Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari

There are various types of recognition available that are face recognition, digit recognition and many more. Various techniques used to recognize pattern such as Neural Network (NN), Genetic Algorithm (GA) and others. This project focuses on isolated digit recognition using GA as the recognition tec...

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Main Author:	Mohd Sari, Farizatul Sarina
Format:	Thesis
Published:	2007
Online Access:	http://ir.uitm.edu.my/id/eprint/9305/

id	uitm-9305
recordtype	eprints
spelling	uitm-93052017-10-14T04:36:08Z http://ir.uitm.edu.my/id/eprint/9305/ Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari Mohd Sari, Farizatul Sarina There are various types of recognition available that are face recognition, digit recognition and many more. Various techniques used to recognize pattern such as Neural Network (NN), Genetic Algorithm (GA) and others. This project focuses on isolated digit recognition using GA as the recognition technique. The objective of the project is to determine the best performance rate between two types of images that are 16 x 16 images and 32 x 32 images with different type of encoding scheme and crossover rate and also to determine the best crossover rates between 0.6,0.7 and O.S. The project is based on a series of experiments that compare performance rate to recognize digit using GA. There are three sources of data involved in the project that are using MNIST database, USPS database and researcher's own data. The data will be in a form of bitmap pictures with 16 x 16 images and 32 x 32 images. In the algorithm, two types of encoding scheme used that are binary encoding and permutation encoding. The type of crossover used is one-point crossover with multiple rates that are 0.6, 0.7 and 0.8 and the type mutation used is inversion with the rate of 0.01. There are four types of experiment that have been conducted. Firstly, 16 x 16 images represented in binary encoding with different crossover rates. Secondly, 16 x 16 images represented in permutation encoding with different crossover rates. Thirdly, 32 x 32 images represented in binary encoding with different crossover rates. Lastly, 32 x 32 images represented in permutation encodmg with different crossover rates. From the experiment made, it was showed that the best performance to recognize a digit is S hours and 41 minutes for 16 x 16 images using binary encoding and 0.7 crossover rate. The best crossover rate among the three rates that is being tested is 0.7. The result of this research shows that the performance rate can be improved so that it can be implemented in various field. 2007 Thesis NonPeerReviewed Mohd Sari, Farizatul Sarina (2007) Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
description	There are various types of recognition available that are face recognition, digit recognition and many more. Various techniques used to recognize pattern such as Neural Network (NN), Genetic Algorithm (GA) and others. This project focuses on isolated digit recognition using GA as the recognition technique. The objective of the project is to determine the best performance rate between two types of images that are 16 x 16 images and 32 x 32 images with different type of encoding scheme and crossover rate and also to determine the best crossover rates between 0.6,0.7 and O.S. The project is based on a series of experiments that compare performance rate to recognize digit using GA. There are three sources of data involved in the project that are using MNIST database, USPS database and researcher's own data. The data will be in a form of bitmap pictures with 16 x 16 images and 32 x 32 images. In the algorithm, two types of encoding scheme used that are binary encoding and permutation encoding. The type of crossover used is one-point crossover with multiple rates that are 0.6, 0.7 and 0.8 and the type mutation used is inversion with the rate of 0.01. There are four types of experiment that have been conducted. Firstly, 16 x 16 images represented in binary encoding with different crossover rates. Secondly, 16 x 16 images represented in permutation encoding with different crossover rates. Thirdly, 32 x 32 images represented in binary encoding with different crossover rates. Lastly, 32 x 32 images represented in permutation encodmg with different crossover rates. From the experiment made, it was showed that the best performance to recognize a digit is S hours and 41 minutes for 16 x 16 images using binary encoding and 0.7 crossover rate. The best crossover rate among the three rates that is being tested is 0.7. The result of this research shows that the performance rate can be improved so that it can be implemented in various field.
format	Thesis
author	Mohd Sari, Farizatul Sarina
spellingShingle	Mohd Sari, Farizatul Sarina Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari
author_facet	Mohd Sari, Farizatul Sarina
author_sort	Mohd Sari, Farizatul Sarina
title	Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari
title_short	Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari
title_full	Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari
title_fullStr	Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari
title_full_unstemmed	Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari
title_sort	isolated handwritten digit recognition using genetic algorithm / farizatul sarina mohd sari
publishDate	2007
url	http://ir.uitm.edu.my/id/eprint/9305/
first_indexed	2023-09-18T22:47:38Z
last_indexed	2023-09-18T22:47:38Z
_version_	1777417331816792064

Isolated handwritten digit recognition using genetic algorithm / Farizatul Sarina Mohd Sari

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