AN APPROACH FOR SLANT CORRECTION USING

PROJECTIVE TRANSFORMATION

Lalit Kumar, Abhay Bansal and Neeraj Jain

Newgen Software Technologies Limited, A-6, Satsang Vihar Marg, Qutab Institutional Area, New Delhi–110067, India

Keywords: Global Slant, Local Slant, Legal Amount Recognition, LAR, Ascender, Descender.

Abstract: Slant correction is a major challenge faced during handwritten text recognition process. Most of the

traditional techniques try to estimate the global slant angle for the whole word, and rotate the word by this

angle to remove the slant. On the other hand, certain other techniques estimate the slant angle at each

abscissa using various techniques like DP technique. This paper presents an approach for correction of non-

uniform slants in words using a hybrid of traditional global slant correction techniques and local slant

approximation techniques. In this paper, we focus on correcting the slant of words that appear on check

image under Legal Amount Region.

1 INTRODUCTION

Banking and Financial Services industries process

huge volumes of checks on a daily basis. Checks are

physically transported from one place to another,

and check amount is manually read and keyed-in.

The manual procedure is cost-intensive as well as

time consuming. Therefore, an automated system,

which can recognise the amount mentioned under

Courtesy Amount Recognition (CAR) region and

Legal Amount Recognition (LAR) region, is highly

desirable. If the amount is machine-printed text, an

OCR engine can be deployed to easily extract the

amount from CAR and LAR. However, a vast

majority of checks have handwritten text, and

therefore, cannot be processed using an OCR engine.

For recognizing handwritten text, an ICR engine is

deployed. However, most of the ICR engines

available today work well only with segmented

characters, and are not able to recognize the natural

handwriting. ICR engines need segmented text

without any slant to correctly recognize the text.

Therefore, determination and removal of slant in

handwritten text is a challenging task essential to

accuracy of an ICR engine.

Exclusively not much work has been done in the

area of slant correction, and a little number of

algorithms have been proposed explictly to cater this

problem. These algorithms for slant estimation and

correction techniques can be broadly grouped under

two classes: uniform and non-uniform. Uniform

slant correction techniques estimate the global slant

angle of the words present in the image, and rotate

the image with the same angle (Uchida et al., 2001).

On the other hand, non-uniform slant correction

techniques try to estimate the angle at every abscissa

and correct the slant abscissa by this angle

(Bozinovic and Srihari, 1989), (Bertolami et al.,

2007). The major drawback with uniform slant

correction techniques is the assumption that the slant

angle of handwritten text is uniform throughout the

image. On the other hand, non-uniform slant

correction techniques estimate slant at every point,

which results in increase in processing time, which

is not acceptable, especially in industries such as

banks. Here, we propose a hybrid technique that

effectively and efficiently addresses the problems of

non-uniform slant and time-consuming processing in

uniform and non-uniform techniques respectively.

The organisation of the paper is as follows: In

section 2, we discuss the details about the proposed

algorithm; in section 3, we describe the experimental

results; and in section 4, we provide the conclusion

of the proposed approach.

2 PROPOSED APPROACH

The proposed algorithm uses a hybrid approach

comprising both uniform and non-uniform slant

correction techniques. Instead of estimating slant

angle at every point, the proposed approach

Kumar L., Bansal A. and Jain N.

AN APPROACH FOR SLANT CORRECTION USING PROJECTIVE TRANSFORMATION.

DOI: 10.5220/0002156600850089

In Proceedings of the Fourth International Conference on Computer Vision Theor y and Applications (VISIGRAPP 2009), page

ISBN: 978-989-8111-69-2

calculates a vector having one global slant angle and

two or more local slant angles. The slant correction

is achieved by propagating the effect of local slant

angles to the region between these slant angles by

using projective transformation.

2.1 Pre Processing

The input image can either be Black & White or

gray. If the image is gray, it is binarized using

modified Niblack algorithm (Chandra et al., 2007),

which works well for most of the handwritten text

images. In case the images are Black & White, they

are already binarized. Once the binarized image is

obtained, noise removal algorithm is applied to clean

the spatial noise, characterized by small components

of size (1 to 3 pixels). A morphological filter (Cote

et al., 1997) is applied to do the same. There may be

some skew present in the image. To correct the

skew, following algorithm is applied.

Algorithm: Deskew

1. Find the centroid, c, of the image

2. Taking c as the origin

3. For θ Å θ

to θ

4. Calculate sum of distances, ∑r

of each

black pixels lying on line l, which makes an

angle θ with horizontal axis at c.

5. End For

6. Rotate the image with angle θ, where ∑r

minimum

2.2 Base Line & Top Line Estimation

Under LAR region, only a specific set of words is

possible, which can be either in upper case or lower

case. If it is lower case, we need to find a base line,

as shown in figure 1, which touches the bottoms of

majority of the characters, leaving out only certain

characters such as y, g, and p, which extend beyond

the base line.

Figure 1: Base lines for lower case words.

Similar to base line, Top line, as shown in figure

2, is the line that touches the tops of majority of the

characters, leaving out only certain characters such

as b, d, and h, which extend beyond the top line.

Figure 2: Top lines for lower case words.

The base line and top line estimation can be done

using following algorithm

Algorithm: Base line and Top line estimation.

I: input image.

S: skeleton (Pyeoung, 1997), (Datta

and Parui, 1994) of the input image

1. Find the skeleton, S, of the input image I.

2. For i Å 1 to m /* for all rows */

3. H

Å number of black to white transitions +

number of white to black transitions in

ith row of I

4. End For

5. T

Å 0.25 * max(H)

6. If H

< T

Then remove H

from H.

7. T

Å 0.7 * average(H)

8. B Å set of rows, where H

…H

> T

and (p +

q) > 0.35 * Height of Image.

9. Top Line Å Line passing through p

10. Base Line Å Line passing through q

2.3 Strokes Detection

Let N represent the number of black pixels lying on

any straight line. Consider a line L that contains

maximum number of black pixels and has a slope,

m, such that m

<m<m

, where m

and m

are slopes

of base line and top line respectively. For each

abscissa, i, on line L, an angle, ω (between an

imaginary line, p, passing through the abscissa and

Y axis) is found such that –α<ω<(α + β), where N is

maximum. β is the additional angle considered to

cater to the general observation that the slant of the

handwritten text is more towards right than towards

left.

Figure 3: Stroke detection, ω is the angle between vertical

axis and line p. ω can vary from -α to α+β.

2.4 Strokes Selection

Once vector V having the strokes for all the

abscissas on line L and in all directions from -α to

+(α+β) is obtained, projective transformations are

applied. Before applying slant correction,

≤

+β

Top line

Base line

≤

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

appropriate strokes are to be selected from the group

of stokes identified. The algorithm for stroke

selection is as follows:

Algorithm: Stroke Selection

length(e): returns the stroke length of the input

element e.

abscissa: returns the position of on line t where

stroke intersects t.

elements(S): returns the number of elements in S.

1. kVal=1

2. threshold_length

(height of word –

descender height) /kVal

3. stroke_length Å threshold_length

4. For j = 1 to n in V

5. len Å length (current_element)

6. If (len > stroke_length) Then

7. A

Å abscissa (current_element)

8. For i = 1 to k in M

9. If (A

== abscissa (Mi)) And (length

) < len) Then

10. Replace (M

, current_element);

11. Go to step 2

12. End If

13. End For

14. Add the current_element to M

15. Else

16. Discard the element

17. End If

18. End For

As shown in the above algorithm, a maximum of

one slant for every abscissa, having maximum stroke

length, is identified and stored in vector M. If either

the number of elements in M is one or all the

elements of M are not evenly distributed throughout

the signature, threshold stroke length is increased by

a factor, kVal, and values for α and β are set

accordingly. This process continues until evenly

distributed strokes are obtained.

Algorithm: Stroke Selection (continued)

19. If (elements (M) < 2) Or (std (abscissa (M)) < ¼

(word_width)) Then

20. Modify

and

values

21. If kVal< kMax

22. kVal

kVal+ 0.1

23. Again apply Strokes Detection

24. Go to step 1

25. Else

26. Apply Global Slant Correction

27. End If

28. End If

In case the word contains no descenders, i.e., no

characters below base line, we can immediately

proceed with slant correction using M. However, if

the words contain descenders, e.g. thirty or forty, the

values for α and β need to be fine-tuned and slant-

selection criterion optimised. If two appropriate

strokes are not found, global slant correction is

applied, which is presented next.

Algorithm: Global Slant Correction

: maximum slant on the left side

: maximum slant on the right side

H: Height of the text.

W: Width of the text

I: Input image

Vert_hist: returns the vertical histogram

max_of_two: returns the greater number

col(element): returns the abscissa of element

1. p

Å H tan

2. p

Å H tan

3. g Å zeros(H, W)

4. loop Å p

5. For i = 1 to loop

6. For m = 1 to H×W

7. a = col(m)-row(m) ×tan(

/180)

8. g(row(m), a) = m

9. End For

10. temp Å max (Vert_hist (g)

11. loc Å i;

12. V

max

Å max_of_two (temp, V

max

)

13. End For

14. V

Å V

max

15. loc

Å loc

16. Flip I along vertical axis

17. loop Å p

18. Repeat steps 5 to 12

19. V

Å V

max

20. loc

Å loc

21. If V

> V

Then

22. Rotate I by angle tan

-1

(loc

/H)

23. Else

24. Rotate I by angle tan

-1

(loc

/H)

25. End

2.5 Image Transformation

If a two-dimensional surface is observed from a

large distance with arbitrary orientation in the third

dimension, the object appears to be distorted by a

combination of translation, shear and scaling. Linear

conformal transformation includes rotation, scaling,

and translation only with same shapes and angles.

AN APPROACH FOR SLANT CORRECTION USING PROJECTIVE TRANSFORMATION

Affine transformation maps any coordinate system

in a plane to another coordinate system that can be

found from above projection. Under affine

transformation, parallel lines remain parallel and

straight lines remain straight. When an object at a

finite distance in a plane is seen from an arbitrary

direction, we get an additional "keystone" distortion

in the image. This is a projective transform, which

keeps straight lines straight but does not preserve the

angle between the lines. This warping cannot be

described by a linear affine transformation.

Therefore, projective transformation has been used

to propagate the effect of local adjacent slant angles

to the region between the corresponding local slants.

3 EXPERIMENTAL RESULTS

The proposed algorithm has been tested with more

than 1000 images containing the check amount in

words. The database of images includes both

machine-printed words and handwritten words. A

random set of handwritten words was selected, and

the results for the same are presented in Table1. The

samples were collected from 20 different people

who used pens with different thickness. As it can be

seen from the table, local slant correction works well

with images that contain words, such as thousand,

hundred, etc, which have sufficient number of long

strokes. For images containing words such as ‘one’

and ‘six’, which have very low probability of finding

a long vertical stroke, global slant correction

technique works well. The results for some sample

input images (Figure 4) are shown in Figure 5. As

evident, for images (i), (ii), (iii) in Figure 4,

sufficient number of local slants are present,

therefore local slant correction was applied.

Conversely, for image (iv) in Figure 4, no local slant

is present, therefore global slant correction is

applied.

The obtained results demonstrate high accuracy

of method, both qualitative and quantitative. Even

high degrees of slants in images were corrected, and

accurate results were clearly visible. Also, the

method was employed in a handwritten-word-

recognition system, which extracts features such as

vertical slants, descenders, etc., to recognize words.

After applying our method, there was a 15%

improvement in system’s handwritten-word-

recognition (CAERE & Kadmos) accuracy. The

improved accuracy is due to the method’s ability to

correctly choose between local and global slant

correction to be applied to a word.

(i)

(ii)

(iii)

(iv)

Figure 4: Input images and the selected strokes. (i)&(ii)

Images with three strokes; (iii) Image with four strokes, all

in almost same direction; (iv) Image without any stokes.

(i)

(ii)

(iii)

(iv)

Figure 5: (i), (ii) & (iii) Images with local slant correction

applied; (iv) Image with global slant correction applied.

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

The timing details presented in table 2 has been

taken on Pentium IV system, 512MB RAM and no

application running on the system. All the images

used in experiment are 200 DPI images.

Table 1: Experimental Results.

Handwritten

word

Data Set

(376 images)

Global/Local

slant correction

(142/234)

‘one’ 13 11/2

‘two’ 12 8/4

‘three’ 12 2/10

‘four’ 12 9/3

‘five’ 12 8/4

‘six’ 12 12/0

‘seven’ 12 8/4

‘eight’ 17 4/8

‘nine’ 10 6/4

‘ten’ 12 6/6

‘eleven’ 12 4/8

‘twelve’ 12 2/10

‘thirteen’ 12 2/10

‘fourteen’ 6 1/5

‘fifteen’ 10 1/9

‘sixteen’ 10 5/5

‘seventeen’ 10 4/6

‘eighteen’ 10 3/7

‘ninteen’ 10 2/8

‘twenty’ 10 3/7

‘thirty’ 10 2/8

‘forty’ 13 2/8

‘fifty’ 9 1/8

‘sixty’ 6 3/3

‘seventy’ 12 4/8

‘eighty’ 12 6/6

‘ninty’ 10 4/6

‘hundred’ 20 2/18

‘thousand’ 25 3/22

‘million’ 10 2/8

‘billion’ 10 2/8

‘trillon’ 12 1/11

‘and’ 5 3/2

‘only’ 5 3/2

‘lakhs’ 10 3/7

‘crores’ 5 4/1

Table 2: Experimental Results (Execution time).

Slant correction technique

Average

Time taken

Only Local Slant Correction is applied 123 ms

Only Global Slant Correction is applied 72 ms

Programmatically identification of

appropriate technique (Global or Local)

& Correction

157 ms

4 CONCLUSIONS

In this paper, we presented an approach that works

very well for check images that have handwritten

words/text. However, this approach would work

equally well with any form of handwritten text. The

images, obtained after applying our method based on

this approach, were free of distortion. This is due to

the method’s remarkable ability to correctly choose

between local and global slant correction to be

applied to individual words.

REFERENCES

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AN APPROACH FOR SLANT CORRECTION USING PROJECTIVE TRANSFORMATION