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Study and Simulation of Different Techniques of

Adaptive Restoration of Degraded Document Images

Ripan Deuri Raghavendra Pal

ELB10033 ELB10034

Keywords: Binari zation, F il teri ng, Thresholding, Adaptive

Abstract

In the last few decades, computer originated electronic documents becomes the exclusive

medium of transmitting information. To compensate with it, old text document images must be

stored in a computer readable format. Document Image Processing deals with the restoration of

document images. Document images are processed in two distinct phases to extract their

contents: Binarization of the document images which refers to the conversion of a gray scale

image into a binary image thereby distinguishing the text areas from the background areas and

the another one is character segmentation and recognition which deals with the recognition of the

characters of the binary document image. Binarization plays an essential role in the whole

process as the performance of the character recognition critically depends on the accuracy of the

binary image. However, binarization is not an easy task because of the degradations that appear

frequently in the document image due to various reasons. Hence some special techniques arerequired to process such text document images. In this minor project, we have studied and

simulated four different binarization techniques: Niblack [5], Sauvola [2], Gatos et al. [1] and

Halabi et al. [4].Niblacks method is good for text regions only, Sauvolas method take cares of

both text and background regions, Gatoes et als method performs well in presence of

degradation in background surface and Halabi et al.s method also performs well by applying

Gaussian function in pre-processing step. The simulation was done in MATLAB. We analyze the

binarization results for each method in OCR software.

Introduction

Binarization of document images are broadly classified into two categories: global and adaptive.

The global methods use only one threshold to separate the text region from the background

region, which gives good result in case of uniform background. Adaptive method uses different

threshold value depending upon the local pixel information and it gives better result in compared

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to global methods when degradation is present in the document image. In this study, we consider

adaptive binarization of text document images only.

Different Binarization methods

[A] Niblacks methodThreshold is calculated based on the local mean and standard deviation of the pixel information

within the sliding window by the following formula:

[B] Sauvolas method

Sauvolas method is the modified version of Niblacks approach. Threshold is calculated

considering the dynamics of standard deviation by the following formula:

[3] Gatos et al.s method

It uses five distinct steps: preprocessing, rough estimation of foreground region, approximate

background calculation, final thresholding and post-processing. Usually adaptive wiener filter is

used for preprocessing of the main grayscale document images. A rough estimation of the text

region is calculated using sauvolas method which gives the superset of correct text regions. An

approximate background is calculated from the filtered grayscale image using interpolation of

the pixel corresponding to the text region of the estimated foreground. Final thresholding is

performed by calculating the distance of foreground text pixel from it background adaptively.

Finally, a post-processing is performed using shrink and swell filtering.

Fig1. : Block diagram of poor quality document image binarization

Weiner

Filter

Sauvola's

adaptive

thresholding

InterpolationExamine pixel contrast

B(x,y)-I(x,y)>d(B(x,y))

Post-

Processing

Is(x,y)Gray-scale

source image

I(x,y)Gray-scale

image after pre-

processing

S(x,y)Intermediate B/W

image

B(x,y)Gray-scale

Background

surface image

T(x,y)Final image

T(x,y)Final image

after post

processing

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(a) (b)

(c) (d)

Fig. 2: (a) Image before binarization (b) OCR output (c) After processing (d) OCR output

[4] Halabi et al.s method

It uses the same steps as the previous method, but a Gaussian filter is used in preprocessing step

to reduce the noise of the grayscale image.

Fig. 3: Block diagram of Halabi et al.s method

Experimental Results and Conclusion

Niblacks method performs binarization well in the regions near to the text areas. But large

amount of noise is present in the non-text regions. This problem of Niblack is overcome by

sauvolas method by considering the dynamics of standard deviation of the pixels for the

grayscale images. However, it gives poor performance when the degree of degradation becomes

more severe. The method proposed by Gatos et al. can deal with the degradations in the

Is(x,y)

Gray-scale

source image

I(x,y)

Gray-scale

image after pre-

processing

S(x,y)

Intermediate B/W

image

B(x,y)

Gray-scale

Background

surface image

T(x,y)

Final image

T(x,y)

Final image

after post

processing

Gaussian

Filter

Sauvola'sadaptive

thresholding

Interpolation Examine pixel contrast

B(x,y)-I(x,y)>d(B(x,y))

Post-

Processing

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background surface by considering the distance of the text and background surface as threshold.

Halabi et al.s method also perform well, but blurring of edges occur due to the usage of

Gaussian filter.

References

1. Gatos B., I. Pratikakis, S. J. Perantonis, Adaptive Degraded Image Binarization,

Journal of Pattern Recocnition, 39 (2006)

2. Sauvola J., M. Pietikainen, Adaptive Document Image Binarization, Pattern

Recognition 33 (2000)

3. Basilios Gatos, Ioannis Pratikakis and Stavros J. Perantonis An Adaptive Binarization

Technique for Low Quality Historical Documents (2004)

4. Yahia S. Halabi, Zaid SA, Faris Hamdan, Khaled Haj Yousef Modeling Adaptive

Degraded Document Image Binarization and Optical Character System (2009)

5. W. Niblack, An Introduction to Digital Image Processing, Prentice-Hall, Englewood

Cliffs, NJ, 1986

6. ABBYY (www.finereader.com).

http://www.finereader.com/http://www.finereader.com/http://www.finereader.com/http://www.finereader.com/