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- 1. Inscription Rubbing Image Analysis forSemi-Automatic Collection of AncientCharacter Images :Case Study Inscriptions in FakkhamScriptsAuthor Papangkorn InkeawIndependent Study Advisor Associate Professor Dr. Jeerayut Chaijaruwanich
- 2. INTRODUCTIONInscription Primary Evidence for 2
- 3. INTRODUCTIONEpigraphy in Thailand3
- 4. INTRODUCTIONInscription Data and 4
- 5. INTRODUCTION20-245
- 6. INTRODUCTIONView full size 6
- 7. MOTIVATION INTRODUCTIONReal Character Image from Inscription 7
- 8. MOTIVATION RESULT & CONCLUSIONOCR Inference of Inscription Creation PeriodCharacter Image Reference AncientData warehouseCharacter Character Relationship ImagesVisualizationDatabaseApplicationsFuture Work and Application 8
- 9. PROPOSED METHOD Preprocessing Segmentation Data CollectionThe Processing of Inscription 9Rubbing Image Analysis
- 10. PROPOSED METHODPreprocessing Step10
- 11. PROPOSED METHODFilteringPreprocessing Step Foreground Estimation 11
- 12. PROPOSED METHODSubtract with Original ImagePreprocessing Step Foreground Estimation 12
- 13. PROPOSED METHODPreprocessing Step Noise Reduction 13
- 14. PROPOSED METHODPreprocessing Step Binarization using Local Ost14
- 15. PROPOSED METHODArea FilterPreprocessing Step MessagesArea Detection15
- 16. PROPOSED METHODIf 4-Connected Component < TThen remove the componentsPreprocessing Step Noise Reduction 16
- 17. PROPOSED METHODPreprocessing Step The Image after Preprocess17
- 18. PROPOSED METHODSegmentation Step18
- 19. PROPOSED METHODConstruct Projection Profile Detect P LocSegmentation Step Line Detection19
- 20. PROPOSED METHODSegmentation Step Each Line Image20
- 21. PROPOSED METHODSkew Detectionusing Hough TransformSegmentation Step Line Level Detection 21 Construct Projection
- 22. PROPOSED METHODUpper Level Median Level Lower LevelConstruct Projection Construct Projection Construct ProjectionProfileProfileProfileFind LocalFind Local Find LocalSegmentationMinimum PointStep HeuristicMinimum Point Segmentation Minimum Point 22
- 23. PROPOSED METHODSegmentation Step Character Segmentation 23
- 24. PROPOSED METHODSQL Language Relational DatabaseCollection Step24
- 25. PROPOSED METHODCollection Step Inscription Database Schema 25
- 26. PROPOSED METHODCollection Step 26
- 27. RESULT & CONCLUSIONStatistical Value Start Accuracy (%) Final Accuracy (%) Correction Rate (%)Median18.0367.27 13.49S.D.20.4111.55 5.00Result Table Accuracy Value 27
- 28. EXAMPLE 28
- 29. ACKNOWLEDGMENT INTRODUCTIONAssociate Professor Dr. Jeerayut ChaijaruwaDr. Churee TechawutDr. Varin ChouvatutDr. Trongjai HutangkuraDr. Sanparith MarukatatThank you 29