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On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing an...

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Main Authors: Zafar, Muhammad Faisal, Mohamad, Dzulkifli, Othman, Muhamad Razib
格式: Article
语言:English
出版: ENFORMATIKA 2005
在线阅读:http://eprints.utm.my/id/eprint/8740/1/Enformatika-v10.pdf
http://eprints.utm.my/id/eprint/8740/
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.6803
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spelling my.utm.87402017-04-12T01:31:07Z http://eprints.utm.my/id/eprint/8740/ On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net Zafar, Muhammad Faisal Mohamad, Dzulkifli Othman, Muhamad Razib On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples. ENFORMATIKA 2005-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/8740/1/Enformatika-v10.pdf Zafar, Muhammad Faisal and Mohamad, Dzulkifli and Othman, Muhamad Razib (2005) On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net. On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net, V10 . pp. 232-237. ISSN 1305-5313 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.6803
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
description On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
format Article
author Zafar, Muhammad Faisal
Mohamad, Dzulkifli
Othman, Muhamad Razib
spellingShingle Zafar, Muhammad Faisal
Mohamad, Dzulkifli
Othman, Muhamad Razib
On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
author_facet Zafar, Muhammad Faisal
Mohamad, Dzulkifli
Othman, Muhamad Razib
author_sort Zafar, Muhammad Faisal
title On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
title_short On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
title_full On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
title_fullStr On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
title_full_unstemmed On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
title_sort on-line handwritten character recognition: an implementation of counterpropagation neural net
publisher ENFORMATIKA
publishDate 2005
url http://eprints.utm.my/id/eprint/8740/1/Enformatika-v10.pdf
http://eprints.utm.my/id/eprint/8740/
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.6803
_version_ 1643645060276813824
score 13.251813

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