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This paper proposes a novel enhancement algorithm for degraded binary document images

captured using a low end cell phone camera. The algorithm starts with colour to grey scale

conversion followed by contrast stretching. Then single scale retinex enhancement is

performed to increase the contrast, reduce global variance and improve the background

uniformity of the image. Otsu's binarisation method is then used to dichotomize the image

and finally, morphological dilation is performed to preserve stroke connectivity of the

document characters by bridging any gaps resulting from the thresholding process.

Computer simulation experiments have been used to demonstrate the effectiveness of the

proposed algorithm. The results show a significant improvement over the Otsu's method in

terms shadow removal, document legibility and optical character recognition. The results

also compare well with the state of the art Sauvola's method results.

Published in:

Image Processing (IPR 2012), IET Conference on 

Date of Conference:

3-4 July 2012Page(s):

1 - 6

E-ISBN :978-1-84919-632-1

INSPEC Accession Number:12863052

Conference Location :London

Digital Object Identifier :10.1049/cp.2012.0420 

Publisher:IET

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The paper presents a hybrid thresholding approach for binarization and enhancement of

degraded documents. Historical documents contain information of great cultural and

scientific value. But such documents are frequently degraded over time. Digitized degraded

documents require specialized processing to remove different kinds of noise and to improve

readability. The approach for enhancing degraded documents uses a combination of two

thresholding . First, iterative global thresholding is applied to the smoothed degraded image

until the stopping criteria is reached. Then a threshold selection method from gray level

histogram is used to binarize the image. The next step is detecting areas where noise still

remains and applying iterative thresholding locally. A method to improve the quality of

textual information in the document is also done as a post processing stage, thus making

the approach efficient and more suited for OCR applications.

Published in:

Signal Processing, 2008. ICSP 2008. 9th International Conference on 

Date of Conference:

26-29 Oct. 2008Page(s):

891 - 894

E-ISBN :978-1-4244-2179-4

Print ISBN:978-1-4244-2178-7

INSPEC Accession Number:10411014

Conference Location :Beijing

Digital Object Identifier :10.1109/ICOSP.2008.4697271 

Publisher:IEEE

author

Babu, N. ; Dept. of Comput. Sci. & Eng., PES Inst. of Technol., Bangalore ; Preethi, N.G. ; Shylaja, S.S. 

1. Otsu, N. "A threshold selection method from gray-level histograms". IEEE Trans. SystemsMan, Cybernet. pp. 62-66, 9 (1), 1979.

 Abstract | Full Text: PDF (3077KB)

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2. Sauvola, J., Pietikainen, M., "Adaptive Document Image Binarization", Pattern Recognition,pp. 225-236, 33 (2000).[CrossRef] 

3. Gatos B., Pratikakis I. and Perantonis S. J. "An adaptive binarisation technique for lowquality historical documents". IAPR Workshop on Document Analysis systems (DAS'2004),

Lecture Notes in Computer Science (3163), Florence, Italy, pp. 102-113.

4. Leedham, G., S. Varma, A. Patankar, V. Govindaraju "Separating Text and Background inDegraded Document Images" Proceedings Eighth International Workshop on Frontiers ofHandwriting Recognition, pp. 244-249, September, 2002.

 Abstract | Full Text: PDF (530KB)

5. E. Kavallieratou, E. Stamatatos, "Improving the Quality of Degraded Document Images",Second International Conference on Document Image Analysis for Libraries (DIAL '06), pp.340-349, 2006

 Abstract | Full Text: PDF (2589KB) | Full Text: HTML 

6. E. Kavallieratou, S. Stathis, "Adaptive Binarization of Historical Document Images", 18thInternational Conference on Pattern Recognition, ICPR 2006, Volume 3, 2006, pp. 742-745

 Abstract | Full Text: PDF (646KB)

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Thresholding: A Pixel-Level Image

Processing MethodologyPreprocessing Technique for an OCR

System for the Brahmi Script

H. K. ANASUYA DEVI

Fellow, National Institute of Advanced Studies IISc Campus,

Bangalore-12

Abstract

In this paper we study the methodology employed for preprocessing the archaeological

images. We present the various algorithms used in the low-level processing stage of image

analysis for Optical Character Recognition System for Brahmi Script. The imagepreprocessing technique covered in this paper is thresholding. We also try to analyze the

results obtained by the pixel-level processing algorithms.

1. Introduction

Optical scanning of the rock inscription yields an image (file of pixels) that forms the raw input to the Optical

Character Recognition System. The output is the set of recognized characters.

Preprocessing is the first phase of document analysis. The purpose of preprocessing is to improve the quality

of the image being processed. It makes the subsequent phases of image processing like recognition of

characters easier. Thresholding is one of the preprocessing methods discussed in this paper.

In thresholding, the color-image or gray-scale image is reduced to a binary image.

2. Thresholding

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Fig. 1.: The process of thresholding along with its inputs and outputs.

2.1 Definition

Thresholding is a process of converting a grayscale input image to a bi-level image by using an optimal

threshold.

2.2 Purpose

The purpose of thresholding is to extract those pixels from some image which represent an object  (either

text or other line image data such as graphs, maps). Though the information is binary the pixels represent a

range of intensities. Thus the objective of binarization is to mark pixels that belong to true foreground

regions with a single intensity and background regions with different intensities.

2.3 Thresholding algorithms

For a thresholding algorithm to be really effective, it should preserve logical and semantic content. There are

two types of thresholding algorithms

1.  Global thresholding algorithms

2.  Local or adaptive thresholding algorithms

In global thresholding, a single threshold for all the image pixels is used. When the pixel values of the

components and that of background are fairly consistent in their respective values over the entire image,

global thresholding could be used.

In adaptive thresholding, different threshold values for different local areas are used.

2.3.1 Quadratic Integral Ratio (QIR) algorithm

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Fig. 2: Three sub images of QIR method

Method: QIR is a global two stage thresholding technique that uses intensity histogram to find the

threshold.

The first stage of the algorithm divides an image into three subimages: foreground, background, and a fuzzy

subimage where it is hard to determine whether a pixel actually belongs to the foreground or the

Background. Two important parameters that separate the subimages are A, which separates the foreground

and the fuzzy subimage, and C , which separate the fuzzy and the background subimage. If a pixel's

intensity is less than or equal to A, the pixel belongs to the foreground. If a pixel's intensity is greater than

or equal to C , the pixel belongs to the background. If a pixel has an intensity value between A and C , it

belongs to the fuzzy sub image and more information is needed from the image to decide whether it actually

belongs to the foreground or the background.

The strategy is to eliminate all pixels with intensity level in [0,A] and [C,255]. Thus produce a range of

promising threshold values delimited by the parameter A and C (T [A,C]).

Performance (with respect to our experiments): QIR performed well as it generally was able to

separate definite foreground (dark) pixels and definite (background pixels). The uncertain or fuzzy pixels

were clearly defined and required further processing to determine appropriate assignment to background or

foreground.

2.3.2 OTSU algorithm

Method: This type of thresholding is global thresholding. It stores the intensities of the pixels in an array.

The threshold is calculated by using total mean and variance. Based on this threshold value each pixel is set

to either 0 or 1. i.e. background or foreground. Thus here the change of image takes place only once.

The following formulas are used to calculate the total mean and variance.

The pixels are divided into 2 classes, C1 with gray levels [1, ...,t] and C2 with gray levels [t+1, ... ,L].

The probability distribution for the two classes is:

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Also, the means for the two classes are

Using Discriminant Analysis, Otsu defined the between-class variance of the thresholded image as

For bi-level thresholding, Otsu verified that the optimal threshold t* is chosen so that the between-class

variance B is maximized; that is,

Performance (with respect to our experiments): Otsu works well with some images and performs badly

with some. The majority of the results from Otsu have too much of noise in the form of the background

being detected as foreground. Otsu can be used for thresholding if the noise removal and character

recognition implementations are really good. The main advantage is the simplicity of calculation of the

threshold. Since it is a global algorithm it is well suited only for the images with equal intensities. This might

not give a good result for the images with lots of variation in the intensities of pixels.

3. Results

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Fig. 3: An input image before thresholding (Pedestal)

Fig. 4: The output image as a result of applying QIR thresholding algorithm to Fig.3

Fig. 5: An input image before thresholdingFig. 6: The output image of applying OTSU algorithm

to Fig. 5

4. Conclusions and Future Enhancement

The preprocessing algorithms discussed so far give fairly average results. A cascaded approach wherein

various thinning and thresholding algorithms are successively applied on the input image can yield better

results. Hybrid preprocessing algorithms can be tried out wherein new methods can be designed to perform

effective thresholding. Preprocessing techniques like Filtering (to remove distortions and noise) could be

incorporated.

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Acknowledgements

The author wishes to thank Mr. Bipin Suresh, Ms. Adithi Sampath, Ms. Anitha J., Ms. Dimple Kolliapure, Mr.

Prasanna Venkatesh and Mr. Santosh Kabbur for their contribution during the execution of the program.

References

The Multi-stage Approach to Grey-Scale Image Thresholding for Specific Applications, Van Solihin and C. G.

Leedham

Document Image Analysis by Rangachar Kasturi, Louis Lam, Seong - Whan Lee & Ching Y. Suen.