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
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Generated Image
word
Preprocessing and Word segmentor
Word Grouping
ClusteringGroups and Clusters for
Holistic Recognition
Proposed ApproachProposed Approach::
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GroupingGrouping::Extraction subwords
(PAW) Extraction dots and
diacritics Used it to select the
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GroupingGrouping::
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Secondaries separation using contour analysis
Secondaries Recognition using
SVM
Grouping ProcessGroups
Preprocessing and Word segmentor
Generated Image Word
Grouping Grouping ExampleExample::
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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
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Challenges Sticking
Sensitive to noiseTreatments
PAWsDown secondaries Upper secondaries
Grouping based on:
Overlapping SVM
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ClusteringClustering:: Complementary of grouping LBG algorithm used Done on groups contain large words Euclidean distance used
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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
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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
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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
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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.
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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 .
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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
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The moment invariant descriptors are calculated and fed to the feature vector. 16
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