Novel Thresholding Method for Document AnalysisAdnan Khashman, Senior Member, IEEE and Boran Sekeroglu, Member, IEEE

Near East University, Lefkosa, Mersin 10, Turkeyamk@neu.edu.tr and bsekeroglugneu.edu.tr

Abstract-Thresholding is a simple and efficient method for relies on maximizing the total entropy of both the object andimage enhancement and segmentation of grayscale documents, background regions to find the appropriate threshold [9].where the relationship of pixel values in the documents can Kittler and Illingworth proposed a method which starts byprovide an effective single point for the separation of the choosing an arbitrary initial threshold T. Then parametersbackground and foreground layers. Document analysis anda a p.effective separation of text may provide useful data for electronic Hi(T), vi(T) and a priori probability Pi(T), where i=1,2 arestorage systems and libraries. This paper presents a novel method computed and then an updated threshold is calculated [10].namely, Mass-Difference Thresholding (MDTh) for enhancement Parker proposed a method that first detects the edges, and thenand text separation from documents. MDTh will be implemented the interior objects between edges are filled. For each pixelusing 30 documents that have various levels of noise and color. A (x,y) in the input image Z, it calculates the negative of thecomparison will be drawn between MDTh and five other well gradient in the direction of the brightest neighbor, then for eachknown and efficient thresholding methods. Experimental resultssuggest that the developed method performs well, thus providing regIon, sample mean and standard deviatons are calculateda fast and efficient method for text separation. [11]. Solihin and Leedham presented the Quadratic Integral Ratio

(QIR) method which uses Quadratic Integral Estimators toseparate image into three classes, foreground, background and

I. INTRODUCTION fuzzy. Class A separates foreground and fuzzy classes andteffective class C separates fuzzy and the background classes. QIR deals

Thepurptionandseparatof nofthedocumentalstci s is th dero with quadratic approximation of the real intensity histogram ofpreparation and separation of the documents in order toprovide efficient and clear data for recognition and analysis of image to separate classes [12].

tnisi Leedham et al. [13] showed that Otsu's algorithm mostlytherdocuments.gThus,the firstse te of dcent analysis overthresholded and it can not produce good results when theperforming the efficient separation of the texts from the background intensity is high and there is low contrast in thebackground. It looks quite easy to separate texts from clean image. It was also declared that Kapur's entropy performedand noiseless background where simple and basic thresholding well if the images had good contrast while QIR produces bettermethods can solve this problem easily and effectively, results than other techniques. These methods, proved theirHowever, some documents - especially historical documents, successes in simple text images or clean document images.have different conditions resulting from layers such as noises, But, under the extreme conditions, most of them are eithercolors, meaningless shapes etc. [1]. Therefore, analyzing these over-thresholded or under-thresholded.degraded documents generally requires more complex multi- This paper presents a new global single-stage thresholdingalgorithms or thresholding techniques to separate these layers. technique, namely Mass-Difference Thresholding (MDTh)

Thresholding is one of the simplest methods that can be used which is based on the mass average and the global maxima ofto separate foreground texts or objects from background. A image to separate foregrounds from backgrounds withfixed-points threshold may be successfully used in very high minimum loss of information and minimum deviation from thecontrast images, however, in low contrast images; it causes.' . ' . . ' . ~~optimum threshold point. The MDTh method will beeither loss of information or fuzzy information. Thresholding implemented using 30 new and historical documentsmethods, such as the background-symmetry algorithm [2] are . .based on brightness histograms of images, and thus are called contaiin23 words.' ~~~~~Acomparison will be drawn between MDTh and five knownhistogram-derived thresholds [3]. efficient methods for text separation, namely Otsu, Kapur,Recently, several algorithms had been proposed for Parker, Kittler-Illingworth and QIR Methods. The comparisonseparating foreground texts from backgrounds [4-7]. is based on the rate of recognized words in a document afterNonetheless, the most commonly used and efficient methods thresholding is applied using all six methods on 15 documentsare considered to include Otsu's thresholding method [8], containing 1205 words.Kapur et al.'s Entropy technique [9], Kittler-Illingworthminimum error technique [I0], Parker Method [1] and Solihin The structure of the paper iS as follows: Section 2 describeset al. Quadratic Integral Ratio (QIR) method [12].

Otsu proposd an ef experimental results of applying MDTh and a comparison ofbased on image histogram. It uses discriminant analysis to the results when using other known thresholding methods.divide foreground and background by maximizing the Finally, section 4 concludes the work and provides suggestionsdiscriminant measure [8]. Kapur et al. suggested a method that for further work.

1-4244-0726-5/06/$20.OO '2006 IEEE 616

II. MAsS-DIFFERENCE THRESHOLDING Figure 1 demonstrates the MDTh operation on the word

MDTh is a global single-stage thresholding technique that "sample" image histogram. Here the Mass of the image (meanfinds the optimum threshold value using the global maxima of intensities) M 226, the Global Maxima (highest pixel(highest pixel value) and the mass average (mean of the value) Gmax = 246 on the grey-level scale of (0 - 255) and theintensities) of an image. The relationship between pixel values optimum threshold point T 206.of grayscale images provides a threshold point for theforeground and the background of the image. The highest pixel III. EXPERIMENTAL RESULTS AND COMPARISONvalue represents the global maxima of the image. The averaged The proposed global single-stage thresholding methodpixel values of a whole image represent the mass average (MDTh) has been implemented using 30 images of various(mean of intensities) of the image. documents containing 2345 words in total. The documentsMDTh is different from the background-symmetry have a variety of noise and contrasts. A total deviation valuealgorithm [2] which assumes a distinct and dominant peak for

the ackrountht i symetrc abut ts aximm. his representing the selected optimum threshold point of an imagemhebaxim roumdpeak isfunbsearchgfbort ith maximum. vale was calculated for each of the 30 document images. Thismaximumnpthehistogram,ywhereasiin the maximum valu e yielded 30 optimum threshold points that are different andin the.histogram ,whereas in MDTh themaximumvalunique to their respective image. Figure 2 and Figure 3 showhighest pixel value within the image. examples of applying MDTh to separate text in documents.MDTh uses the deviations between mass average and global The efficiency of text separation methods can be determined

maxima. The Mass of an image can be defined as: by the recognition rate of words in the thresholded or

(d'im dim, segmented document images. Visual inspection of theM=l ' l[x y] l/(dimYxdim )() documents deterrnines the number of recognized and readable

i(, x words in a thresholded document. This was implemented by 15independents persons. The total number of the words in the

where M represents the mass of image, dimx and dimy denote original document and the number of recognized and readable'. ~~~~words after thresholding were used to determine thethe x and y dimensions of image respectively, and I represents . .

the original grayscale image. The Global Maxima or maximum recognition rate as in equation (7).brightness of an image is defined in equation (2) as amaximum function of original grayscale image.

Gmax = Fmax (2)

After the calculation of mass and global maxima, the LocalDeviation (D) of the Mass (M) from the Global Maxima (Gmax)is defined as:

D = Gmax -M (3)

The Total Deviation (T) which represents the Optimum 18W OThreshold Value is defined as the difference of local deviationand the mass of the image, which is defined in equation (4).Absolute value of the difference of local deviation and the 14Wmass of the image is considered to avoid the negative threshold 12Wvalues in the cases of the smaller mass of the images than thelocal deviation.

T = IM-DI (4) 8X t6W0

Equation (4) can also be written as: 4W2X T hI 1ll l || G,;~~~T

T IM (Gmax M) (5) .dII

An MDTh image (MI) is obtained using equation (6): 0 50 100 150 200 250

xo, if I[x,y].T Fig. 1. "Sample" Image and Histogram Operations: Mass of Image (M-MI ~ ~ {25 else].= (6) 226), Global Maxima (Gma 246), Optimum Threshold (T= 206).

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TABLE ICOMPARISON OF RECOGNITION RATES

Method Clean Degraded Highly Total RecognitionDegraded Rate

Otsu 300/325 335/450 290/430 925/1205 77 %Kapur 289/325 325/450 175/430 789/1205 65 %Parker 252/325 250/450 235/430 737/1205 61 %

Kittler and Illingworth 199/325 60/450 35/430 294/1205 24 %QIR 215/325 90/450 80/430 385/1205 32 %MDTh 308/325 350/450 300/430 958/1205 800

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REFERENCES

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