Using Word Based Features for Word Clustering The Thirteenth Conference on Language Engineering 11-12, December 2013 Department of Electronics and Communications,

Using Word Based Features for Word Using Word Based Features for Word ClusteringClustering

The Thirteenth Conference on Language The Thirteenth Conference on Language Engineering 11-12, December 2013Engineering 11-12, December 2013

Department of Electronics and Communications, Faculty of Engineering

Cairo University

Research Team:Farhan M. A. NashwanProf. Dr. Mohsen A. A. Rashwan

Presented By:Farhan M. A. Nashwan

ContributionContribution::Reduce vocabulary

Increase speed

2The Thirteenth Conference on Language Engineering 11-12, December 2013

Generated Image

word

Preprocessing and Word segmentor

Word Grouping

ClusteringGroups and Clusters for

Holistic Recognition

Proposed ApproachProposed Approach::


GroupingGrouping::Extraction subwords

(PAW) Extraction dots and

diacritics Used it to select the

group4The Thirteenth Conference on Language Engineering 11-12, December 2013

GroupingGrouping::


Secondaries separation using contour analysis

Secondaries Recognition using

SVM

Grouping ProcessGroups

Preprocessing and Word segmentor

Generated Image Word

Grouping Grouping ExampleExample::

6

Grouping code (1,21,2)

Grouping Code (3,0, 2)

Grouping Code (4,11, 12)Grouping Code (3,2, 21)Grouping Code (2,0,

2)

PAW=1Upper Sec.=2

PAW=3Down Sec.=0

Upper Sec.=2

PAW=4

Down Sec.=1&1

Upper Sec.=1 & 2PAW=3Down Sec.=2Upper Sec.=2 &1PAW=2Down Sec.=0Upper

Sec.=2

Down Sec.= 2 & 1

The Thirteenth Conference on Language Engineering 11-12, December 2013

7

Challenges Sticking

Sensitive to noiseTreatments

PAWsDown secondaries Upper secondaries

Grouping based on:

Overlapping SVM

The Thirteenth Conference on Language Engineering 11-12, December 2013

ClusteringClustering:: Complementary of grouping LBG algorithm used Done on groups contain large words Euclidean distance used


Groups Feature Extracti

on

Clustering using LBG

Clusters & Groups

FeaturesFeatures: : 1- (ICC): Image centroid and Cells2- (DCT):Discrete Cosine Transform 3- (BDCT):Block Discrete Cosine Transform 4-(DCT-4B): Discrete Cosine Transform 4-Blocks 5- (BDCT+ICC):Hybrid BDCT with ICC.6- (ICC+DCT): Hybrid DCT with ICC7- (ICZ):Image Centroid and Zone 8- (DCT+ICZ): Hybrid DCT and ICZ. 9- (DTW ):Dynamic Time Warping 10- The Moment Invariant Features


ResultsResults: : Features

Cluster Rate (%)

Total ER (%)

Clustering ER(%)

Group ER (%)

Word/Cluster

ICC98.71.310.550.75115BDCT96.83.222.470.75118DCT99.20.810.050.75129

DCT-4B98.71.30.550.75113ICC+BDCT98.31.660.910.75117ICC+ DCT99.00.980.230.75114

IZC96.73.282.530.75116IZC+DCT98.71.340.590.75115

DTW98.11.921.170.75154Moments82.617.3

916.640.75176

TABLE 1: CLUSTERING RATE OF SIMPLIFIED ARABIC FONT USING DIFFERENT FEATURES


Features

Cluster Rate(%)

Word/ClusterFeat_Ext_Time (ms)

Clus_Ave_Time(ms)

To_Ave_Time (ms)

ICC98.7

1150.040.250.29

BDCT96.8

1180.380.130.51

DCT99.2

12911.950.0311.99

DCT-4B98.7

1131.870.021.90

ICC+BDCT98.3

1170.410.240.66

ICC+ DCT99.0

1141.900.262.16

IZC96.7

1160.010.040.05

IZC+DCT98.7

1151.870.061.94

DTW98.1

1450.054.044.09

Moments82.6

1760.130.150.29

TABLE 2: PROCESSING TIME FOR FEATURE EXTRACTION AND CLUSTERING OF SIMPLIFIED ARABIC FONT USING DIFFERENT

FEATURES


ConclusionConclusion:: based on their holistic features:Recognition speed increasedunnecessary entries in the vocabulary removedTotal average time of ICC or Moments (0.29 ms) is better than that of other methods.but the clustering rates are not the best (98.69% for ICC and 82.61% for Moment).the clustering rate of DCT (99.19%) is the better, but time is the worst (~12 ms).With two parameters (clustering rate and time) ICC may be a good compromise.


Thanks for your Thanks for your attentionattention....


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counting the number of black counting the number of black pixelspixels

Vertical Vertical transitiotransitions from ns from black to black to whitewhite

horizontahorizontal l transitiotransitions from ns from black to black to whitewhite


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DCT

.-Applying DCT to the whole word image-The features are extracted in a vector form by using

the DCT coefficient set in a zigzag order.-Usually we get the most significant DCT

coefficients(160 coef.)


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Block Discrete Cosine Transform (BDCT) Apply the DCT transform for Apply the DCT transform for

each celleach cell

Get the Get the average average of the of the differencdifferences es between between all the all the DCT DCT coefficiecoefficientsnts


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Discrete Cosine Transform 4-Blocks (DCT-4B)

1 -Compute the center of gravity of the input image.2 -Divide the word image into 4-parts taking the center of

gravity as the origin point.3 -Apply the DCT transform for each Part.

4 -Concatenate the features taken from each part to form the feature set of the given word.


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Image Centroid and Zone (ICZ)Compute the average distance among these

points (in a given zone) and the centroid of the word image


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DTW (Dynamic Time Warping) Features .

The three types of features are extracted from the binarized images and used in our DTW techniques :

X-axis and Y-axis Histogram ProfileProfile Features(Upper, Down, Left and Right)Forground/Background Transition

DTW) is an algorithm for measuring similarity between two sequencesThe distance between two time series x1 . . . xM and y1 . . . yN is D(M,N), that is calculated in a dynamic programming approach using


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DTW (Dynamic Time Warping) Features .


Figure 1: The Four Profiles Features: (A) Left Profile. B) Up (C) Down Profile. D) Right Profile

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The Moment Invariant Features

Hu moments: Hu defined seven values, computed from central moments through order three


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Moments


The moment invariant descriptors are calculated and fed to the feature vector. 16

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Using Word Based Features for Word Clustering The Thirteenth Conference on Language Engineering 11-12, December 2013 Department of Electronics and Communications,