Graham Kendall
Various Images

Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

I am the chair of the MISTA (Multidisciplinary International Conference on Scheduling: Theory and Applications)
http://bit.ly/hvZIaN
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Video Channels for Numbers, Periodic Tables, Deep Sky and all that is Molecular

Publication(s)

Diversity in Genetic Programming: An Analysis of Measures and Correlation with Fitness
http://bit.ly/gT5U5I
On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
http://bit.ly/2kqxtD3
A New Bottom-left-Fill Heuristic Algorithm for the 2D Irregular Packing Problem
http://bit.ly/fneYnV
A Hybrid Evolutionary Approach to the Nurse Rostering Problem
http://bit.ly/ey147Y

Graham Kendall: Details of Requested Publication


Citation

Ibrahim, Z; Isa, D; Rajkumar, R and Kendall, G Document Zone Classification for Technical Document Images Using Artificial Neural Network and Support Vector Machines. In Second International Conference on the Applications of Digital Information and Web Technologies, pages 345-350, 2009.


Abstract

Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/ICADIWT.2009.5273957 You can link directly to the original paper, via the doi, from here

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information



URL

This pubication does not have a URL associated with it.

The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


Bibtex

@INPROCEEDINGS{iirk2009, author = {Z. Ibrahim and D. Isa and R. Rajkumar and G. Kendall},
title = {Document Zone Classification for Technical Document Images Using Artificial Neural Network and Support Vector Machines},
booktitle = {Second International Conference on the Applications of Digital Information and Web Technologies},
year = {2009},
pages = {345--350},
abstract = {Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.},
doi = {10.1109/ICADIWT.2009.5273957},
owner = {gxk},
timestamp = {2011.12.08},
webpdf = {http://www.graham-kendall.com/papers/iirk2009.pdf} }