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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2007 / Cilt: 7 - Sayı: 1

Handwritten character recognition system using artificial neural networks

Pelin GORGEL
Oguzhan OZTAS
Pages
309–313
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Input datas, network parametres andtraining period effect the result.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, a Handwritten Character Recognition system is designed using Multilayer FeedforwardArticial Neural Networks.
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Key Findings & Data:Recognition percentage of the system is higher than acceptable level.
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Conclusion & Impact:Input datas, network parametres andtraining period effect the result.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper, a Handwritten Character Recognition system is designed using Multilayer FeedforwardArticial Neural Networks. Backpropagation Learning algorithm is prefered for training of neural network. Training set occures of various Latin characters collected from different people. The characters are presented directly to the network and correctly sized in pre-processing. Recognition percentage of the system is higher than acceptable level. Input datas, network parametres andtraining period effect the result.

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Cite This Article

GORGEL, P., OZTAS, O. (2022). Handwritten character recognition system using artificial neural networks. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 7 - Sayı: 1, 309–313. https://ajindex.com/articles/543780

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References (4)

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  4. Salameh, W. A., Otair M. A. “Online handwritten character recognition using an optical backpropagation neural network”. Issues in Informing Science and Information Technology, 787-795. 2005. [6] http://www.rgu.ac.uk