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LYU0203 Smart Traveller with Visual Translator for OCR and Face Recognition

Department of Computer Science & Engineering The Chinese University of Hong Kong. LYU0203 Smart Traveller with Visual Translator for OCR and Face Recognition. Supervised by Prof. LYU, Rung Tsong Michael. Prepared by: Wong Chi Hang Tsang Siu Fung. Outline. Introduction

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LYU0203 Smart Traveller with Visual Translator for OCR and Face Recognition

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  1. Department of Computer Science & Engineering The Chinese University of Hong Kong LYU0203Smart Traveller with Visual Translatorfor OCR and Face Recognition Supervised by Prof. LYU, Rung Tsong Michael Prepared by: Wong Chi Hang Tsang Siu Fung

  2. Outline • Introduction • System Architecture • Korean OCR • Friend Reminder • Conclusion • Acknowledgement

  3. Introduction – What is VTT? • Smart Traveller with Visual Translator (VTT) • Mobile Device which is convenient for a traveller to carry • Mobile Phone, Pocket PC, Palm, etc. • Recognize and translate the foreign text into native language • Detect and recognize the face into name

  4. Introduction – Objective • Two main features: • Korean to English Visual Translation • Remind Somebody’s Information with Face Image

  5. Introduction – Objective (Cont.) • Real Life Examples • Sometimes we lose the way, we need to know where we are. • Sometimes we forget somebody we met before.

  6. User Request Response GUI Output Request Request Request Response Request Response Data Korean OCR Face Recognizer Camera API Result Query Result Query Update Data Request Stroke Database & Dictionary Face Database Camera System Architecture

  7. Korean OCR (KOCR) • Usage • Visual Translator from Korean to English • Procedure for using KOCR • Text Area Detection • Character Identification • Translation

  8. Initialization Capture Image Text Segmentation Recognition Translation KOCR – Program Flow

  9. Horizontal Projection Threshold Vertical Projection KOCR – Text Area Detection • Edge Detection using Sobel Filter • Horizontal Projection and Vertical Projection • Find Potential Text Area by threshold

  10. KOCR – Text Area Detection (Cont.)

  11. KOCR – Character Identification • Features on Stroke • Extracted by Labeling Connected Component algorithm • Proposed Feature Extraction • Five rays each side • Difference of adjacent rays (-1 or 0 or 1) • Has holes (0 or 1) • Dimension ratio of Stroke (width/height) (-1 or 0 or 1)

  12. KOCR – Character Identification (Cont.)

  13. KOCR – Translation • Dictionary • Korean to English • About 1000 Korean Words • Matching • Longest Match from left to right

  14. KOCR – Translation (Cont.)

  15. KOCR – Evaluations • OCR Correctness • Training Set (3327 – 30% of all Character) • Testing Set (7845 – Others) • Result (64%) • Suggestion • Train all Korean characters

  16. KOCR – Evaluations (Cont.) • Text Segmentation Correctness • 45 Captured Images • 99 Characters • Result • Segment 83% characters correctly • Segment 71% image correctly • Acceptable Result

  17. KOCR – Evaluations (Cont.) • OCR Correctness • 45 Captured Images • 99 Characters • Result • 79% Characters correctly Recognized • 69% Images correctly Recognized

  18. Initialization Capture Image Face Segmentation Recognition Show Profile Friend Reminder – Program Flow

  19. Friend Reminder (FR) • Usage • Show the Profile of Friend by capturing a photo • Procedure for using FR • Face Segmentation • Face Identification • Friend’s Profile

  20. FR – Face Segmentation • Eye Detection • Algorithm • Gabor Wavelet Feature • Log-Polar Sampling • Manual Selected (Suggest) • Selected Eyes and Mouth Positions

  21. FR – Face Segmentation

  22. FR – Face Identification • EigenFace • By using Principal Component Analysis (PCA) • Project the input face into the eigenvectors that pre-learned • Find the difference between the projection and the faces in database • Face determined to be ‘NEW’ if the difference is larger than a threshold

  23. FR – Friend’s Profile

  24. FR – Evaluations • Eye Detection Correctness • 40 Images • Result • 22.5% Image Successfully Detected • Non-acceptable • Suggestion • Manually Select Eyes and Mouth Positions

  25. FR – Evaluations • Face Identification • Evaluation Information • 26 Test Persons’ Faces • 16 faces is in database • 10 faces is not in database • 3 faces Trained per person • 8 persons in face database • Result • 77% Successfully Identified • 63% Successfully Identified as person in database • 100% Successfully Identified as person not in database

  26. Conclusion • Combined Modern Equipments • Digital camera • Personal Data Assistant (PDA) • Techniques Learned • Image Processing • Optical Character Recognition • Face Recognition Techniques • VTT Integrated • VTT for Korean to English OCR • VTT for Friend Reminder

  27. Acknowledgement • Thanks Professor Michael Lyu,Project Supervisor • Give us valuable advice • Provide us necessary equipments • Thanks Edward Yau,Technical Manager of VIEW project • Give us many ideas

  28. ~The End~

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