A Set of Features Extraction Methods for the Recognition of the Isolated Handwritten Digits

Transcription

1 Internatonal Journal of Computer and Communcaton Engneerng, Vol. 3, No. 5, September 214 A Set of Features Extracton Methods for the Recognton of the Isolated Handwrtten Dgts S. Ouchtat, M. Redjm, and M. Bedda Abstract In ths paper we present an off lne system for the recognton of the solated handwrtten dgts. The study s based manly on the evaluaton of neural network performances, traned wth the gradent back propagaton algorthm and fed by several feature vectors. The used parameters to form the nput vector of the neural network are extracted on the bnary mages of the dgts by several methods: the dstrbuton sequence, sondes applcaton, the Barr features, The central moments of mage codng accordng to the drectons of Freeman, and the centred moments of the dfferent projectons and profles. Index Terms Optcal characters recognton, neural networks, barr features, mage processng, pattern recognton, features extracton. I. INTRODUCTION Wrtng, whch has been the most natural mode of collectng, storng, and transmttng nformaton through the centures, now serves not only for communcaton among humans but also serves for communcaton of humans and machnes. The handwrtten wrtng recognton has been the subject of ntensve research for the last three decades. However, the early researches were lmted by the memory and power of the computer avalable at that tme. Wth the exploson of nformaton technology, there has been a dramatc ncrease of research n ths feld. The nterest devoted to ths feld s explaned by the potental mode of drect communcaton wth computers and the huge benefts that a system, desgned n the context of a commercal applcaton, could brng. Accordng to the way handwrtng data s generated, two dfferent approaches can be dstngushed: on-lne and off -lne. In the former, the data are captured durng the wrtng process by a specal pen on an electronc surface. In the latter, the data are acqured by a scanner after the wrtng process s over. Off-lne and on-lne recognton systems are also dscrmnated by the applcatons they are devoted to. The off-lne recognton s dedcated to bank check processng, mal sortng, readng of commercal forms, etc., whle the on-lne recognton s manly dedcated to pen computng ndustry and securty domans such as sgnature verfcaton and author authentcaton. Optcal II. OPTICAL CHARACTERS RECOGNITION SYSTEMS Today, the OCR (Optcal Characters Recognton) systems are only able to recognton hgh qualty prnted or neatly handwrtten documents. The current research s now basng on documents that are not well handled and ncludng severely degraded, omnfont machne prnted text, and unconstraned handwrtten text. A wde varety of technques are used to perform handwrtng recognton. A general model for handwrtng recognton s used to hghlght the many components of a handwrtng recognton system. The model begns wth an unknown handwrtten character that s presented at the nput of the recognton system as an mage. Frstly, to convert ths mage nto nformaton understandable by computers, parameterzaton operaton s needed whch extracts from the mage all of the necessary meanngful nformaton n a compact form, compatble wth the computer language. Ths nvolves the preprocessng of the mage to reduce some undesrable varablty that only contrbutes to complcate the recognton process. Operatons lke slant correcton, smoothng, normalzaton, etc. are carred out at ths stage. The second step s to extract dscrmnaton features from the mage to ether buld up a feature vector or to generate graphs, strng of codes or sequence of symbols. Manuscrpt receved January 3, 214; revsed Aprl 21, 214. Salm Ouchtat s wth the Electronc Research Laboratory of Skkda Unversty, Route El Hadak, Bp: 2 Skkda 21, Algera (e-mal: ouchtatsalm@yahoo.fr). Mohammed Rdjm s wth Computer Scence Department, Skkda Unversty, Route El Hadak, Bp: 2 Skkda 21, Algera (e-mal: medredjm@gmal.com). Mould Bedda s wth the Electrcal Engneerng Department, Al-Jouf Unversty, Arabe Saoudte (e-mal: mould_bedda@yahoo.fr). DOI: 1.773/IJCCE.214.V3.348 Characters Recognton (OCR) s one of the successful applcatons of handwrtng recognton; ths feld has been a topc of ntensve research for many years. Frst only the recognton of solated handwrtten characters was nvestgated [1], [2], but later whole words were addressed [3]. Most of the systems reported n the lterature untl today consder constraned recognton problems based on vocabulares from specfc domans, e.g. the recognton of handwrtten check amounts [4] or postal addresses [5], []. Free handwrtng recognton, wthout doman specfc constrants and large vocabulares, was addressed only recently n a few papers. The recognton rate of such systems s stll low, and there s a need to mprove t. Character and handwrtng recognton has a great potental n data and word processng, for nstance, automated postal address and ZIP code readng, data acquston n banks, text-voce conversons, etc. As a result of ntensve research and development efforts, systems are avalable for Englsh language [7]-[9], Chnese language [1], Arabc language [11]-[13] and handwrtten numerals [14], [15]. There s stll a sgnfcant performance gap between the human and the machne n recognzng unconstraned handwrtng. Ths s a dffcult research problem caused by huge varaton n wrtng styles and the overlappng and the ntersecton of neghborng characters. 349

2 Internatonal Journal of Computer and Communcaton Engneerng, Vol. 3, No. 5, September 214 However, the characterstcs of the features depend on the precedng step. Features extracton method s probably the most mportant factor n achevng hgh recognton performance n character recognton systems, extracted features must be nvarant to the dstortons, translatons, and rotatons. The features vector sze s also mportant n order to avod a phenomenon called the dmensonalty problem. Several methods for features extracton are desgned for dfferent representatons of the characters, such as bnary characters, character contour, skeletons (thnned characters), or even gray levels characters [1]. The features extracton methods are valued n terms of nvarance propertes, and expected dstortons and varablty of the character. Today, the studes are based not only on how to choose the approprate features extracton methods, but also on the selecton of meanngful and pertnent features from the features vector [17]-[19]. The fnal step s the character recognton; most recognzers have adopted classcal pattern classfcaton methods. Major approaches are statstcal based, structural analyss, template matchng, and neural network approaches. Sgnfcant progress has been made n these classfcaton methods but more work s requred to match human performance. III. A RECOGNITION SYSTEM FOR THE HANDWRITTEN ISOLATED DIGITS In the settng of the handwrtten wrtng recognton, we proposed an off lne system for the recognton of the solated handwrtten dgts (see Fg. 1), ths system s dvded n three phases: Acquston and preprocessng. Features extracton. Recognton. wth varable szes and varable thckness, and wth one thousand (1) samples for every class. Let's note that the characters mages of our database are formed only by two gray levels: the black for the object and the whte for the bottom. The Fg. 2 (see Fg. 2) shows some samples of the used database. 2) Preprocessng The preprocessng operatons are classcal operatons n mage processng, ther objectve s to clean and prepare the mage for the other steps of the system. The preprocessng attempts to elmnate some varablty related to the wrtng process and that are not very sgnfcant under the pont of vew of the recognton, such as the varablty due to the wrtng envronment, wrtng style, acquston and dgtzng of mage. For our case, we used the followng preprocessng operatons: a) Flterng and nverson of the gray levels Ths operaton conssts n elmnatng the noses n the bnary mage due to dfferent reasons (bad Acquston condtons, bad wrtng condtons, the wrter's mood.. etc.), n our case, some dgts are marked by the nose of type "peppers and salt", the applcaton of the flter medan on the dgt mage permtted us to elmnate easly ths type of nose. Let's note that for reasons of calculaton we reversed the gray levels of the character mage (black for the bottom and whte for the object). The Fg. 3 (see Fg. 3) shows us the flterng and nverson operaton of the gray levels of some handwrtten dgts. b) Normalzaton of the dgt mage Knowng that the dgts mages have varable szes, ths operaton conssts at normalzng the mage sze at 4 4 pxels (see Fg. 4). Fg. 2. Some samples of the used database. Fg. 1. General Schema of our handwrtten solated dgts recognton system. A. Acquston and Preprocessng 1) Acquston Before analyzng the dfferent processng steps, let's recall that we are especally nterested at the off lne processng. For our case, the acquston s done wth a numerc scanner of resoluton 3 dp wth 8 bts/pxels, the used samples are all possble classes of the handwrtten dgts (,1,2,3,4,5,,7,8,9) Fg. 3. Flterng and nverson of the gray levels of some handwrtten dgts. Fg. 4. Normalzaton of some handwrtten dgts. 35

3 Internatonal Journal of Computer and Communcaton Engneerng, Vol. 3, No. 5, September 214 Low vertcal Sondes Left horzontal Sondes Rght horzontal Sondes Let's note that each Sonde s appled every eght pxels whch allows us to encode the dgt mage wth thrty two () parameters. B. Features Extracton Features extracton s an mportant step n achevng good performance of OCR systems. However, the other steps also need to be optmzed to obtan the best possble performance, and these steps are not ndependent. The choce of features extracton method lmts or dctates the nature and output of the preprocessng step and the decson to use gray-scale versus bnary mage, flled representaton or contour, thnned skeletons versus full-stroke mages depends on the nature of the features to be extracted. Features extracton has been a topc of ntensve research and we can fnd a large number of features extracton methods n the lterature, but the real problem for a gven applcaton, s not only to fnd dfferent features extracton methods but whch features extracton method s the best?. Ths queston led us to characterze the avalable features extracton methods, so that the most promsng methods could be sorted out. In ths paper, we are especally nterested n the bnary mage of the dgts, and the methods used to extract the dscrmnaton features of are the followng: 3) Barr-features The Barr features have been used wth success n several works [2], they are calculated on the bnary dgts mages. Frstly, four mages parameters are generated, and every mage parameter corresponds to one of the followng drectons: east (e), North (n), Northeast (ne), Northwest (nw). Every mage parameter has a whole value representng the Barr length n the drecton n queston. The features are calculated from the mages parameters usng zones that overlap to assure a certan degree of smoothng. Ffteen rectangular zones are arranged n fve lnes wth three zones for every lne; every zone s of sze [(h/3) (w/2)] where h and w are respectvely the heght and the wdth of the mage. The hgh corners on the left of the zones are at the postons {(r, c): r=, h/, 2h/, 3h/, 4h/ and c=, w/4, 2w/4}. The values n every zone of the parameters mages are added and the sums are normalzed, and the dmenson of the features vector s 15 4=. If we suppose f 1, f 2, f 3, f 4 are the 1) The dstrbuton sequence Whle dvdng the dgt mage at a determned number of zones, the dstrbuton sequence characterzes a number of the object pxels n relaton to the total pxels number n a gven zone. For our applcaton, the dgt mage s dvded n 4 zones (see Fg. 5), and every zone s of sze 8 8 pxel and the values of the dstrbuton sequence are defned by: x N mages parameters assocated at a shape n entry and Z (=1, 2.15) s an rectangular zone of sze [(h/3) (w/2)] wth the top corner on the left s ( r, c ), the value Pk of the N Wth: x s the th value of the dstrbuton sequence. N s a number of the object pxels n the th zone. N s a total pxels number n the th zone. parameter assocated to the Z zone for the mage parameter f k (k=1,2,3,4) s gven lke follows: Pk 1 N r w c h 2 3 r r c c f k ( r, c ) Wth N s a factor of normalzaton (for our applcaton N=4). The Fg. 7 shows the mages parameters of the dgts three and eght and ther Barr features. Fg.. Applcaton of the Sondes on the handwrtten dgt three. Fg. 5. The dgts three and eght and ther dstrbuton sequences. 2) Applcaton of the Sondes Ths method s based on measurng the normalzed dstance between the border of the mage and the frst object pxel encountered (see Fg. ), from ths pont of vew, we can defne the followng probes dstance: Top vertcal Sondes Fg. 7. The Images parameters of the dgt three and ts barr features. 351

4 Internatonal Journal of Computer and Communcaton Engneerng, Vol. 3, No. 5, September 214 4) The central moments of mage codng accordng to the drectons of Freeman Ths method consst to dvde the dgt mage nto four zones of equal sze (sze of each zone s pxel) and to encode each zone accordng to the eght drectons of Freeman (see Fg. 8). In other words, for a gven zone, each parameter s the accumulated object pxel n one of the drectons of Freeman. So each zone wll be encoded by eght parameters, and the dgt mage wll be encoded by a sequence of thrty-two parameters (see Fg. 9). The central moments of the sequence of Freeman are obtaned by the formulas (3), (4) and (5). pxels accordng the drecton n queston. Whle the centered moments of the projecton are gven by the followng formulas (3), (4) and (5). For our applcaton, we chose the frst sx moments for every projecton. u k M 1 k ( x x). p( x ) p( x ) prob [ x x ] x 1 x p( x ) Wth u : s the centered moment of k order k x s the mean value of the projecton n queston. p ( x ) : s the probablty of l 'element x n the sequence projecton. M: s projecton sequence sze. For our applcaton, we chose the frst sx moments. Fg. 8. The eght Freeman drectons. Fg. 1. The dgts three and eght and ther dfferent projectons. ) The centered moments of the dfferent profles By defnton, the mage profle n a gven drecton s the set of the object pxels seen whle standng n ths drecton. From ths pont of vew, we can defne the followng profles (see Fg. 11): Low profle: t s the set of the object pxels seen whle standng below the mage of the number Hgh profle: t s the set of the object pxels seen whle standng over the mage of the number Left profle: t s the set of the object pxels seen whle standng on the left of the mage of the number Rght profle: t s the set of the object pxels seen whle standng on the rght of the mage of the number Whle the centered moments of the dfferent profles are calculated by formulas: 3, 4 and 5. For our applcaton, we chose the frst sx moments for every profle. Fg. 9. The dgts three and ts centred moments of ther Freeman codng. 5) The centered moments of the dfferent projectons In ths case, the dscrmnaton parameters are the centered moments extract from the follow projectons (see Fg. 1): The vertcal projecton: for a gven column, the value of the projecton s equal to the number of object pxels n ths column. The horzontal projecton: for a gven lne, the value of the projecton s equal to the number of object pxels n ths lne. The dagonals projectons: for a gven dagonal, the value of the projecton s equal to the number of object Fg. 11. The dgts three and eght and ther dfferent profles. 352

5 Internatonal Journal of Computer and Communcaton Engneerng, Vol. 3, No. 5, September 214 7) Used features vector It s the features vector used to characterze the dgt mage, and wth whch, we wll noursh the recognton module. For our applcaton we have tred to test sx vectors (V1, V2, V3, V4, V5, V) the Table I shows the composton of each vector. TABLE I: COMPOSITION OF THE DIFFERENT USED VECTORS V1 V2 V3 V4 V5 V Parameters Nbr Nbr Nbr Nbr Nbr Nbr D_S S_P B_F CM_FC CM_PR CM_PRF Parameters Number Wth: D_S: Dstrbuton Sequence S_P: Sondes Parameters B_F: Barre Features CM_FC: Centred Moments of Freeman Codng CM_PR: Centred Moments of dfferent Projectons CM_PRF: Centred Moments of the dfferent Profles C. Dgt Recognton The handwrtten dgts recognton s a problem for whch a recognton model must necessarly take n account an mportant number of varabltes, dce at the tme, the recognton technques based on the neural networks can brng certan suppleness for the constructon of such models. For our system, we opted for an MLP (Mult-Layers Perceptron) whch s the most wdely studed and used neural network classer. Moreover, MLPs are effcent tools for learnng large databases. The used MLP n our work s traned wth the back propagaton wth momentum tranng algorthm. The transfer functon employed s the famlar sgmod functon. 1) The nput data The database conssts of ten thousand (1) bnary mages. These mages represent all classes possble of the handwrtten dgts (,1,2,3,4,5,,7,8,9) wth varable szes and varable thckness, and wth one thousand (1) samples for every class. Ths database s dvded to two sets, 7 for tranng the neural network and 3 % for testng t. 2) Neural network parameters The structure of the used neural network vary dependng of the used vector parameters, the nput layer nodes number (N_IL) s equal to the sze of the used features vector, the output layer nodes number s equal to the classes number to recognze (N_OL=1), for the hdden layers, we used a double hdden layer for all the feature vectors, except for the fourth feature vector where we use one hdden layer, and the number of nodes (N_HL) for each hdden layer s fxed by gropng. The ntal connecton weghts are n the range [-1, 1]. The Table II shows us the dfferent structures of the used neural network. TABLE II: DIFFERENT STRUCTURES OF THE USED NEURAL NETWORK Used N_IL N_HL1 N_HL2 N_OL vector V V V V V V ) The tranng process For tranng the neural network, back propagaton wth momentum tranng method s followed. Ths method was selected because of ts smplcty and because t has been prevously used on a number of pattern recognton problems. The method works on the prncple of gradent descent. The algorthm uses two parameters whch are expermentally set, the learnng rate and momentum. These parameters allow the algorthm to converge more easly f they are properly set by the expermenter. For our case, we have opted for the followng values: =.35 and =.9. Durng the learnng phase the neural network learns by example and the connecton weghts are updated n an teratve manner. The tranng process for the network s stopped only when the sum of squared error falls below.1. 4) The expermental results The neural network performances are measured on the entre database (tranng or learnng set and testng set). Durng ths phase, we present the dgt mage to recognze to the system entry, and we collect at the ext ts affectaton to one of the possble classes. The results can be: Recognzed dgt: the system arrves to assocate one and only one prototype to the dgt to recognze. Ambguous dgt: the system proposes several prototypes to the dgt to recognze. Rejected dgt: the system doesn't take any decson of classfcaton. Non recognzed dgt: the system arrves to take a decson for the presented dgt, but t s not the good decson. The results and the dfferent rates are regrouped n the Table III: TABLE III: RESULTS AND DIFFERENT OBTAINED RATES R_R(%) A_R(%) J_R(%) NR_R(%) V V V V V V

6 Internatonal Journal of Computer and Communcaton Engneerng, Vol. 3, No. 5, September 214 [7] Wth: R-R: Recognzer rate; A-R: Ambguty rate. J-R: Reject rate; NR-R: Non recognzer rate. [8] IV. CONCLUSION AND PERSPECTIVES [9] The recognton of the handwrtten solated dgt s a problem for whch a model of recognton must necessarly take n account an mportant number of varabltes and constrants due at the varaton of the dgt shape of the same class (varaton of the wrtng styles, use of dfferent wrtng nstruments, varaton of wrtng of a wrter to another. et al.). In our work, we presented an off lne system for the recognton of solated dgts recognton, the study s based manly on the evaluaton of neural network performances, traned wth the gradent back propagaton algorthm and fed by several feature vectors. The used parameters to form the nput vector of the neural network are extracted on the bnary mages of the dgts by several methods: the dstrbuton sequence, sondes applcaton, the Barr features, the central moments of mage codng accordng to the drectons of Freeman, and the centred moments of the dfferent projectons and profles. The gotten results are very encouragng and promoters; however we foresee the followng evoluton possbltes: To wden the database by takng n account a bgger number of wrters and wrtng nstruments. To consder other classfcaton methods. To use of the algorthms capable to control the ambguty, reject and non recognzer rates by adjustng the reject and ambguty rates by use of sutable doorsteps. To use of other features extracton methods. Use of the post-processng technques to mprove the system performances. ACKNOWLEDGMENT REFERENCES [2] [3] [4] [5] [] [11] [12] [13] [14] [15] [1] [17] [18] [19] [2] Ths work was supported n part by the Electronc Research Laboratory of Skkda, Unversty of August 2, 1955 Algera- and Electrcal Engneerng Department, Al-jouf Unversty Arabe Saoudte. [1] [1] J. Mantas, An overvew of character recognton methodologes, Pattern Recognton, vol. 19, no., pp , 198. S. Mor, C. Y. Suen, and K. Yamamoto, Hstorcal revew of OCR research and development, Proceedngs of the IEEE, vol. 8, no. 7, pp , A. L. Koerch, R. Sabourn, C. Y. Suen, and A. El-Yacoub, A syntax drected level buldng algorthm for large vocabulary handwrtten word recognton, n Proc. 4th Internatonal Workshop on Document Analyss Systems (DAS 2), Ro de Janero, Brazl, December 2. L. S. Olvera, R. Sabourn, F. Bortolozz, and C. Y. Suen, A modular system to recognze numercal amounts on brazlan bank checks, n Proc. th Internatonal Conference on Document Analyss and Recognton (ICDAR 21), Seattle, USA, September 1-13, 21, pp A. Flatov, N. Nktn, A. Volgunn, and P. Zelnsky, The address scrpt TM recognton system for handwrtten envelopes, n Proc. Internatonal Assocaton for Pattern Recognton Workshop on Document Analyss Systems (DAS 98), Nagano, Japan, November 4-, 1998, pp A. E. Yacoub, Modélsaton markovenne de L écrture manuscrte applcaton `a la reconnassance des adresses postales, PhD thess, Unverstéde Rennes 1, Rennes, France, J. Hu, M. K. Brown, and W. Turn, HMM based on-lne handwrtng recognton, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 18, pp , October 199. G. Km and V. Govndaraju, A lexcon drven approach to handwrtten word recognton for real-tme applcatons, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 19, pp , Aprl R. Buse, Z. Q. Lu, and T. Caell, A structural and relatonal approach to handwrtten word recognton, IEEE Trans. Systems, Man and Cybernetcs, Part-B, vol. 27, pp , October, 1997, K. Lu, Y. S. Huang, and C. Y. Suen, Identfcaton of fork ponts on the skeletons of handwrtten Chnese characters, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 21, pp , October, A. Amn, Off-Lne Arabc character recognton the state of the art [revew], Pattern Recognton, vol. 31, no. 5, pp , S. Ouchtat, L. Bennacer, and M. Bedda, An off lne system for the Arabc handwrtten words recognton, Internatonal Revew on Computers and Software, vol. 3, no., pp , 28. S. Ouchtat and M. Bedda, Multlayer neural network for the recognton of the Arabc handwrtten words of the Algeran Department, n Proc. Book on the Internatonal Conference on Appled Analyss and Mathematcal Modelng ICAAMM 213, 2-5 June 213, Yldz Techncal Unversty, Istanbul, Terkey. J. Ca and Z. Q. Lu, Integraton of structural and statstcal nformaton for unconstraned handwrtten numeral recognton, IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 21, pp , March S. Ouchtat, M. Redjm, M. Bedda, and F. Bouchareb, A new off lne system for handwrtten dgts recognton, Asan Journal of Informaton Technology, vol. 5, no., pp. 2-2, 2. O. D. Trer, A. K. Jan, and T. Taxt, Feature extracton methods for character recognton a survey, Pattern Recognton, vol. 29, no. 4, pp. 41-2, 199. M. Bedda, M. Ramdan, and S. Ouchtat, Sur le chox d une représentaton des caractères manuscrts arabes, n Proc. 2ème Conférence Internatonale Sgnaux, Systèmes, et Automatque SSA2 99, Unverstéde Blda, Algére, 1-12 Ma 1999, pp F. Grandder, Un nouvel algorthme de sélecton de caractérstques applcaton àla lecture automatque de l écrture manuscrte, Thèse de doctorat en géne Ph.D, Ecole de technologe supéreure, Unversté du Quèbec Canada, Janver 23. N. Benahmed, Optmsaton des réseaux de neurones pour la reconnassance des chffres manuscrts solés, sélecton et pondératon des prmtves par algorthmes génétques, Thèse pour l obtenton de la Maîtrse en Géne de la Producton Automatsée, Montréal le 1 Mars 22. S. Ouchtat, M. Ramdan, and M. Bedda, Un réseau de neurones multcouches pour la reconnassance hors-lgne des caractères manuscrts Arabes, Revue Scences et Technologe Unversté de Constantne, no. 17, pp , Jun 22. Salm Ouchtat was born on August 7, 197 n Azzaba W Skkda Algera. He receved hs B. Eng. and M.Sc. degrees n electronc from Annaba Unversty, Algera n 1994 and 1999 respectvely. He obtaned hs doctorate dploma n 27 n automatc and hs HDR dploma (habltaton to drect the research) n electronc n 21 from Annaba Unversty. He s currently an assocate professor at Skkda Unversty, member of the Skkda Electronc Laboratory, responsble course of ndustral control, responsble of the research project wth the ttle: Selecton and weghtng of dscrmnaton parameters n a recognzng handwrtten dgts system and member of the project: Realzaton of a system for readng of the Algerans postal checks. He has a several scentfc works publshed n many nternatonal journals such as: Segmentaton and Recognton of Handwrtten Numerc Chans (Journal of Computer Scence), A New off Lne System for Handwrtten Dgts Recognton" (Asan Journal of Informaton Technology), An off lne System for the Handwrtten Numerc Chans Recognton (Internatonal Journal of Soft Computng). Hs researches area focused manly on the handwrtten recognton, artfcal ntellgence and mage processng. Dr. Salm Ouchtat was a member of the scentfc commttee of the Electrcal Engneerng Department n Skkda Unversty from 25-21, member of the scentfc commttee of the Technology Faculty n the Skkda Unversty from 25-28, he was a revewer n several nternatonal conferences such as: Frst Internatonal Conference on 'Networked Dgtal Technologes(NDT

7 Internatonal Journal of Computer and Communcaton Engneerng, Vol. 3, No. 5, September ) Ostrava, The Czech Republc, July 29-31, 29 and The Second Internatonal Conference on the Applcatons of Dgtal Informaton and Web Technologes (ICADIWT 29), August London Metropoltan Unversty, UK, member of the Scentfc Commttee n several nternatonal conference. Mohammed Redjm was born on March, 195 n Rejattas, Skkda, he receved the dploma of docteur - ngeneur n computer scence from the Unversty of Llle 1, France n 1984, and hs HDR dploma (Habltaton to drect the research) n computer scence n 27 from Annaba Unversty. He s currently an assocate professor of computer scence at Skkda Unversty, responsble of the team research Modelng and smulaton of complex processes at Skkda Computer Scence and Communcaton Laboratory, responsble of the research project wth the ttle: Cooperatve approaches for mages segmentaton, responsble of the research project wth the ttle: Bayesan approach n computer vson. He has several scentfc works publshed n many nternatonal journals such as: An adaptve mult-agent system approach for mage segmentaton, (Internatonal Journal of Computer Applcatons), An mage encrypton approach usng stream cphers based on nonlnear flter generator, (Theoretcal and Appled Informaton Technology). Hs man research nterests nclude modelng and smulaton systems manly by usng mult-agents systems, mage recognton and computng hardware and software systems. Dr. Mohammed Redjm was a revewer and member of the Scentfc Commttee n several nternatonal conferences such as: Frst Conference on Theoretcal and Applcatve Aspects of Computer Scence Skkda Unversty-, Fractonal Order Systems and Applcatons (SOFA 21) Skkda Unversty Mould Bedda was born on March 1, 195 n El_Oued Algera. He receved bachelor s degree from the Unversty of Haouar Boumedenne Algers, Algera n 1981, the M.Sc degree from the Unversty of Languedoc Montpeler, French, n 1982, and the PhD degree n electrcal engneerng from the Unversty Nancy 2, Nancy, French, n He was an assstant professor from , assocate professor from 199- and professor from -2 at the Unversty of Annaba Algera, from 2 to date full professor at the College of Engneerng at Aljouf Unversty KSA. He has several scentfc works publshed n many nternatonal journals. Professor Mould Bedda was the drector of Automatc and Sgnals Laboratory from Hs researches nterests are: DSP, speech processng, OCR, artfcal ntellgence, and bomedcal. 355