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การรู้จำลายมือเขียนตัวพยัญชนะภาษาไทยโดยการใช้อีลาสติกดีฟอเมชัน  The Handwritten Thai-Alphabet Recognition by Using Elastic Deformation นายสุธี คงเกียรติไพบูลย์ ม . 6/3  เลขที่  22 นายภควัฒน์ เอี่ยมสุโร ม . 6/10  เลขที่  23 อาจารย์ที่ปรึกษา อ . ปทุมศิริ  สงศิริ
ที่มาและความสำคัญ ,[object Object],[object Object],[object Object],[object Object]
จุดประสงค์ ,[object Object]
ขอบเขตการศึกษา ,[object Object],[object Object],[object Object],[object Object]
แนวคิดและสมมติฐาน ,[object Object],[object Object],[object Object],[object Object],[object Object]
แผนภาพการทำงาน 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
แผนภาพการทำงาน 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
แผนภาพการทำงาน 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  ในการ  thinning
แผนภาพการทำงาน 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
แผนภาพการทำงาน 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  อัตราส่วน  Height / Width
แผนภาพการทำงาน 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
จำนวนจุดบน   /  จำนวนจุดล่าง Fig.3  การหาจำนวนจุดบน   /  จำนวนจุดล่าง
แผนภาพการทำงาน 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  การแบ่งโซนของ   End Point & Junction Fig.5  การแบ่งโซนของ   Connection
แผนภาพการทำงาน 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  การหา  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.
แผนภาพการทำงาน 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   การหา  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.
แผนภาพการทำงาน 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   การแบ่งโซนของ   Crossing Count
แผนภาพการทำงาน 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  ลักษณะของ  Cavity  แบบต่างๆ 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.
แผนภาพการทำงาน 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  การแบ่งส่วนการทำ  Histogram
แผนภาพการทำงาน 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  การแบ่งส่วนการทำ  Empty Zone
Normalize ,[object Object],[object Object],[object Object],[object Object]
แผนภาพการทำงาน 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
การหา  Feature  ที่เหมาะสม ตารางแสดงค่าความถูกต้องในการเลือกใช้  feature  แต่ละชนิด  (10  อันดับสูงสุด ) * 1 =  ใช้  feature   นั้น  normalize, 2 =  ใช้  feature   นั้นไม่  normalize, 0 =  ไม่ใช้  feature  นั้น % 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
แผนภาพการทำงาน 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  การทำ  Elastic Deformation
ตารางแสดงค่าความถูกต้องเมื่อมีการเพิ่มจำนวนตัวอย่าง 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
สรุปผล ,[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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Present

  • 1. การรู้จำลายมือเขียนตัวพยัญชนะภาษาไทยโดยการใช้อีลาสติกดีฟอเมชัน The Handwritten Thai-Alphabet Recognition by Using Elastic Deformation นายสุธี คงเกียรติไพบูลย์ ม . 6/3 เลขที่ 22 นายภควัฒน์ เอี่ยมสุโร ม . 6/10 เลขที่ 23 อาจารย์ที่ปรึกษา อ . ปทุมศิริ สงศิริ
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. Height / Width Height Width Fig.2 อัตราส่วน Height / Width
  • 13.
  • 14. จำนวนจุดบน / จำนวนจุดล่าง Fig.3 การหาจำนวนจุดบน / จำนวนจุดล่าง
  • 15.
  • 16. End point & Junction point, Connection Fig.4 การแบ่งโซนของ End Point & Junction Fig.5 การแบ่งโซนของ Connection
  • 17.
  • 18. Head Fig. 6 การหา 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.
  • 19.
  • 20.
  • 21.
  • 22. Crossing Count Fig.8 การแบ่งโซนของ Crossing Count
  • 23.
  • 24. Cavity Fig.9 ลักษณะของ Cavity แบบต่างๆ 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.
  • 25.
  • 26.
  • 27.
  • 28. Empty Zone Fig.11 การแบ่งส่วนการทำ Empty Zone
  • 29.
  • 30.
  • 31. การหา Feature ที่เหมาะสม ตารางแสดงค่าความถูกต้องในการเลือกใช้ feature แต่ละชนิด (10 อันดับสูงสุด ) * 1 = ใช้ feature นั้น normalize, 2 = ใช้ feature นั้นไม่ normalize, 0 = ไม่ใช้ feature นั้น % 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
  • 32.
  • 33.
  • 34. ตารางแสดงค่าความถูกต้องเมื่อมีการเพิ่มจำนวนตัวอย่าง 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
  • 35.
  • 36.
  • 37.