Word Recognition Word Recognition Using Fuzzy LogicUsing Fuzzy LogicWord Recognition Word Recognition Using Fuzzy LogicUsing Fuzzy Logic

作者：作者： R. Buse, Z. Liu, and J. BezdekR. Buse, Z. Liu, and J. Bezdek報告人：余家豪報告人：余家豪

The offline recognition of handwritten cursive word

• Segment the word into its character parts.

• Word-based.

Word-based approach’s challenges

• The complexity more greater.• Have lower discrimination

capabilities.• It is usually restricted to few word

groups.

How to extract word feature ?

• First step: Slant and Tilt Correction• Second step: Using Gabor filter• Third step: Word Alignment

Two-dimensional (2-D) fuzzy membership

function

Use to represent both sizes and

positions of the extracted word feature.

First step: Slant and Tilt

CorrectionSilver

Vegas

Second step: Using Gabor filter

The extracted feature image

Angle of Gabor filter Ø=90°

Third step: Word Alignment(1)

K-means clustering algorithm

( Centroids )

The horizontal andvertical Alignment points

Formula

Third step: Word Alignment(2)

Using alignment points to transform the extracted feature images into a standard data structure of aligned image features.

How to form fuzzy membership function

?• First step:

Composite aligned images• Second step:

Determine threshold points• Third step:

Corner point Correspondences• Fourth step:

Membership Value

First step: Composite aligned

imagesBaton

(a)

(f)(e)

(d)

(c)(b)

(g) - Rij

Second step: Determine threshold

points

Cu = 0.25

Cl = 0.1

Huij : upper threshold point

Hlij : lower threshold point

Rij

Found boundary

Third step: Corner point

Correspondences

C: set of valid combination pairs (i,j) (24 possible combinations)

T: top rectangle point

B: bottom rectangle point

|| · || distance between the two corners

The sideof theMembershipfunction

Fourth step: Membership Value

2-D membershipfunction

μ(x,y): memebership value at point (x,y)

Matching test words to the membership

function

( at Ø=90° )