A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition

dc.article.pages420-436
dc.contributor.authorRababaah, Aaron Rasheed
dc.date.accessioned2024-02-05T08:32:41Z
dc.date.available2024-02-05T08:32:41Z
dc.date.issued2023-01-01
dc.description.abstractThis 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.
dc.identifier.doi10.1504/IJCVR.2023.131985
dc.identifier.urihttp://hdl.handle.net/11675/10928
dc.identifier.urlhttps://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcvr
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85166402843&partnerID=8YFLogxK
dc.journal.issue4
dc.journal.volume13
dc.publisherInderscience Enterprises Ltd
dc.relationComputer Science and Info Systems
dc.relation.journalInternational Journal of Computational Vision and Robotics
dc.titleA comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition
dc.typeOther
dcterms.bibliographicCitationPublisher Copyright: Copyright © 2023 Inderscience Enterprises Ltd.
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