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āļāļēāļĢāļĢāļđāđ‰āļˆāļģāļĨāļēāļĒāļĄāļ·āļ­āđ€āļ‚āļĩāļĒāļ™āļ•āļąāļ§āļžāļĒāļąāļāļŠāļ™āļ°āļ āļēāļĐāļēāđ„āļ—āļĒāđ‚āļ”āļĒāļāļēāļĢāđƒāļŠāđ‰āļ­āļĩāļĨāļēāļŠāļ•āļīāļāļ”āļĩāļŸāļ­āđ€āļĄāļŠāļąāļ™  The Handwritten Thai-Alphabet Recognition by Using Elastic Deformation āļ™āļēāļĒāļŠāļļāļ˜āļĩ āļ„āļ‡āđ€āļāļĩāļĒāļĢāļ•āļīāđ„āļžāļšāļđāļĨāļĒāđŒ āļĄ . 6/3  āđ€āļĨāļ‚āļ—āļĩāđˆ  22 āļ™āļēāļĒāļ āļ„āļ§āļąāļ’āļ™āđŒ āđ€āļ­āļĩāđˆāļĒāļĄāļŠāļļāđ‚āļĢ āļĄ . 6/10  āđ€āļĨāļ‚āļ—āļĩāđˆ  23 āļ­āļēāļˆāļēāļĢāļĒāđŒāļ—āļĩāđˆāļ›āļĢāļķāļāļĐāļē āļ­ . āļ›āļ—āļļāļĄāļĻāļīāļĢāļī  āļŠāļ‡āļĻāļīāļĢāļī
āļ—āļĩāđˆāļĄāļēāđāļĨāļ°āļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļ ,[object Object],[object Object],[object Object],[object Object]
āļˆāļļāļ”āļ›āļĢāļ°āļŠāļ‡āļ„āđŒ ,[object Object]
āļ‚āļ­āļšāđ€āļ‚āļ•āļāļēāļĢāļĻāļķāļāļĐāļē ,[object Object],[object Object],[object Object],[object Object]
āđāļ™āļ§āļ„āļīāļ”āđāļĨāļ°āļŠāļĄāļĄāļ•āļīāļāļēāļ™ ,[object Object],[object Object],[object Object],[object Object],[object Object]
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
[object Object],Thinning Y.Y. Zhang and P.S.P. Wang,  A Parallel Thinning Algorithm with Two-Subiteration that Generates One-Pixel-Wide Skeletons Fig.1 Psuedo Code  āđƒāļ™āļāļēāļĢ  thinning
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Height / Width Height Width Fig.2  āļ­āļąāļ•āļĢāļēāļŠāđˆāļ§āļ™  Height / Width
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
āļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļšāļ™   /  āļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļĨāđˆāļēāļ‡ Fig.3  āļāļēāļĢāļŦāļēāļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļšāļ™   /  āļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļĨāđˆāļēāļ‡
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
End point & Junction point, Connection Fig.4  āļāļēāļĢāđāļšāđˆāļ‡āđ‚āļ‹āļ™āļ‚āļ­āļ‡   End Point & Junction Fig.5  āļāļēāļĢāđāļšāđˆāļ‡āđ‚āļ‹āļ™āļ‚āļ­āļ‡   Connection
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Head Fig. 6  āļāļēāļĢāļŦāļē  Head I. Methasate, S. Marukatat, S. Sae-tang, T. Theeramunkong, “The Feature Combination Technique for Off-line Thai Character Recognition System” Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR’05), 2005.
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Vertical Stroke ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Fig.7   āļāļēāļĢāļŦāļē  Vertical Stroke I. Methasate, S. Sae-tang, “The Clustering Technique for Thai Handwritten Recognition” Proceedings of the 9th Int’l Workshop on Frontiers in Handwriting Recognition (IWFHR-9 2004), 2004.
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Crossing Count Fig.8   āļāļēāļĢāđāļšāđˆāļ‡āđ‚āļ‹āļ™āļ‚āļ­āļ‡   Crossing Count
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Cavity Fig.9  āļĨāļąāļāļĐāļ“āļ°āļ‚āļ­āļ‡  Cavity  āđāļšāļšāļ•āđˆāļēāļ‡āđ† Pisit Phokharatkul and Chom Kimpan, “Recognition of handprinted Thai characters Using the cavity features of character base on neural network.” The 1998 IEEE Asia-Pacific Conference on circuits and systems, 24-27 November 1998, pp. 149-152.
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Histogram ,[object Object],Fig.10  āļāļēāļĢāđāļšāđˆāļ‡āļŠāđˆāļ§āļ™āļāļēāļĢāļ—āļģ  Histogram
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Empty Zone Fig.11  āļāļēāļĢāđāļšāđˆāļ‡āļŠāđˆāļ§āļ™āļāļēāļĢāļ—āļģ  Empty Zone
Normalize ,[object Object],[object Object],[object Object],[object Object]
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
āļāļēāļĢāļŦāļē  Feature  āļ—āļĩāđˆāđ€āļŦāļĄāļēāļ°āļŠāļĄ āļ•āļēāļĢāļēāļ‡āđāļŠāļ”āļ‡āļ„āđˆāļēāļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡āđƒāļ™āļāļēāļĢāđ€āļĨāļ·āļ­āļāđƒāļŠāđ‰  feature  āđāļ•āđˆāļĨāļ°āļŠāļ™āļīāļ”  (10  āļ­āļąāļ™āļ”āļąāļšāļŠāļđāļ‡āļŠāļļāļ” ) * 1 =  āđƒāļŠāđ‰  feature   āļ™āļąāđ‰āļ™  normalize, 2 =  āđƒāļŠāđ‰  feature   āļ™āļąāđ‰āļ™āđ„āļĄāđˆ  normalize, 0 =  āđ„āļĄāđˆāđƒāļŠāđ‰  feature  āļ™āļąāđ‰āļ™ % H/W U/L End Junc. Con. Verst. H. In. H. Fill. H. Re. C. Ver. C. Hor. Cavity His ver His Hor Empty 69.3478 1 1 2 2 2 2 0 2 2 2 2 1 2 2 2 69.3478 1 1 2 2 2 1 0 2 2 2 2 1 2 2 2 69.3478 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 69.2391 1 1 2 2 2 2 0 0 2 2 2 1 2 2 2 69.2391 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 69.0217 1 1 2 2 2 1 2 2 2 2 2 1 2 2 2 69.0217 1 1 2 2 2 2 0 2 2 2 2 1 2 2 0 68.587 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 68.587 1 1 0 0 0 0 0 0 0 2 2 1 2 2 2 68.3696 1 1 2 0 2 1 0 2 2 2 2 1 2 2 2
āđāļœāļ™āļ āļēāļžāļāļēāļĢāļ—āļģāļ‡āļēāļ™ Input ,[object Object],[object Object],[object Object],Preprocessing Feature Extraction Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Elastic Deformation
Elastic Deformation ,[object Object],Fig.12  āļāļēāļĢāļ—āļģ  Elastic Deformation
āļ•āļēāļĢāļēāļ‡āđāļŠāļ”āļ‡āļ„āđˆāļēāļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡āđ€āļĄāļ·āđˆāļ­āļĄāļĩāļāļēāļĢāđ€āļžāļīāđˆāļĄāļˆāļģāļ™āļ§āļ™āļ•āļąāļ§āļ­āļĒāđˆāļēāļ‡ 116 100 80 60 40 20 0 1 3 5 69.3478 69.3478 70.1087 48.4783 69.3478 69.5652 70.1087 49.4565 69.3478 69.4565 69.8913 51.1957 69.2391 69.1304 69.6739 50.4348 69.2391 69.3478 69.7826 50.2174 0 1 3 5 69.7826 69.0217 69.5652 57.5 68.2609 67.9348 68.6957 55.6522 69.4565 69.1304 69.3478 56.8478 69.2391 68.8043 68.913 56.413 69.0217 69.3478 69.7826 56.5217 0 1 3 5 67.9348 67.3913 68.1522 55.5435 67.7139 66.9565 67.9348 63.913 67.3913 67.1739 68.0435 65.5435 66.3043 66.413 66.9565 64.2391 67.7174 67.5 68.0435 64.2391 0 1 3 5 66.6304 66.7391 66.8478 59.5652 65.9783 66.087 65.9783 67.2826 66.7391 66.6304 66.6304 67.1739 66.1957 66.413 66.413 67.0652 67.1739 66.7391 67.1739 67.0652 0 1 3 5 62.8261 62.5 63.6957 64.4565 59.3478 47.2826 61.087 62.7174 59.5652 59.0217 60.9783 60.7609 62.2826 61.5127 63.2609 63.4783 63.587 63.0435 63.913 64.5652 0 1 3 5 56.5217 57.0652 58.3696 59.1304 55.9783 55.9783 57.8261 59.4565 54.4565 54.8913 56.1957 56.9565 56.1957 56.6304 57.3913 58.587 56.413 56.413 57.5 58.8043
āļŠāļĢāļļāļ›āļœāļĨ ,[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]

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  • 1. āļāļēāļĢāļĢāļđāđ‰āļˆāļģāļĨāļēāļĒāļĄāļ·āļ­āđ€āļ‚āļĩāļĒāļ™āļ•āļąāļ§āļžāļĒāļąāļāļŠāļ™āļ°āļ āļēāļĐāļēāđ„āļ—āļĒāđ‚āļ”āļĒāļāļēāļĢāđƒāļŠāđ‰āļ­āļĩāļĨāļēāļŠāļ•āļīāļāļ”āļĩāļŸāļ­āđ€āļĄāļŠāļąāļ™ The Handwritten Thai-Alphabet Recognition by Using Elastic Deformation āļ™āļēāļĒāļŠāļļāļ˜āļĩ āļ„āļ‡āđ€āļāļĩāļĒāļĢāļ•āļīāđ„āļžāļšāļđāļĨāļĒāđŒ āļĄ . 6/3 āđ€āļĨāļ‚āļ—āļĩāđˆ 22 āļ™āļēāļĒāļ āļ„āļ§āļąāļ’āļ™āđŒ āđ€āļ­āļĩāđˆāļĒāļĄāļŠāļļāđ‚āļĢ āļĄ . 6/10 āđ€āļĨāļ‚āļ—āļĩāđˆ 23 āļ­āļēāļˆāļēāļĢāļĒāđŒāļ—āļĩāđˆāļ›āļĢāļķāļāļĐāļē āļ­ . āļ›āļ—āļļāļĄāļĻāļīāļĢāļī āļŠāļ‡āļĻāļīāļĢāļī
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  • 12. Height / Width Height Width Fig.2 āļ­āļąāļ•āļĢāļēāļŠāđˆāļ§āļ™ Height / Width
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  • 14. āļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļšāļ™ / āļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļĨāđˆāļēāļ‡ Fig.3 āļāļēāļĢāļŦāļēāļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļšāļ™ / āļˆāļģāļ™āļ§āļ™āļˆāļļāļ”āļĨāđˆāļēāļ‡
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  • 16. End point & Junction point, Connection Fig.4 āļāļēāļĢāđāļšāđˆāļ‡āđ‚āļ‹āļ™āļ‚āļ­āļ‡ End Point & Junction Fig.5 āļāļēāļĢāđāļšāđˆāļ‡āđ‚āļ‹āļ™āļ‚āļ­āļ‡ Connection
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  • 18. Head Fig. 6 āļāļēāļĢāļŦāļē Head I. Methasate, S. Marukatat, S. Sae-tang, T. Theeramunkong, “The Feature Combination Technique for Off-line Thai Character Recognition System” Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR’05), 2005.
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  • 22. Crossing Count Fig.8 āļāļēāļĢāđāļšāđˆāļ‡āđ‚āļ‹āļ™āļ‚āļ­āļ‡ Crossing Count
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  • 24. Cavity Fig.9 āļĨāļąāļāļĐāļ“āļ°āļ‚āļ­āļ‡ Cavity āđāļšāļšāļ•āđˆāļēāļ‡āđ† Pisit Phokharatkul and Chom Kimpan, “Recognition of handprinted Thai characters Using the cavity features of character base on neural network.” The 1998 IEEE Asia-Pacific Conference on circuits and systems, 24-27 November 1998, pp. 149-152.
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  • 28. Empty Zone Fig.11 āļāļēāļĢāđāļšāđˆāļ‡āļŠāđˆāļ§āļ™āļāļēāļĢāļ—āļģ Empty Zone
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  • 31. āļāļēāļĢāļŦāļē Feature āļ—āļĩāđˆāđ€āļŦāļĄāļēāļ°āļŠāļĄ āļ•āļēāļĢāļēāļ‡āđāļŠāļ”āļ‡āļ„āđˆāļēāļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡āđƒāļ™āļāļēāļĢāđ€āļĨāļ·āļ­āļāđƒāļŠāđ‰ feature āđāļ•āđˆāļĨāļ°āļŠāļ™āļīāļ” (10 āļ­āļąāļ™āļ”āļąāļšāļŠāļđāļ‡āļŠāļļāļ” ) * 1 = āđƒāļŠāđ‰ feature āļ™āļąāđ‰āļ™ normalize, 2 = āđƒāļŠāđ‰ feature āļ™āļąāđ‰āļ™āđ„āļĄāđˆ normalize, 0 = āđ„āļĄāđˆāđƒāļŠāđ‰ feature āļ™āļąāđ‰āļ™ % H/W U/L End Junc. Con. Verst. H. In. H. Fill. H. Re. C. Ver. C. Hor. Cavity His ver His Hor Empty 69.3478 1 1 2 2 2 2 0 2 2 2 2 1 2 2 2 69.3478 1 1 2 2 2 1 0 2 2 2 2 1 2 2 2 69.3478 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 69.2391 1 1 2 2 2 2 0 0 2 2 2 1 2 2 2 69.2391 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 69.0217 1 1 2 2 2 1 2 2 2 2 2 1 2 2 2 69.0217 1 1 2 2 2 2 0 2 2 2 2 1 2 2 0 68.587 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 68.587 1 1 0 0 0 0 0 0 0 2 2 1 2 2 2 68.3696 1 1 2 0 2 1 0 2 2 2 2 1 2 2 2
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  • 34. āļ•āļēāļĢāļēāļ‡āđāļŠāļ”āļ‡āļ„āđˆāļēāļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡āđ€āļĄāļ·āđˆāļ­āļĄāļĩāļāļēāļĢāđ€āļžāļīāđˆāļĄāļˆāļģāļ™āļ§āļ™āļ•āļąāļ§āļ­āļĒāđˆāļēāļ‡ 116 100 80 60 40 20 0 1 3 5 69.3478 69.3478 70.1087 48.4783 69.3478 69.5652 70.1087 49.4565 69.3478 69.4565 69.8913 51.1957 69.2391 69.1304 69.6739 50.4348 69.2391 69.3478 69.7826 50.2174 0 1 3 5 69.7826 69.0217 69.5652 57.5 68.2609 67.9348 68.6957 55.6522 69.4565 69.1304 69.3478 56.8478 69.2391 68.8043 68.913 56.413 69.0217 69.3478 69.7826 56.5217 0 1 3 5 67.9348 67.3913 68.1522 55.5435 67.7139 66.9565 67.9348 63.913 67.3913 67.1739 68.0435 65.5435 66.3043 66.413 66.9565 64.2391 67.7174 67.5 68.0435 64.2391 0 1 3 5 66.6304 66.7391 66.8478 59.5652 65.9783 66.087 65.9783 67.2826 66.7391 66.6304 66.6304 67.1739 66.1957 66.413 66.413 67.0652 67.1739 66.7391 67.1739 67.0652 0 1 3 5 62.8261 62.5 63.6957 64.4565 59.3478 47.2826 61.087 62.7174 59.5652 59.0217 60.9783 60.7609 62.2826 61.5127 63.2609 63.4783 63.587 63.0435 63.913 64.5652 0 1 3 5 56.5217 57.0652 58.3696 59.1304 55.9783 55.9783 57.8261 59.4565 54.4565 54.8913 56.1957 56.9565 56.1957 56.6304 57.3913 58.587 56.413 56.413 57.5 58.8043
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