer perceptron (MLP) is a representative model for ANN models. The two models MLP and CNN were implemented using MATLAB development environment and tested using a publicly available image database. The databse consists of over 20,000 samples with all ten hand-written digits each of which is 24 × 24 pixels. The experimental results showed that the CNN model was superior to the MLP model with an average classification accuracy of 95.14% and 89.74% respectively. Furthermore, the CNN model was observed to have better performance stability and better execution efficiency as the MLP model requires human intervention to handcraft and pre-process the features of the digit patterns. |
| id | AUKR_0e4eb90b8dd2c84c10b2abcd3ec49e3d |
| identifier_str_mv | 10.1504/IJCVR.2023.131985 |
| network_acronym_str | AUKR |
| network_name_str | AU Kuwait Rep |
| oai_identifier_str | oai:dspace.auk.edu.kw:11675/10928 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | Inderscience Enterprises Ltd |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognitionRababaah, AaronThis paper presents an investigation that aims at comparing deep learning (DL) and traditional artificial neural networks (ANNs) in the application of hand-written digits recognition (HDR). In our study, convolution neural networks (CNNs) are a representative model for the DL models and the multi-layer perceptron (MLP) is a representative model for ANN models. The two models MLP and CNN were implemented using MATLAB development environment and tested using a publicly available image database. The databse consists of over 20,000 samples with all ten hand-written digits each of which is 24 × 24 pixels. The experimental results showed that the CNN model was superior to the MLP model with an average classification accuracy of 95.14% and 89.74% respectively. Furthermore, the CNN model was observed to have better performance stability and better execution efficiency as the MLP model requires human intervention to handcraft and pre-process the features of the digit patterns.Inderscience Enterprises Ltd2024-02-05T08:32:41Z2024-02-05T08:32:41Z2023-01-01Otherinfo:eu-repo/semantics/publishedVersion10.1504/IJCVR.2023.131985http://hdl.handle.net/11675/10928https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcvrhttp://www.scopus.com/inward/record.url?scp=85166402843&partnerID=8YFLogxKComputer Science and Info SystemsInternational Journal of Computational Vision and Roboticsoai:dspace.auk.edu.kw:11675/109282025-06-18T08:58:37Z |
| spellingShingle | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition Rababaah, Aaron |
| status_str | publishedVersion |
| title | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition |
| title_full | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition |
| title_fullStr | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition |
| title_full_unstemmed | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition |
| title_short | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition |
| title_sort | A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition |
| url | http://hdl.handle.net/11675/10928 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcvr http://www.scopus.com/inward/record.url?scp=85166402843&partnerID=8YFLogxK |