Classification of Graffiti digits by using Computational Intelligence: Several architectures and techniques to optimize the performance of the Neural Networks in the Pattern Recognition

Ali H. Al-Fatlawi

ISBN 10: 3330969369 / ISBN 13: 9783330969360

Published by Noor Publishing, 2017

About this Item

96 pages. 8.66x5.91x0.22 inches. In Stock.

Seller Inventory # 3330969369

Bibliographic Details

• Title: Classification of Graffiti digits by using Computational Intelligence: Several architectures and techniques to optimize the performance of the Neural Networks in the Pattern Recognition
• Publisher: Noor Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 3330969369
• ISBN 13: 9783330969360
• Binding: Paperback
• Condition: Brand New

About this title

Synopsis

The technological advances and the massive flood of papers have motivated many researchers and companies to innovate new methods and technologies. They build automatic readers to recognize handwritten documents. In particular, handwriting recognition is very useful technology to support applications like electronic books (eBooks), postcode readers (that sort the mail in post offices), and some bank’s applications. This book proposed systems to discriminate handwritten graffiti digits and some commands with different architectures and abilities. It introduced three classifiers, namely single neural network (SNN) classifier, parallel neural networks (PNN) classifier and tree-structured (TS) classifier. The three classifiers have been designed through adopting feed-forward neural networks. The back-propagation algorithm has been used to optimize the network’s parameters (connection weights). Several architectures are applied and examined to present a comparative study about the three systems from different perspectives.

About the Author

Ali Al-Fatlawi is a researcher in the Information Technology Research and Development Center at University of Kufa since 2009. He has a Master degree from the University of Technology, Sydney (Australia) in field of the Computer Control Engineering. His works focus on investigating technologies and algorithms related to the machine learning.