Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi

This project presents a study on the offline handwritten character recognition using backpropagation algorithm that is one of the training algorithms used in Artificial Neural Network. In this method, information about errors is propagated backwards from output to input layers in order to adjust the...

Full description

Bibliographic Details
Main Author:	Mohadi, Masnizah
Format:	Thesis
Language:	English
Published:	2005
Subjects:	Neural networks (Computer science) Pattern recognition systems
Online Access:	http://ir.uitm.edu.my/id/eprint/9391/ http://ir.uitm.edu.my/id/eprint/9391/1/TD_MASNIZAH%20MOHADI%20CS%2005_1.pdf

id	uitm-9391
recordtype	eprints
spelling	uitm-93912017-01-25T06:45:33Z http://ir.uitm.edu.my/id/eprint/9391/ Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi Mohadi, Masnizah Neural networks (Computer science) Pattern recognition systems This project presents a study on the offline handwritten character recognition using backpropagation algorithm that is one of the training algorithms used in Artificial Neural Network. In this method, information about errors is propagated backwards from output to input layers in order to adjust the connection between the layers thus improving the network's performances. The purpose of this project is to recognized handwritten characters that were scanned earlier before the recognition process begins. For the purpose of this project, only lowercase characters from a to z are considered which total up to 26 characters all. Each handwritten characters is collected from 19 people, each in a 2 X 1.5cm box using a black ballpoint pen. From this process, 13 data collected are used for training and 6 more are used to train the network in order to evaluate the network's performance. The useful features of information that was extracted from the handwritten character images are the edges of the character using Sobel Edge Detection Method. As a result of this project, it is proven that the backpropagation algorithm can be used for recognizing handwritten characters and recognition tasks depends highly on how the data was preprocessed and the network parameter itself. 2005 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/9391/1/TD_MASNIZAH%20MOHADI%20CS%2005_1.pdf Mohadi, Masnizah (2005) Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Neural networks (Computer science) Pattern recognition systems
spellingShingle	Neural networks (Computer science) Pattern recognition systems Mohadi, Masnizah Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi
description	This project presents a study on the offline handwritten character recognition using backpropagation algorithm that is one of the training algorithms used in Artificial Neural Network. In this method, information about errors is propagated backwards from output to input layers in order to adjust the connection between the layers thus improving the network's performances. The purpose of this project is to recognized handwritten characters that were scanned earlier before the recognition process begins. For the purpose of this project, only lowercase characters from a to z are considered which total up to 26 characters all. Each handwritten characters is collected from 19 people, each in a 2 X 1.5cm box using a black ballpoint pen. From this process, 13 data collected are used for training and 6 more are used to train the network in order to evaluate the network's performance. The useful features of information that was extracted from the handwritten character images are the edges of the character using Sobel Edge Detection Method. As a result of this project, it is proven that the backpropagation algorithm can be used for recognizing handwritten characters and recognition tasks depends highly on how the data was preprocessed and the network parameter itself.
format	Thesis
author	Mohadi, Masnizah
author_facet	Mohadi, Masnizah
author_sort	Mohadi, Masnizah
title	Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi
title_short	Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi
title_full	Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi
title_fullStr	Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi
title_full_unstemmed	Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi
title_sort	offline handwritten character recognition using backpropagation neural network / masnizah mohadi
publishDate	2005
url	http://ir.uitm.edu.my/id/eprint/9391/ http://ir.uitm.edu.my/id/eprint/9391/1/TD_MASNIZAH%20MOHADI%20CS%2005_1.pdf
first_indexed	2023-09-18T22:47:42Z
last_indexed	2023-09-18T22:47:42Z
_version_	1777417335869538304

Offline handwritten character recognition using backpropagation neural network / Masnizah Mohadi

Similar Items