the second ph.d seminar document image analysis and ... .pdf · degraded image caused by...

Document image analysis and recognition

Songtao Huang Supervisor: Dr. Ahmadi

Electrical and computer engineeringUniversity of Windsor

The Second Ph.D Seminar

Procedure of document image analysis

Post-Processing

Pre-processing

Character recognition

or Object Recognition

Document acquisition

Binarization

Page Segmentation

(Layout Analysis)

Overview

1. Challenge of binarization.

2. Existing binarization algorithms.

3. Proposed HMM based binarization method.

4. Edge based binarization algorithm.

5. Future work---2D HMM based OCR.

Binarization

Convert gray images into

binary images for further process.

Challenges of binarization (1)

Degraded image caused by non-uniform illumination

Challenges of binarization (2)Image with low contrast and stroke dependent noises

Challenges of binarization (3)Image with variable background intensity

Conventional Binarization Algorithms

1. Histogram shape information[1][2][3].2. Histogram entropy information[4][5]. 3. Clustering-based methods[6].4. Thresholding Based on Attribute Similarity[7-12]5. Spatial information[13]. 6. Local adaptive thresholding[14-17].

Histogram shape information

The peaks, valleys and curvatures of the smoothed histogram are analyzed.

Convex hull thresholding[1].Peak-and-valley thresholding[2].Shape-modeling thresholding[3].

0 50 100 150 200 250 3000

500

1000

1500

2000

2500

3000Original Histogram

Histogram entropy information

Entropy-based methods result in algorithms that usethe entropy of the foreground and background regions,the cross-entropy between the original and binarizedimage, etc.

1) Entropic thresholding[4].2) Cross-entropic thresholding[5].

i

j

iij yyE log

0∑=

= jnjn

jnj

j

jtotal AA

AAEE

AAE

E −−−

−+−= loglog

Clustering-based methods[6].

The gray-level samples are clustered in two parts as background and foreground object, or alternately are modeled as a mixture of two Gaussians.[6]

Thresholding Based on Attribute Similarity

These algorithms select the threshold value based on some attribute quality or similarity measure between the original image and the binarized version of the image.

Moment preserving thresholding[7].Edge field matching thresholding[8].Fuzzy similarity thresholding[9][10].Topological stable-state thresholding[11].Maximum information thresholding[12].

Spatial Thresholding Methods

This class of algorithms utilizes not only gray value distribution but also dependency of pixels in a neighborhood, for example, in the form of context probabilities, correlationfunctions, cooccurrence probabilities, local linear dependence models of pixels, 2-D entropy, etc.

Cooccurrence thresholding methods[13].

local Adaptive Thresholding

A threshold is calculated at each pixel, which depends on some local statistics like range, variance, or surface-fitting parameters of the pixel neighborhood.

Local variance methods[14]. Local contrast methods[15]. Center-surround schemes[16].Surface-fitting thresholding[17].

Proposal 1: HMM based binarization algorithm

A

B C

Neighborhood of different kinds of pixels(A)

Neighborhood of different kinds of pixels(B)

Neighborhood of different kinds of pixels(C)

Feature extraction

Seven elements in vertical direction feature vector

)0,0()0,1(1 )4( PPv −=

)0,0()0,1()0,2(

1 2)(

)1( PPP

v −+

= −−

)0,0()0,1()0,2()0,3(

1 3)(

)0( PPPP

v −++

= −−−

)0,0()0,1()0,2()0,3(

1 3)(

)6( PPPP

v −++

=

)0,0()0,1()0,2(

1 2)(

)5( PPP

v −+

=

)0,0()0,1(1 )2( PPv −= −

)0,0(1 )3( Pv =

Four direction feature vectors

3,60,|3|

)(3

11)0,0(

),0(1 ≠≤≤−

−= ∑

−

−=

iiPiP

ivi

orj

j

3,60,|3|

)(3

11)0,0(

),(2 ≠≤≤−

−= ∑

−

−=

iiPiP

ivi

orj

jj

3,60,|3|

)(3

11)0,0(

)0,(3 ≠≤≤−

−= ∑

−

−=

iiPiP

ivi

orj

j

3,60,|3|

)(3

11)0,0(

),(4 ≠≤≤−

−= ∑

−

−=

− iiPiP

ivi

orj

jj

Feature vectors

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]6[ ]5[ ]4[ ]3[ ]2[ ]1[ ]0[

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vvvvvvvvvvvvvvvvvvvvv

vvvvvvv

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4

3

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1

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Quantization

. Evaluation problem. Given the HMM M=(A, B, π) and the observation sequence O=o1 o2 ... oK , calculate the probability that model M has generated sequence O .

• Decoding problem. Given the HMM M=(A, B, π) and the observation sequence O=o1 o2 ... oK , calculate the most likely sequence of hidden states si that produced this observation sequence O.

• Learning problem. Given some training observation sequences O=o1 o2 ... oK and general structure of HMM (numbers of hidden and visible states), determine HMM parameters M=(A, B, π) that best fit training data. O=o1...oK denotes a sequence of observations ok∈{v1,…,vM}.

Main issues using HMMs :

HMM based binarization

Feature vector

Hidden Markov Model of foreground

Number of observations is 10.Number of states is 3

Hidden Markov Model of background

Comparison of the possibility

Flow of training procedure

Pixels extraction

K-mean to obtain 10 central vectors

Pixels quantization

Training HMM to obtain the parameters in the models

Input vectors [0000]---[9999]

Save the attributes of the vectors [0000]---[9999]

Classification through HMM

Comparing the distances of each vector to the ten cluster centers acquired in the training step, we derive the observation sequence for every pixel. Since attribute of each pixel can be found in the look-up table savedin the reference set at the training step, the recognition result can be obtained with minimum time consumed in this stage.

Feature extraction

Look up the table of reference

Vector quantization

Proposed binarization algorithm 1(HMM based binarization algorithm)

In the first stage, a coarse global thresholding method is used to discriminate the bright part of the whole image from the foreground pixels which have lower values.

In the second stage, the left unconfirmed pixels which are supposed to be a mixture of foreground and part of the background are input into the HMM pixel classifier toget the attribute of each pixel.

A coarse global threshold.

HMM based

classifier

∑∑

=

== 255

0

255

0

)(

)(

i

i

ih

iihMean

0 50 100 150 200 250 3000

500

1000

1500

2000

2500

3000Original Histogram

Simulation resultsK

ittle[19]

HM

MLocal[21]

Otsu[20]

Simulation resultsK

ittle[19]

HM

MLocal[21]

Otsu[20]

FAIL

Simulation results

HMM

Kittle[19]

HM

MLocal[21]

Otsu[20]

Proposal 2: Edge based binarization algorithm

Proposed binarization algorithm 2(Edge based binarization algorithm)

Step 1: Edge dectection- Prewitt detector

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],[1

1

1

1∑ ∑−= −=++−−+

= k kjkiIjkiI

jiP6

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= k kkjiIkjiI

jiQ

22 ],[],[],[ jiQjiPjiM += )],[],[arctan(],[

jiQjiPjiO =

111000-1-1-1

10-110-110-1

HORIZONTAL ORIENTATIONAL KERNEL VERTICAL ORIENTATIONAL KERNEL

Thresholding for Edge detection

Here the simplest root mean square (RMS) value is utilized as shown below, the threshold

heightwidth

jiMT

width

i

height

jedge ×

×=

∑ ∑= =1 1),(4

Gradient determination

P(i,j)

011-101

-1-10

11010-10-1-1

111000-1-1-1

10-110-110-1

Q(i,j)

S(i,j)R(i,j)

|)),(||,),(||,),(||,),((|),( jiSjiRjiQjiPMaxjiG =

Select the minimum and maximum values fromthe determined gradient direction

Demo of pixels selection

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1007967

123|),(| =jiQ

19|),(| =jiS

199|),(| =jiR

164|),(| =jiP

|),(|),( jiRjiG =

Foreground pixel Background pixel

Selected foreground pixels Selected background pixels

Histogram of selected pixels

The histogram of the original image

∑=

≤≤=j

ibackback iHjE

0255j0 )()(∑

=

≤≤=j

iforefore iHjE

255255j0 )()(

Determination of threshold

)()()(:as calculated becan error general the

iEiEiEEThen

backforetotal

total

+=

Flow of edge based thresholdingalgorithm

Edge detection

Corner detection

Edge thresholding

Foreground and background pixels determination

Determination of threshold

Result

Enhanced edge based thresholding

Combination of Kittle and zoning methods.

Zoned edge information

My contributions and achievements

1) A HMM based binaization algorithm

2) An new edge information based binarization algorithm

3) Both algorithms have good performances in comparison with the existing algorithms.

Future job: A new polar 2D HMM system.

• The structure of hidden states is chosen.

• Observations are feature vectors extracted from vertical slices.

Character recognition with 1DHMM[22]

Character recognition with 2D Pseudo HMM[22]