Off-Line Signature Verifi'cation of Bank Cheque Having DifferentBackground Colors

Javed Ahmed MaharDepartment of Computer ScienceShah Abdul Latif UniversityKhairpur, Pakistan.

Mohammad Khalid Khan,PAF-Karachi Institute ofEconomic & Technology,Karachi, Pakistan.

Prof. Dr. Mumtaz Hussain Mahar,Department of Computer ScienceShah Abdul Latif University,Khairpur, Pakistan.

Abstract

Most of the banks issue the chequebooks to theircustomers having different background colors whereas the original specimen signature is always taken onthe white paper at the time of the opening of theaccount. When these signatures are verified digitallythe results are not accurate. In this paper, we haveproposed a mechanism that automates the offlinesignature verification for bank cheques even withdifferent background colors. Work present in thispaper is focused, to examine whether an inputsignature of colored bank cheque is a genuinesignature or a forged. This task is performed bycomparing the collected signature samples (whitebackground) with input signatures (coloredbackground). The Signature Verification of ColoredCheques (SVCC) system of verifying the signatureshaving different background colors in spite of whitepaper specimens through variation of color intensityis discussed andpresented.

1. Introduction

A signature appears on many types of documentssuch as bank cheques and credit slips, thus signaturehas a great importance in a person's life. Automaticbank cheque processing is an active topic in the fieldof document analysis and processing, the items ofinterest, namely date, courtesy amount and legalamounts extracted by some module based on edgeanalysis [14]. Signature validity confirmation of bankcheques is one of the important problems inAutomatic Bank Cheque Processing.

The common use of bank cheques in daily lifejustifies the development of cheque processingsystems. Bank transactions involving cheques are stillincreasing throughout the world in spite of the overallrapid emergence of electronic payments by creditcards [19]. However, fraud committed in cheques isalso growing at an equally alarming rate withconsequent losses. Automatic Bank ChequeProcessing systems are, hence, needed not only to

counter the growing cheque fraud threat but also toimprove productivity and allow for advance customerservices.

The performance in signature verification is greatlyimproved by constraining the signing, whichaddresses the problem of segmentation and makes thepeople sign more carefully [12]. But banks are notwilling to change the format of the cheque to imposesigning constraints such as guidelines or boxes tospecify the location where each signature should berecorded. Instead they are interested in reducing theworkload of the employees that manually examinethe signature of cheque. The employees also makemistakes during examining the signature of cheque.A system that is capable to examine the signatureautomatically would be very helpful, especially if it isfast and accurate. Even if misclassification occurs,the mistake could potentially be detected during theverification process; however it is more desirable thatthe system rejects a cheque in case of doubt so that itcan be directed for manual process [16][18].

The aim of this research is to develop a fast, robustand a reliable off-line signature verification systemwhich will not only identify the genuine signatureimage captured from bank cheque but it also rejectsall kinds of forgeries. In this paper we have proposeda mechanism that automates the offline signatureverification for bank cheque process even withdifferent background colors. Work presented in thispaper is focused, to examine whether an inputsignature of colored bank cheque is a genuinesignature or a forged. This task is performed bycomparing the collected signature samples with inputsignatures. For this three feature extraction methodsare compared that confirm the performance of thelearning and verification system. The SignatureVerification of Colored Cheques (SVCC) systemverified the signatures having the differentbackground colors in spite of white paper specimensthrough variation of color intensity is discussed andpresented.

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2. Related Work

Signature verification contains two main areas: Off-line signature verification, where signature samplesare scanned into image representation and On-linesignature verification, where signature samples arecollected from a digitizing tablet which is capable ofrecording pen movements during the writing.

On-line signature verification is one of the mostapplicable authentication methods in e-business inaffiliation with On-line banking transactions,electronic payments, access control and so on.Therefore, currently it is a very active research area.G.Rigoll, A.Kosmala [2] initiated the problem ofvariation in length and height that can be solved bybalancing signatures through several HMM-basedtechniques. Taik, Sung, Jin [4] presented skilledforgeries can be rejected through discriminativefeatures by using segment-to-segment compression.Wessels, Omlin [6] stated hybrid system that reducesthe clash in vast number of signatures and constructsintensity verification system.

In past, there have been many developments in thesignature verification problem. It is the Off-Linesignature verification area that is focused. Thecontrols of forgeries by Off-Line systems have animportant role in application area as medicine(prescriptions, medical reports), commerce (cheques,contracts), government and law. In these cases thesignatures have been signed in beginning. During thelast two decades, several off-line signatureverification system have been proposed, Dimauro andImpedovo [5] took more signatures of the writerthrough that inconsistency can be removed.Ramanujan and Winston [7] discussed that, valid &forge signature can be decided with multipleinstances of test signature. Sabourin et al. [8]presented a new formula of Visual perception forsignature representation that determines the shapedescriptor and pertaining features in signatureverification. Sargur et al. [9] initiated writerindependent model, determines the temporalinformation of writer, through that identification ofsignatures become easier.

Recently, many research efforts have been put intothe field of feature extraction, including featureconstruction, space dimensionality reduction, sparesrepresentation and feature selection. Edson, Flavioand Sabourin [1] suggested, skilled forgeries can bediscerned using pseudo dynamic feature extractiontechniques of handwriting motion. Edson, Flavio andSabourin [10] proposed robustness of one singlestatic feature, or the density of pixels in an off-line

signature verification pertaining to cross-validationprocess. Jalal and Chowdhury [11] demonstrated, byusing feature analyzer; noises can be removedthrough extracting invariable information in signatureverification than system can be impressive andefficient.

Recently, many advanced bank cheques processingsystems have been developed for informationextraction, courtesy & legal amount recognition andsignature. The basic step of automatic reading ofcheques is to extract the interested items such assignature, to develop an effective item extractionsystem that is difficult task, especially when thecheques contain complicated and colorfulbackground. Comparatively, there is very limitedpublished work on the processing of off-linesignature verification on colored cheques. Vamsi etal. [12] declared to verify the fields of the chequethrough automatic segmentation and recognitionmethod, through this transaction could be safe. KeLiu et al. [14] approached, to extract item of thecheques even if complex colorful background andpictures on cheques. Santos et al. [15] distinguished,the machine written and handwritten text that resultsof item extractions becomes effective and accurate.S.Djeziri et al. [16] method is proposed to subtractbetween a virgin model and a filled specimen of acheque that clearly extracts the items of cheques.Qizhi Xu et al. [17] advised a segmentation methodwhich processes the entire date zone of the chequethat increases the computational performance.Alessandro et al. [18] an approach is proposed tosegment the printed information and user entered datathat automatically extracts similar gray levels datafrom cheque. The proposed system is composed bythree main modules: data acquisition, database andimage processing.

If bank cheque signature is verified, the first step is toextract the signature from the cheque, it is noted thatsuch kind of research is already made before as:Vamsi et al. [12] proposes method to verify the fieldsof the cheques through automatic segmentation andrecognition. Ke Liu et al. [14] approached, to extractitem of the cheques even if complex colorfulbackground and pictures on cheques. The way ofsignature extraction that is captured in this research isthe selection of threshold because it is observed; thismethod clearly extracts the signature of chequesespecially where signature image characteristics canchange over a broad range of intensity distribution.

Recently, many research efforts have been put intothe field of feature extraction. After studying fewpapers, some feature extraction techniques are

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identified as like: Edson, Flavio and Sabourin [1]discussed pseudo dynamic grid feature extractiontechniques. Kai Huang and Hong Yan [13] put forth,the fractal transformation based signature verificationtechnique effectively verifies the global signatureshape. Therefore we identify three kinds of featuresfor off-line signature verification which are based onthe comparison methods between an input signatureand a reference signature; which are: (i) Grid FeatureComparison, (ii) Global Feature Comparison, (iii)Texture Feature Comparison.

Best possible ways are selected of doing twodifferent works and combined the both techniques tosolve the complete problem of signature verificationof bank cheque that comply the accurate results.

3. Problems in Signature Verification dueto Colored Background

Grayscale values in digital imaging are represented asnonnegative integer values, where zero representsblack and some positive value, typically 255,represents the maximum white. The grayscaleintensity is stored as an 8-bit integer giving 256possible different shades of gray from black to white.

Color is defined by a vector in the three-dimensionalcolor space. The intensity is given by the length ofthe vector, and the actual color by the two anglesdescribing the orientation of the vector in the colorspace. The amounts of red, green, and blue needed toform any color value.

A grayscale image is one in which colors are shadesof gray. The reason for differentiating such imagesfrom other sort of color image is that less informationneeds to be provided for each pixel. In fact a graycolor is one in which all red, green and bluecomponents have equal intensity in RGB space, andso it is only necessary to specify a single intensityvalue for each pixel, as opposed to the threeintensities needed to specify each pixel in a colorimage.

When the signature having white background iscompared with signature having colored backgroundthen the result is not accurate to automate thesignature verification process, because white paperhas low intensity, while the color paper has highintensity rate. The gray scale signature image isdigitally stored into a form of two-dimensional vectormatrix whereas the colored signature image is storedinto a form of three dimensional vector matrixes. Weneed to perform different kinds of arithmetic

operations on both types of signature images for thecomparison and verification process. Due to adifferent nature of vector matrixes it is impossible.

4. Overview of Proposed SVCC System

Figure 1 shows the flow diagram of the SVCCsystem proposed in this paper. The input chequesignature is scanned into image representation, forthe verification process it is converted into binaryimage. The example of colored input signature isshown in Figure 2.

Figure 1. Flow diagram of SVCC System proposed inthis paper

One of the important steps of bank cheque process isto extract signature from the cheque by removal ofthe color background. The preprocessing phase isdivided into four different parts: noise reduction, sizenormalization, signature skeletonization and thinningof signature image. We identify three kinds of featureextraction methods for off-line bank cheque signatureverification, which are based on the comparisonbetween an input cheque signature and a referencesignature; which are: (i) Grid Feature, (ii) Global

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Feature, (iii) Texture Feature. All the extractedfeatures are used by a k-nearest neighbor classifierthat compares the extracted feature to a number ofprototypes that are coming from cheque writer withknown identity. The final step of proposed SVCCsystem is to recognize or reject the signature ofcheque. For each signature we queried the SVCCsystem 100 times, one time for each cheque signer.The training database contained 600 signature imagesand that made 100 X 600 = 60,000 testing cases. Thecheque signature is recognized if systems responsewas positive and asked that signature belonged to thecorrect signer. The cheque signature is rejected ifsystems response was negative and asked thatsignature belonged to false signer.

3. Data Collection

The signatures were collected using either black orblue ink (no pen brands were taken intoconsideration), on a white A4 sheet of paper, withfour signatures per page. A scanner subsequentlydigitized the four signatures, contained on each page,with a 300-dpi resolution in 256 grey levels.Afterwards the images were cut and pasted in arectangular areas of 3xl0 cm and were each savedseparately in files.

A group of 20 persons were used to collect 30specimens of each, making up a total of 600signatures for training database. Ten persons havecollected the genuine signatures and other ten personshave produced the forged samples. One hundredcheques are collected from 20 persons for testing,having 5 cheques from each one, ten persons haveproduced forged signatures on cheques. The saidcheques are sampled cheques.

5. Signature Extraction Phase

The problem of processing bank cheque signature canbe divided in two main branches: the extraction andthe verification of the signature. Currently, twostrategies are used for extracting the signature:thresholding techniques and image subtraction.Different thresholding techniques have beensuggested to isolate the signature from the bankcheque [20] [21] [22]. It is seen in practice thesetechniques have shown good results and useful inthose applications where the cheques have onlysimple background colors. However, in realapplications, bank cheques may contain a variety ofcomplex colorful backgrounds. If these techniqueswere applied to bank cheques in which thebackground pattern has complex colors, it would be

very difficult to find a threshold value to segment thebackground from the signature. On the other hand,the techniques based on image subtraction haveshown more robustness to segment the signature frombank cheque that has colorful pictures on thebackground pattern [23].

The way of signature extraction that is captured inthis research is the selection of threshold because it isobserved; this method clearly extracts the signatureof cheques especially where signature imagecharacteristics can change over a broad range ofintensity distribution. The example of extractedsignature from colored cheque is shown in Figure 3.

r igure -. txampie OI extracteu signature Iromcolored cheque

The verification is based on the assumption that thesimilarities for an individual writer tend to cluster,while those of a population of writers are morewidely scattered. We determine the threshold valuefor verification based on only the statistical propertyof genuine cluster.

Let { Sj (j= 1,2..........,n)}be the obtained similaritiesbetween the registered signature and its n genuinesignatures for training. Moreover, let pt and a be themean value and the standard deviation of { Sj }respectively. The threshold value T of the genuinesignatures for each registered one is defined as thefollowing equation.

T= t - aaWhere a is called threshold coefficient.

For unknown examined signature, if the similarityS(u) of an examined signature is larger than T, thenthe signature is classified as a genuine otherwiseforged. Thresholding is a quick way to convertgrayscale images into black-and-white images.

6. Pre - Processing Phase

The preprocessing stage is divided into four differentparts: size normalization, noise reduction, imagethinning and skeletonization.

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6.1 Size NormalizationA signer may use an arbitrary baseline when writingthe signature. We normalize the signature positionalinformation by calculating an angle 0 of correctiverotation about the centroid of the (x,y) such thatrotating the signature by 0 brings it back to a uniformbaseline. We calculate 0 by maximizing the deviationof the data, one direction, e.g. the x direction. Theimage size is adjusted so that the width reaches adefault value while the height-to-width ratio remainsunchanged. The size normalization in offlinesignature verification is important because itestablishes a common ground for image comparison.A low spatial resolution makes all signatures looklike the same while a very high spatial resolution mayhighlighten the variability [6].

6.2 Noise reductionDirt on camera or scanner lens, imperfections in thescanner lighting, etc introduces noises in the scannedsignature images. A filtering function is used toremoval the noises in the image. Filtering functionworks like a majority function that replaces eachpixel by its majority function.A noise reduction filter is applied to the binaryscanned image. The goal is to eliminate single whitepixels on black background and single black pixelson white back ground. In order to accomplish this, weapply a 3 x 3 mask to the image with a simpledecision rule: if the number of the 8-neighbors of apixel that have the same color with the central pixelis less than two, we reverse the color of the centralpixel [11].

6.3 ThinningThinning is a morphological operation that is used toremove selected foreground pixels from binaryimage, somewhat like opening. It can be used forseveral applications, but is particularly useful forskeletonization. In this mode it is commonly used totidy up the output of edge detectors by reducing alllines to single pixel thickness. Thinning is normallyonly applied to binary image, and produces anotherbinary image as output.

6.4 SkeletonizationA simplified version of the skeletonization techniquedescribed in [24] is used. The simplified algorithmused here consists of the following three steps:Step 1: mark all the points of the signature that arecandidates for removing (black pixels that have atleast one white 8-neighbor and at least two black 8-neighbors pixels).Step 2: Examine one by one all of them, followingthe contour lines of the signature image, and remove

these as their removal will not cause a break in theresulting pattern.Step 3: If at least one point was deleted go again toStep 1 and repeat the process once more.Skeletonization makes the extracted featuresinvariant to image characteristics like the qualities ofthe pen, the paper, the signer used, the digitizingmethod and quality.

7. Feature Extraction Phase

The choice of a powerful set of features is crucial insignature verification systems. The features usedmust be suitable for the application and for theapplied classifier. In this system, three groups offeatures are used categorized as grid features, globalfeatures and texture features. For grid informationfeatures, the image is segmented in 120 rectangularregions. Only the area (the number of signaturepoints) in each region is utilized in order to form thegrid information feature group. For the globalfeatures provide information about specific casesconcerning the structure of the signature. For thetexture feature group to be formed, a coarsersegmentation scheme is adopted. The signature imageis segmented in only six rectangular areas, while, foreach area, information about the transition of blackand white pixels in the four different directions isused.

7.1 Grid FeatureGrid segmentation procedures have been usedextensively in the off-line signature verificationapproach. The skeletonized image is divided into 120rectangular segments (15x8), and for each segment,the area (the sum of foreground pixels) is calculated.The representation of simple grid is shown in Figure4. The results are normalized so that the lowest value(for the rectangle with the smallest number of blackpixels) would be zero and the highest value (for therectangle with the highest number of black pixels)would be one. The resulting 96 values form the gridfeature vector. A set of different grid resolutions wasused in the experiments, but a grid with square cellsof medium resolution (50x50 pixels) showed betterresults [25].

Figure 4. Simple grid of 15x8 segments

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i | -I 0 0 1 1 0 0 1 1 0

i m m i m

*0 m 0 0

i I i i i i 1 1 i iX* i i * * i i * * 0

* *m i* m

It is very encouraging to recognize the diagonally sothat more points may be diagnosed for generating thevector matrix to get results more accurate than thesimple grid [28]. The representation of diagonal gridis shown in Figure 5.

Figure 5. Representation of diagonal grid

7.2 Global FeatureSome common global features are discussed bellowswhich are implemented in our experiments. However,more global features are recommended for the betterresults [25].Signature height. The height of the signature image,after width normalization, can be considered as a wayof representing the height-to-width ratio.Image area. The number of black pixels in theimage. In skeletonized signature images, it representsa measure of the density of the signature traces.Pure width. The width of the image with horizontalblank spaces removed.Pure height. The height of the signature image aftervertical blank spaces removed.Baseline shift. The deference, between the verticalcenters of gravity of the left and the right part of theimage. It was taken as a measure for the orientationof the signature.Number of edge points. An edge point is defined asa signature point that has only one 8-neighbor.

7.3 Texture FeaturesTo extract the texture feature group, the co-occurrence matrices of the signature image are used[25]. In a gray-level image, the co-occurrence matrixPd [ij] is defined by first specifying a displacementvector d=(dx,dy) and counting all pairs of pixelsseparated by d and having gray level values i and j. Inour case, the signature image is binary and thereforethe co-occurrence matrix is a 2x2 matrix describingthe transition of black and white pixels. Therefore,the co-occurrence matrix Pd [ij] is defined as:

Pd[i,j] P 00

L P 10

occurs, separated by d. plO is the same as pO1 . pi 1 isthe number of times that two black pixels occur,separated by d. The image is divided into sixrectangular segments (3x2). For each region theP(1,0) , P(1,1) , P(O,1) and P (-1,1) matrices arecalculated and the pO1 and pll elements of thesematrices are used as texture features of the signature.The above procedure sums up to 48 features (sixsegments x four matrices x two elements).

8. Results Classification Phase

There are several ways to work out the distancebetween two points in multidimensional space.Which one to use is often subject to debate? Themost commonly used is the Euclidian distancemeasure [26]. It can be considered the shortestdistance between two points.

de V (xi - yi)

All the extracted features are used by a k-nearestneighbor classifier that compares the extractedfeature vector to a number of prototype vectorscoming from writers with known identity. Thesquared Euclidean distance between a test vector andreference vector was measured [27]. For allexperiments, we use a Euclidean-distance based K-Nearest Neighbor (K-NN) classifier. This classifierdetermines the all nearest neighbors to each inputfeature vector and opts for the class that is most oftenrepresented. In case of a tie, the class with thesmallest sum of distances is chosen. The number ofnearest neighbors has been empirically determined.The sample output of distance measurement from'VZXCC QciftwqrP, kihown in Firnirp_ 6

P 01P 11

Where pOO is the number of times that two whitepixels occur, separated by d. pO1 is the number oftimes that a combination of a white and a black pixel

Figure 6. Sample output of distance measurement from SVCC software

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9. Experiment

For our experiments, 20 persons are selected for 30specimen signatures which make the total of 600signatures. Among them 10 persons have given theirgenuine signatures and other 10 have producedforged samples of their signatures.

One hundred cheques are collected from 20 personsfor testing, having 5 cheques from each one, tenpersons have produced forged signatures on cheques.

(false acceptance of forge signature) error ratesevaluated for the 20 persons. Table 1 shows the errorrate obtained using each feature separately.

Features usedGrid FeatureGlobal FeatureTexture Feature

Table 1: Type I andfeatures

Type I Type II8.07% 5.910011.26% 9.53%3.73% 2.83%

Type II error rates of three

To verify and test the cheque signature through oursoftware, it was executed 100 times. This trainingdatabase contains 600 signature images which willdevelop 100x600=60,000 testing cases.

For verification process, one signature is taken fromtesting samples randomly, to compare it with trainingsamples' signatures, if SVCC system found itapproximately 7000 genuine & 3000 forge then itcould be considered genuine. If it is found 70°O forge& 30°O genuine then it could be considered forge.The sample output of signature comparison fromSVCC software is shown in Figure. 7

Figure 7. Sample output of signature comparison from SVCC software.

The feature set is split into three different groups, i.e.,Grid feature, Global feature and Texture features.Due to the different nature and non-correlation of theabove feature sets, the combinations of their featurevectors cover the required feature information. Allsignature images have been defined and evaluatedusing a KNN classifier. The performance of theverification system is reported in terms of type I(false rejection of genuine signatures) and Type II

10. Conclusion

As an automated signature, verification process couldbe significantly beneficial & efficient for the bankingsystem particularly for cheque signature forgeries,which can give a large monetary loss to the bankeach year. In this paper a powerful mechanism hasbeen proposed in which a complete automatic offlinesignature verification system has been design. Thissystem is capable of verifying the image ofhandwritten signature that is captured from the bankcheques that are often in colored paper. The SVCCsystem has been discussed and presented for theabove purpose. The simple features were comparedand implemented to confirm the performance offeature extraction methods; K-NN classifier is alsoused. The test accuracies achieved through gridfeature method were 92.7%, the test accuracies gotthrough global feature method are 89.8% and the testaccuracies achieved through texture feature methodare 96.9%. The performance of the verificationsystem is reported in terms of type I & type II.

Work presented in this paper contributes more thanthe previous work of the same area because most ofthe researchers have used plain paper whereas; we

have used paper with colored background forsignature verification. Previous research work usedone feature extraction method whereas, we havecompared three feature extraction methods forsignature verification. We have implementeddiagonal grid that has improved the results whereassimple grid approach has been used by otherresearchers. Most of the researchers have calculatedco-occurrence matrix in horizontal and verticaldirections, whereas we have calculated co-occurrence

matrix in horizontal, vertical and diagonal directions.

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