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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

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

outline
Outline
  • Introduction
  • System Architecture
  • Korean OCR
  • Friend Reminder
  • Conclusion
  • Acknowledgement
introduction what is vtt
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
introduction objective
Introduction – Objective
  • Two main features:
    • Korean to English Visual Translation
    • Remind Somebody’s Information with Face Image
introduction objective cont
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.
system architecture

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
korean ocr kocr
Korean OCR (KOCR)
  • Usage
    • Visual Translator from Korean to English
  • Procedure for using KOCR
    • Text Area Detection
    • Character Identification
    • Translation
kocr program flow

Initialization

Capture Image

Text Segmentation

Recognition

Translation

KOCR – Program Flow
kocr text area detection

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
kocr character identification
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)
kocr translation
KOCR – Translation
  • Dictionary
    • Korean to English
    • About 1000 Korean Words
  • Matching
    • Longest Match from left to right
kocr evaluations
KOCR – Evaluations
  • OCR Correctness
    • Training Set (3327 – 30% of all Character)
    • Testing Set (7845 – Others)
    • Result (64%)
    • Suggestion
      • Train all Korean characters
kocr evaluations cont
KOCR – Evaluations (Cont.)
  • Text Segmentation Correctness
    • 45 Captured Images
    • 99 Characters
    • Result
      • Segment 83% characters correctly
      • Segment 71% image correctly
    • Acceptable Result
kocr evaluations cont1
KOCR – Evaluations (Cont.)
  • OCR Correctness
    • 45 Captured Images
    • 99 Characters
    • Result
      • 79% Characters correctly Recognized
      • 69% Images correctly Recognized
friend reminder program flow

Initialization

Capture Image

Face Segmentation

Recognition

Show Profile

Friend Reminder – Program Flow
friend reminder fr
Friend Reminder (FR)
  • Usage
    • Show the Profile of Friend by capturing a photo
  • Procedure for using FR
    • Face Segmentation
    • Face Identification
    • Friend’s Profile
fr face segmentation
FR – Face Segmentation
  • Eye Detection
    • Algorithm
      • Gabor Wavelet Feature
      • Log-Polar Sampling
    • Manual Selected (Suggest)
      • Selected Eyes and Mouth Positions
fr face identification
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
fr evaluations
FR – Evaluations
  • Eye Detection Correctness
    • 40 Images
    • Result
      • 22.5% Image Successfully Detected
    • Non-acceptable
    • Suggestion
      • Manually Select Eyes and Mouth Positions
fr evaluations1
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
conclusion
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
acknowledgement
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