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Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris

Character recognition system can contribute tremendously towards the advancement of automation process and can be useful in many other applications such as Data Entry, Document Processing and Cheque Verification. In this research, a prototype of text extraction and recognition using Adaptive Resonan...

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Main Author: Idris, Iffarini
Format: Thesis
Language:English
Published: 2008
Subjects:
C (Computer program language). C++
Image processing
Online Access:https://ir.uitm.edu.my/id/eprint/65767/1/65767.pdf
https://ir.uitm.edu.my/id/eprint/65767/
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spelling my.uitm.ir.657672022-09-22T09:08:11Z https://ir.uitm.edu.my/id/eprint/65767/ Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris Idris, Iffarini C (Computer program language). C++ Image processing Character recognition system can contribute tremendously towards the advancement of automation process and can be useful in many other applications such as Data Entry, Document Processing and Cheque Verification. In this research, a prototype of text extraction and recognition using Adaptive Resonance Theory 1 (ARTl) was proposed. For this project, several sets of images were collected from magazines and text books. In prototype design, the interface of ARTl and the ARTl neural network architecture were designed. The pre-process part of this prototype was developed using MATLAB and the recognition part was developed using C++. During the pre-processing stage, images were converted to binary image. Then, the title of the document images was extracted using Mathematical Morphological technique and the characters were segmented using labeling technique. After the pre-processing stage, each of the pixels value that represent the character will be the input to the ARTl network for character recognition process. ARTl neural network has proven to give good performance with 65.7 % recognition rate. A comparative study was conducted between ARTl and Backpropagation Neural Network (BPNN) to compare their recognition performances. BPNN is unable to meet the performance goal because of insufficient number of training data. In conclusion, ARTl is better than BPNN when the number of training data is small. 2008 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/65767/1/65767.pdf Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris. (2008) Degree thesis, thesis, Universiti Teknologi MARA (UiTM).
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic C (Computer program language). C++
Image processing
spellingShingle C (Computer program language). C++
Image processing
Idris, Iffarini
Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris
description Character recognition system can contribute tremendously towards the advancement of automation process and can be useful in many other applications such as Data Entry, Document Processing and Cheque Verification. In this research, a prototype of text extraction and recognition using Adaptive Resonance Theory 1 (ARTl) was proposed. For this project, several sets of images were collected from magazines and text books. In prototype design, the interface of ARTl and the ARTl neural network architecture were designed. The pre-process part of this prototype was developed using MATLAB and the recognition part was developed using C++. During the pre-processing stage, images were converted to binary image. Then, the title of the document images was extracted using Mathematical Morphological technique and the characters were segmented using labeling technique. After the pre-processing stage, each of the pixels value that represent the character will be the input to the ARTl network for character recognition process. ARTl neural network has proven to give good performance with 65.7 % recognition rate. A comparative study was conducted between ARTl and Backpropagation Neural Network (BPNN) to compare their recognition performances. BPNN is unable to meet the performance goal because of insufficient number of training data. In conclusion, ARTl is better than BPNN when the number of training data is small.
format Thesis
author Idris, Iffarini
author_facet Idris, Iffarini
author_sort Idris, Iffarini
title Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris
title_short Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris
title_full Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris
title_fullStr Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris
title_full_unstemmed Development of text extraction and recognition prototype using Adaptive Resonance Theory 1 (ART1) neural network / Iffarini Idris
title_sort development of text extraction and recognition prototype using adaptive resonance theory 1 (art1) neural network / iffarini idris
publishDate 2008
url https://ir.uitm.edu.my/id/eprint/65767/1/65767.pdf
https://ir.uitm.edu.my/id/eprint/65767/
_version_ 1745565321636347904
score 13.211869

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