Using contour analysis to improve feature extraction in Thai handwritten character recognition systems
This research proposes an approach to the analysis of contour characteristics for the feature extraction process of an off-line Thai handwritten character recognition system (THW-CR). The system aims at increasing the accuracy of both feature extraction rate and character recognition rate. We introd...
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th-mahidol.243812018-08-24T08:56:35Z Using contour analysis to improve feature extraction in Thai handwritten character recognition systems Jarernsri L. Mitrpanont Urairat Limkonglap Mahidol University Computer Science Mathematics This research proposes an approach to the analysis of contour characteristics for the feature extraction process of an off-line Thai handwritten character recognition system (THW-CR). The system aims at increasing the accuracy of both feature extraction rate and character recognition rate. We introduce the contour to capture the movement of features. The propose modules are the feature representation and the feature conflict resolution rules. With our proposed approach, the experiment shows the results increase reasonably. The maximum Feature Extraction Rate (FER) reached 97.73% Meanwhile, the highest Character Recognition Rate (CRR) reached 95.35% Additionally, the result of the overall evaluation showed that the THW-CR system generated reliable results for improving the accuracy of the feature extraction rate and the character recognition rate compared with the previous research by 3.62% and 8.33%, respectively. © 2007 IEEE. 2018-08-24T01:48:01Z 2018-08-24T01:48:01Z 2007-12-01 Conference Paper CIT 2007: 7th IEEE International Conference on Computer and Information Technology. (2007), 668-673 10.1109/CIT.2007.4385161 2-s2.0-38049052226 https://repository.li.mahidol.ac.th/handle/123456789/24381 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=38049052226&origin=inward |
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Computer Science Mathematics Jarernsri L. Mitrpanont Urairat Limkonglap Using contour analysis to improve feature extraction in Thai handwritten character recognition systems |
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This research proposes an approach to the analysis of contour characteristics for the feature extraction process of an off-line Thai handwritten character recognition system (THW-CR). The system aims at increasing the accuracy of both feature extraction rate and character recognition rate. We introduce the contour to capture the movement of features. The propose modules are the feature representation and the feature conflict resolution rules. With our proposed approach, the experiment shows the results increase reasonably. The maximum Feature Extraction Rate (FER) reached 97.73% Meanwhile, the highest Character Recognition Rate (CRR) reached 95.35% Additionally, the result of the overall evaluation showed that the THW-CR system generated reliable results for improving the accuracy of the feature extraction rate and the character recognition rate compared with the previous research by 3.62% and 8.33%, respectively. © 2007 IEEE. |
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Mahidol University |
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Mahidol University Jarernsri L. Mitrpanont Urairat Limkonglap |
format |
Conference or Workshop Item |
author |
Jarernsri L. Mitrpanont Urairat Limkonglap |
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Jarernsri L. Mitrpanont |
title |
Using contour analysis to improve feature extraction in Thai handwritten character recognition systems |
title_short |
Using contour analysis to improve feature extraction in Thai handwritten character recognition systems |
title_full |
Using contour analysis to improve feature extraction in Thai handwritten character recognition systems |
title_fullStr |
Using contour analysis to improve feature extraction in Thai handwritten character recognition systems |
title_full_unstemmed |
Using contour analysis to improve feature extraction in Thai handwritten character recognition systems |
title_sort |
using contour analysis to improve feature extraction in thai handwritten character recognition systems |
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2018 |
url |
https://repository.li.mahidol.ac.th/handle/123456789/24381 |
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1763488216877367296 |