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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
ENFORMATIKA
2005
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| Online Access: | http://eprints.utm.my/8740/ http://eprints.utm.my/8740/1/Enformatika-v10.pdf |
| _version_ | 1848891756736675840 |
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| author | Zafar, Muhammad Faisal Mohamad, Dzulkifli Othman, Muhamad Razib |
| author_facet | Zafar, Muhammad Faisal Mohamad, Dzulkifli Othman, Muhamad Razib |
| author_sort | Zafar, Muhammad Faisal |
| building | UTeM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T21:03:02Z |
| format | Article |
| id | utm-8740 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:03:02Z |
| publishDate | 2005 |
| publisher | ENFORMATIKA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-87402017-04-12T01:31:07Z http://eprints.utm.my/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/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 |
| spellingShingle | Zafar, Muhammad Faisal Mohamad, Dzulkifli Othman, Muhamad Razib On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net |
| title | 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_short | On-line Handwritten Character Recognition: An
Implementation of Counterpropagation Neural Net |
| title_sort | on-line handwritten character recognition: an
implementation of counterpropagation neural net |
| url | http://eprints.utm.my/8740/ http://eprints.utm.my/8740/ http://eprints.utm.my/8740/1/Enformatika-v10.pdf |