Arabic Handwritten Word Recognition based on Dynamic Bayesian Network

Transcription

1 1024 The Internatonal Arab Journal of Informaton Technology Vol. 13, o. 6B, 2016 Arabc Handwrtten Word Recognton based on Dynamc Bayesan etwork Khaoula Jayech, Mohamed Al Mahjoub, and ajoua Ben Amara Department of Computer Scence, atonal Engneerng School of Sousse Unversty of Sousse, Tunsa Abstract: Dstngushng an Arabc handwrtten text s a hard task because the Arabc word s morphologcally complex and the wrtng style from one model s hghly varable, lke the recognton of words representng the names of Tunsan ctes. Actually, ths s the frst work based on the Dynamc Herarchcal Bayesan etwork (DHB). Its objectve s to get the best model by learnng the structure and parameter of Arabc handwrtng to decrease the complexty of the recognton process by allowng the partal recognton. In fact, we propose segmentng the word based on a vertcal smoothed hstogram projecton usng varous wdth values to put down the segmentaton error. After that, we extract the characterstcs of each cell usng the Zernke and HU moments, whch are nvarant to rotaton, translaton and scalng. Then, the sub-character s estmated at the lowest level of the Bayesan etwork (B) and the character s estmated at the hghest level of the B. The overall Arabc words are processed by a dynamc B. Our approach s tested usng the IF/EIT database, where the experment results are very promsng. Keywords: Arabc handwrtng recognton, dynamc B, herarchcal model, OCR, IF/EIT databases. Receved October 21, 2013; accepted June 18, 2014; publshed onlne June 11, Introducton The term handwrtng recognton s used most often to descrbe the capablty of a computer to transform human wrtng nto text. It has many applcatons n many felds such as bank-check processng, postaddress nterpretaton, document archvng, mal sortng and form processng n admnstraton and nsurance [2, 3, 4, 12, 15, 17]. The handwrtng recognton can be dvded nto two categores: The onlne recognton and the off-lne one. In the onlne case, temporal nformaton lke the pen-tp coordnates as a functon of tme s avalable [6] so; t makes t easer than the offlne case. In addton, the offlne handwrtng recognton has many problems that can be dvded nto two sgnfcant research areas: Segmentaton and recognton [14, 19, 21]. These two areas are very dffcult tasks especally n Arabc handwrtng due to the cursve nature of the Arabc scrpt and the dfferent handwrtng styles and uncertanty of human wrtng and other problems such as overlappng of some pseudo-words, fuson of dacrtcal marks n dfferent poston n the word, shape dscrmnaton and varaton, scannng methods, wrtng dscontnuty and slant. In ths work, we propose a novel approach that reduces the segmentaton step s error and ncreases the recognton accuracy. The recognton step s preceded by a feature extracton process that extracts the characters and subcharacter features from the segmented word. In the segmentaton step, we have used a smoothed vertcal hstogram projecton wth dfferent wdth values to mnmze the segmentaton error. The overall system dagram s shown n Fgure 1. Our system processes multple analyss levels. Character Grapheme1 Grapheme2 Grapheme3 a1 a2 a3 a1 a2 a3 a1 a2 Characters Sequence Character Grapheme1 Grapheme2 Grapheme3 a1 a2 a3 a1 a2 a3 a1 a2 Character Structure Word classfcaton Character Recognton Q1 Q2 Q3 Y1 Y2 Y3 DB HB Feature Extracton usng the Segmentaton Algorthm Fgure 1. System dagram. The character features are extracted from our developed segmentaton system; afterwards, they are used to estmate our character class usng the Bayesan etwork (B). The class of character results s then concatenated to form a sequence and the sequence classfcaton s performed by a Dynamc Bayesan etwork (DB). In the character class estmaton step, the left panel shows a tree structure that represents the bloc character of the word. The root node represents the character class and ts chldren nodes represent the frame generated by a unform sldng wndow. Each frame s subdvded nto cells. The rght panel shows a B to estmate the character class n each bloc. In the sequence classfcaton stage, the left panel shows the word sequence represented by the concatenaton of three tree structures representng a character at each frame. The rght panel shows a DB whch recognzes the word mage.

2 Arabc Handwrtten Word Recognton based on Dynamc Bayesan etwork 1025 The contrbutons of ths paper are as follows: A new herarchcal framework s detaled for the recognton of the off-lne Arabc handwrtten word usng the Herarchcal Bayesan etwork (HB), a stochastc graphcal model s proposed for the word recognton and a combned system that balances between segmentaton and recognton s used to match and reduce the segmentaton error to have a hgher recognton rate. The rest of the paper s dvded as follows. In secton 2, we descrbe the works related to our research. Secton 3 presents the developed system and the concepts of the Dynamc Herarchcal Bayesan etworks (DHBs). In secton 4, we descrbe the expermentatons and results. Fnally, we present the dscusson and conclusons. 2. Related Works A revew of the lterature ndcates that relatvely lmted applcatons, based on Bs and DBs, have been developed for the handwrtng recognton. We state the man works n what follows. Halloul et al. [10] developed a new probablstc model desgned for the off-lne prnted character recognton based on the DB. Ther system was evaluated usng varous DB archtectures and acheved a recognton rate of 98.3% wth the vertcal HMM and 93.7% wth the horzontal one. evertheless, when testng degraded letters, the recognton rate went down to 93.8% wth the vertcal HMM and 88.1% wth the horzontal one. Also, Mahjoub et al. [18] proposed a new system for the offlne handwrtten Arabc word recognton based on coupled HMMs, consdered as a sngle DB. Each mage of the handwrtten word was transformed nto two sequences of feature vectors that would be the observatons to be gven to the DB model. The developed system was tested on the IF/EIT database and acheved a recognton rate between 67.9% and 77.4%. Al-Hajj et al. [2] presented a system for the off-lne recognton of handwrtten Arabc cty names. The system combned three homogeneous HMMs havng the same topology as a reference system whch dffered only n the orentaton of the sldng wndow. The results showed that the combnaton of classfers would perform better than a sngle classfer dealng wth slant-corrected mages and that the approach was robust for a wde range of orentaton angles. Benouareth et al. [6] proposed an offlne unconstraned Arabc-handwrtten-word recognton system based on a segmentaton-free approach and on dscrete Hdden Markov Models (HMMs) wth an explct state duraton. The system appled a sldng wndow approach and extracted a set of statstcal and structural features. Several experments were performed usng the IF/EIT and the recognton rate, saved wth a non-unform segmentaton, was better than the unform one. Parvez and Mahmoud [25] suggested an off-lne Arabc handwrtten text recognton system usng structural technques. The Arabc word mage was preprocessed. Then, a segmentaton algorthm was ntegrated nto the recognton phase of the handwrtten text. The recognton of the Arabc PAWs was done usng a novel fuzzy polygon matchng algorthm. Ths proposed system was tested usng the IF/EIT database and the recognton rate acheved was promsng, whch was about 79.58%. Alkhateeb [5] developed a mult-classfcaton system based on the DB. Frst, the words were pretreated and normalzed. Then, a two unform sldng wndow were used to segment the word mage n a horzontal and vertcal drecton n order to extract the correspondng features. After that, several coupled- HMM archtectures, vewed as a sngle DB, (see Fgure 2), were constructed by addng drected edges between the two streams wthn the same tme slce n dfferent ways. Ther suggested system has been successfully tested on the IF/EIT database and the results were promsng. Fgure 2. Coupled archtecture representng a sngle DB [5]. In contrast to Alkhateeb, we have proposed, n ths work, a herarchcal model based on the DHBs to recognze an offlne Arabc-handwrtng word. Frst, after the preprocessng step, we have segmented the word nto characters and sub-characters usng the smoothed vertcal hstogram projecton. Then, each character has been segmented usng a unform sldng wndow nto 3 frames dvded nto 2 cells. Fnally, we have extracted the HU and Zernke moments for each cell and used t to tran our constructed HDBs. In fact, the advantages of such a model consst n fndng the best model of Arabc handwrtng to reduce the complexty of the recognton process by permttng the partal recognton. Ths model has made good results n the human nteracton doman, but by analysng the lterature, t has never been used n the Arabc-handwrtng recognton. 3. System Archtecture The system archtecture that we have proposed to the recognton of offlne handwrtten Arabc cty names s based on the DHB as shown n Fgure 3. It contans fves stages n terms of pre-processng, segmentaton, feature extracton, vector quantzaton and learnng and classfcaton. The frst step s the pre-processng; t conssts n normalzng the mage. After that, the word s segmented, usng our proposed non-unform segmentaton, nto characters. Then, these characters

3 1026 The Internatonal Arab Journal of Informaton Technology Vol. 13, o. 6B, 2016 are segmented, usng a unform segmentaton, nto graphemes. In the thrd step, we extract the relable features to characterze these graphemes. The use of the dscrete DBs as a classfer s necessary to process to the next step to quantze these ones. In the fourth step, we have traned and optmzed our model to use t n the classfcaton step. Parameter Learnng Structure Learnng IF/EIT data base Pre-processng Segmentaton Feature Extracton Vector Quantzaton Class n Class1 Inference P( O/ ) Recognton arg max PO ( / ) Class مارث, شعال Fgure 3. The proposed block dagram system for word recognton Pre-Processng and Segmentaton The am of the pre-processng step s to enhance the word mage to have a better segmentaton and so ncrease the recognton rate. The pre-processng chan of the recognzer starts out wth some of the usual operaton such as: Thnnng, remove horzontal and vertcal space and the dacrtcal marks. As t s well known, the segmentaton s a very hard step n Arabchandwrtten-word recognton because of ts hgh varablty and the complex morphology of Arabc as a sem cursve scrpt. So, we have ntroduced, n the next step, a non-unform segmentaton based on the vertcal hstogram projecton to segment the word nto characters. To solve the problem of over or under segmentaton of a word, we have proposed to use a heurstc. It consst n smoothng the vertcal projecton hstogram to gve us the best number of frames, whch have to be the same as the number of characters n the word, and the best boundares of each characters for each model. After that, we segment the character nto frames and cells usng a unform segmentaton. The number of frames s fxed emprcally, yeldng the hghest recognton rate, to 3 and the number of cells to 2. The pre-processng and segmentaton algorthm s detaled as shown n Algorthm 1: Algorthm 1: Pre-Processng and segmentaton algorthm. For k=1 to number of mages of each word Begn Img_n=Read the bnary mage. Img_n=Resze the Img_n nto Img_n=Remove the horzontal and vertcal space from mage. Img_n_Dac=Remove the dacrtcal marks from Img_n. Blocks_Lmt=Segment the mage word nto characters by fndng the best boundares of each character usng the peaks of the smoothed vertcal hstogram projecton of Img_n_Dac. The optmal value of the wdth of smoothng s determnng emprcally gvng us the best number of frames. Block_Charact= Dvde the Img_n usng each Blocks_Lmt nto characters. Then, we dvde each characters nto 3 unform horzontal frames and each frame nto 2 unform cells. end 3.2. Feature Extracton and Vector Quantzaton Feature extracton s an mportant step n the handwrtten recognton. The choce of feature sets should be ndependent to the sze, orentaton and locaton of the pattern. So, wth referrng to the lterature, we have extracted for each cell the moment nvarant of Zernke and HU whch are nvarant to translaton, rotaton and scalng, to check the prmtves of each character. evertheless, the descrptors of these moments gve us contnuous features. However, we use dscrete DBs, so we have to process to the next step of pre-treatment, whch consst n quantzng each contnuous feature vector representng a cell to a dscrete symbol. Ths quantzaton s done by k-means method. The k-means method ams to cluster the feature vector of the tranng samples nto several classes. Each one s represented by ts centroîd whch s a 17 dmensonal vector. After that, the ndex of each centroîd s consdered as a codebook symbol. For each model, we have chosen the optmal codebook usng a valdaton data set Classfcaton The DBs are a class of temporal graphcal probablstc models that have become a standard tool for modellng varous stochastc tme varyng phenomena. The temporal probablstc graphcal models as two-tme Bs are the most used and popular models for the DB. Before ntroducng the noton of the DB, we wll brefly recall the defnton of the B Bayesan etwork Defnton: A statc B combnes between the graph theory and probablty theory. Thus, a B conssts of a Drected Acyclc Graph (DAG) whose nodes are random varables that may have a dscrete number of possble states or contnuous values. The B s defned by: A DAG G= (V, E), where V s a set of nodes of G and E s a set of arcs of G. A fnte probablstc space (Ω, Z, p). A set of random varables assocated wth graph nodes and defned on (Ω, Z, p) as: p ( V, V,..., V ) p ( V C ( V )) Where C(V ) s a set cause [parents] V n the graph G. (1)

4 Arabc Handwrtten Word Recognton based on Dynamc Bayesan etwork Herarchcal Bayesan etwork The HBs are a generalzaton of the statc Bs, where a node n the network may be an aggregate data type. Ths allows the random varables of the network to arbtrarly represent structured types. Wthn a sngle node, there may also be lnks between components, representng probablstc dependences among parts of the structure. The HBs encode the condtonal probablty dependences n the same way as the statc Bs as shown n Fgure 4. Fgure 4. HB wth three layers Dynamc Herarchcal Bayesan etwork The DHBs are an extenson of the statc HBs whch represent the temporal evoluton of any random varable. Thus, a dynamc B s a chan of the same B repeated as many tmes as needed. The temporal dynamcs are represented by arcs connectng the varous statc Bs between each other Fgure 5. The constructon of a DHB requres determnng ts structure and ts parameters. So, to specfy a DB [23] we need to defne the ntra-slce topology (wthn a slce), the nter-slce topology (between two slces) and the parameters for the frst two slces as explaned n the next secton. Fgure 5. DHB for a word mage composed of three characters Inference and Learnng n DB Ths partcular DB s equvalent to a tradtonal HMM because t emulates what an HMM does. One major dfference s that t explctly represents the HB consstence of the word, character and sub character. Structural Learnng: Descrpton of settngs. 0 1 X x, x,..., x t t t t Is a set of the hdden state n the tme t, where s the number of the latent states n one slce M M y, y,..., y, y, y,..., y,..., t t t t t t Y t (3) 1 2 M y, y,..., y t t t (2) Is a set of the observed state n the tme t, where each state has two observed states, wth M j j[1, ] beng the number of the observed state of each latent state. We can assume that the set of: 1 2 Yt Y, Y,..., Y t t t : The ntal state probablty. A a j B b j ( k ) 1 P ( X ) P ( x j x ), j 1 : The state transton probablty. a P ( x j x ),1, j T j t t 1 j : The observaton probablty dstrbutons. j t t t t 1 b ( k ) P ( Y k X j ) P ( Y k X j ) Parameter Learnng:More succnctly, a DHB can be represented by the parameter =(A, B, ). To sutably use the DHB n the handwrtng recognton, three problems must be solved. The frst s concerned wth the probablty evaluaton of an observaton sequence, gven the model. In the second problem, we attempt to determne the state sequence that best explans the nput sequence of observatons. The thrd problem conssts n determnng the method to optmze the model parameters to satsfy a specfc optmzaton crteron. The model parameter determnaton s usually done by the Expectaton/Maxmzaton procedure and conssts n teratvely maxmzng the observaton, gven the model and often converges to a local maxmum. As the DB usually captures the jont dstrbuton of the varable sequence, t s typcally learned by maxmzng the log lkelhood of the tranng sequence MLE =argmax P(Y). Inference and Recognton: The smplest nference method for a dscrete-state DB s to convert t to an HMM and then to apply the forward-backward algorthm. Therefore, the probablty P(O) of a DB model wth an explct state duraton, for an observaton vector sequence usng the length of the observaton sequence for each t slce, can be computed by a generalzed forward-backward algorthm. Ths choce s justfed by the avalablty of the estmaton formulas, whch are derved wth respect to the lkelhood crteron, for the parameter set of the dstrbuton. In the recognton phase, a soluton to the state decodng problem, based on dynamc programmng, has been desgned, namely the Vterb algorthm. The sequence of the extracted feature vectors s passed to a lexcon network formed of word models. 4. Expermental Results In the followng secton, we wll present the expermental results done usng a lexcon from the IF/EIT data base. The conducted experments are (4) (5) (6) (7)

5 Recognton Rates (%) Recognton Rates (%) Recognton Rates (%) Recognton Rates (%) 1028 The Internatonal Arab Journal of Informaton Technology Vol. 13, o. 6B, 2016 compared wth the other system. The results are presented below n detal IF/EIT Database The IF/EIT data base conssts of 946 handwrtten Tunsan cty names and ther correspondng postcodes. The old verson (v1.0p2 verson) of the database contans 26,459 Arabc names handwrtten by 411 dfferent people. In the new v2.0 p1e verson, the addtonal set e contanng 6,033 names handwrtten by 87 wrters has been added, whch makes the whole set have 32,492 name samples. In the last new verson, two addtonal sets have been added: set f contanng 8,671 names and set s contanng 1,573 ones. Relevant experments and results are presented n the next subsectons Experments on IF/EIT Database v1.0p2 In ths group of experments, three subsets (a-c) are used for tranng and valdate the DHB and another one (d) for testng. After pre-processng, the optmal wdth of the smoothed hstogram s determned emprcally usng dfferent numbers varyng from 8 to 15. We have chosen the wdth that provdes a number of characters that reflects better the number of characters n the model. After segmentng the word nto characters, we dvde the character block nto 3 frames and each frame nto 2 cells. We have chosen the frame and cell number that provdes the hghest recognton rate. Then, an optmal number of states used n the DB s also determned emprcally. Usng possble numbers varyng equally from 10 to 25, the obtaned recognton rates are lsted n Fgure 6. It has been noted that the recognton rate mproves as the number of the states ncreases to reach the maxmum possble state for a specfc feature set. Ths makes the tranng data ndependent from the testng data, hence avodng over-fttng the classfer to test the data. In our case, as shown n Fgure 6, the optmal state number of class c3 s found as 21 and of the class c4 s found as 13. In ths paper, a DB has been used for the Arabc handwrtten word. Each character s represented by ts feature vector and each character requres a number of observatons for tranng and testng the DB. In the phase of quantzng the data, experments have been conducted usng 7 codebook sze parameter values: 6, 18, 24, 36, 48, 58, 68 and 100. Fgure 6 shows the result for a dfferent codebook sze that yelds to a better recognton rate of a شماخ and الرضاع class. The best performance has been found usng the number of observaton szes whch s 58 for C4 and 38 for C7 to obtan a good trade off between the best recognton accuracy and the low tme factor. Fgure 7 shows the expermental results of the performance evaluaton of our recognton system usng the tranng set (a-c) and the test set (d). Ths leads to an average recognton rate of about 91.25%, whch s acheved wth the tranng set and of about 82% whch s acheved wth the test set. Test Set Class Test Set Class Fgure 7. Recognton rates obtaned by tranng and test set for some classes. Table 1 gves the comparatve results on set d of the IF/EIT database. Table 1. Comparatve study usng v1.0p2 database. Authors Classfer Tranng Data Test Data Recognton Rate Top1 Top 10 Al-Hajj et al. [2] HMM Set a-c Set d Benouareth et al. [6] HMM Set a-c Set d umber Of State Dreuw et al. [7] HMM Set a-c Set d Unknown El-Abed and Margner [20] HMM Set a-c Set d Al-Hajj et al. [1] HMM Set a-c Set d Unknown Menasr et al. [22] HMM/A Set a-c Set d 87.2 Unknown Pechwtz and Margner [24] HMM Set a-c Set d Alkhateeb et al. [3] HMM Set a-c Set d Kundu et al.[16] Varable Duraton Set a-c Set d 60 Unknown Proposed System HMM DB Set a-c Set d 82 Unknown Code Book Sze Fgure 6. Recognton rate vs number of state and code book sze.

6 Recognton Rates (%) Arabc Handwrtten Word Recognton based on Dynamc Bayesan etwork Experments on v2.0p1e IF/EIT Database Verson and Comparatve Study We present now our results on set e of the IF/EIT database. In ths group of experments, four subsets (ad) are used for tranng and valdate the DHB and set e for testng; we have acheved a word recognton accuracy of 78.5%. Fgure 8 llustrates the recognton rates obtaned by test set e for some classes. Class Fgure 8. Recognton rates obtaned by test set e for some classes. Table 2 gves the comparatve results on set e of IF/EIT database. A detaled analyss of the results s gven below. Table 2. Comparatve study usng the v2.0p1e database. Authors Classfer Tranng Data Test Data Elbaat et al. [8] HMM Set a-d Set e Lexcon Usage ot mentoned Recognton Rate Sngle- Classfer Margner et al. [20] HMM Set a-d Set e yes hamdan et al. [11] Multple HMM Set a-d Set e Kessentn et al. [13] Parvez et al. [25] Gménez et al. [9] Mult-stream HMM FATF wth setmedans wndowed Bernoull HMMs ot mentoned Mult- Classfers Set a-d Set e yes Isolated characters Set a-d Set e yes Set e ot mentoned Proposed System DB Set a-d Set e yes As t can be noted from Tables 1 and 2, the proposed system s the frst attempt to experment wth the IF/EIT database wth the DB. Most of the prevous results on the IF/EIT database are based on HMM. Tables 1 and 2 also show the tranng and test sets used by the other researchers. The authors gven n Table 1 had traned ther systems on sets a-c and tested on set d, and those gven n Table 2 had traned ther systems on sets a-d and tested on set e Dscusson The developped system has some error rate lke the other proposed approach n ths feld. In fact, due to the cursve nature of Arabc handwrtten as scrpt and ts varablty, we evoke the followng reasons. The frst one s related to the hgh varablty of Arabc handwrtten word caused by many factors such as the varatons of shapes come from the human wrtng habt, style and condton. In addton, we fnd the cause related to the fuson of dacrtcal marks, wrtng 84 nstrument, touchng of word and sub-words. For nstance, f one word contans samples n varous wrtng styles/forms or dfferent words share one smlar shape, t nevtably leads to msclassfcaton. The second one s lnked to the process of preprocessng and word segmentaton as the descenders of a letter and the dacrtcal marks are often not at the exact poston on top or under the man part of the letter. Those errors wll be propagated and lead to an nexact feature extracton due to a wrong word boundary and/or naccurate extracton of topologcal features comng from an over or under segmentaton. To reduce these error and so mprove the performance of the system, we can amelorate the pre-processng step by baselne locaton or slant correcton. Smlarly, each word has a number of characters whch has the same length as of the nter-slce, so that t may partcpate n the error of the recognton rate. Fnally, the nequtable frequence of some words n the data base affects ts correct recognton. In fact, some words has more occurrence represented by a few hundreds of samples. However, others words are reprenseted by three samples (even absent) n the tranng data. 5. Conclusons We have proposed a new approach for the offlne Arabc handwrtten word recognton based on the DHB usng a free segmentaton released by a smoothed vertcal projecton hstogram wth dfferent wdth values. The model s consst of three levels. The frst level represents the layer of the hdden node whch models the character class. The second layer models a frame set representng the sub-characters, and the thrd layer models the observaton nodes. The developed system has been expermented and the results are provded on a subset of the IF/EIT benchmark data base. These results show a sgnfcant mprovement n the recognton rate because of the use of the DHB. Most of the recognton errors of the proposed system can be attrbuted to the segmentaton process error and to the poor qualty of some data samples. In addton, some character shapes are nsuffcently represented n the database. As a consequence, ther models are badly traned. References [1] Al-Hajj R., Lkforman-Sulem L., and Mokbel C., Arabc Handwrtng Recognton usng Baselne Dependant Features and Hdden Markov Modelng, n Proceedngs of the 8 th Internatonal Conference Document Analyss and Recognton, pp , [2] Al-Hajj R., Lkforman-Sulem L., and Mokbel C., Combnng Slanted-Frame Classfers for Improved HMM-Based Arabc Handwrtng Recognton, IEEE Transacton Pattern Analyss Machne Intellgence, vol. 31, no. 7, pp , 2009.

7 1030 The Internatonal Arab Journal of Informaton Technology Vol. 13, o. 6B, 2016 [3] AlKhateeb J., Ren J., Jang J., and Al-Muhtaseb H., Offlne Handwrtten Arabc Cursve Text Recognton usng Hdden Markov Models and Re-Rankng, Pattern Recognton Letters, vol. 32, no. 8, pp , [4] Alkhateeb J., Ren J., Ipson S., and Jang J., Knowledge-Based Baselne Detecton and Optmal Thresholdng for Words Segmentaton n Effcent Pre-Processng of Handwrtten Arabc Text, n Proceedngs of the 5 th Internet. Conf. Informaton Technology: ew Generatons, Las Vegas, pp , [5] Alkhateeb J., Word-Based Handwrtten Arabc Scrpts Recognton usng Dynamc Bayesan etwork, n Proceedngs of the 5 th Internatonal Conference on Informaton Technology, [6] Benouareth A., Ennaj A., and Sellam M., Sem-Contnuous HMMs wth Explct State Duraton for Unconstraned Arabc Word Modelng and Recognton, Pattern Recognton Letters, vol. 29, no. 12, pp , [7] Dreuw P., Jonas S., and ey H., Whte-Space Models for Offlne Arabc Handwrtng Recognton, n Proceedngs of the 19 th Internet. Conference on Pattern Recognton, Tampa, pp. 1-4, [8] Elbaat A., Boubaker H., Kherallah M., Alm A., Ennaj A., and El Abed H., Arabc Handwrtng Recognton usng Restored Stroke Chronology, n Proceedngs of the 10 th Internatonal Conference on Document Analyss and Recognton, Barcelona, pp , [9] Gménez A., Khoury I., Andrés-Ferrer J., and Juan A., Handwrtng Word Recognton usng Wndowed Bernoull HMMs, Pattern Recognton letters, Artcle n Press, vol. 35, pp , [10] Halloul K., Lkforman-Sulem L., and Sgelle M., Réseau Bayésen Dynamque Pour la Reconnassance des Caractères Imprmés Dégradés, avalable at : /2042/13554/A16.pdf?...1, last vsted [11] Hamdan M., El Abed H., Kherallah M., and Alm A., Combnng Multple HMMs usng on- Lne and Off-Lne Features for Off-lne Arabc Handwrtng Recognton, n Proceedngs of the 10 th Internatonal Conference on Document Analyss and Recognton, pp , [12] Jayech K., Trmech., Mahjoub M., and Ben Amara., Dynamc Herarchcal Bayesan etwork for Arabc Handwrtten Word Recognton, n Proceedngs of the 4 th Internatonal Conference on ICT and Accessblty, Hammamet, pp. 1-6, [13] Kessentn Y., Paquet T., and Ben hamadou A., Mult-Scrpt Handwrtng Recognton wth - Streams Low Level Features, n Proceedngs of the 19 th Internatonal Conference on Pattern Recognton, Tampa, pp. 1-4, [14] Khorsheed M., Recognzng Handwrtten Arabc Manuscrpts usng a Sngle Hdden Markov Model, Pattern Recognton Letters, vol. 24, no. 14, pp , [15] Khorsheed M., Off-lne Arabc Character Recognton-a Revew, Pattern Analyss and Applcatons, vol. 5, no. 1, pp , [16] Kundu A., Hnes T., Phllps J., Huyck B., and Van Gulder L., Arabc Handwrtng Recognton usng Varable Duraton HMM, n Proceedngs of the 9 th Internatonal Conference on Document Analyss and Recognton, Parana, pp , [17] Lkforman-Sulem L. and Sgelle M., Recognton of Degraded Characters usng Dynamc Bayesan etworks, Pattern Recognton, vol. 41, no. 10, pp , [18] Mahjoub M., Ghanm., Jayech K., and Ben Amara., Proposton D un Modèle de Réseau Bayésen Dynamque Applqué a La Reconnassance de Mots Arabes Manuscrts, avalable at : /document, last vsted [19] Mârgner V. and El Abed H., Arabc Handwrtng Recognton Competton, n Proceedngs of the 9 th Internatonal Conference on Document Analyss and Recognton, Parana, pp [20] Mârgner V., El Abed H., and Pechwtz M., Offlne handwrtten Arabc Word Recognton usng HMM-A Character Based Approach wthout Explct Segmentaton, avalable at: document, last vsted [21] Masmoud S., Ben Amara., and Amr H., Arabc Handwrtten Words Recognton Based on a Planar Hdden Markov Model, the Internatonal Arab Journal of Informaton Technology, vol. 2, no. 4, pp , [22] Menasr F., Contrbuton à la Reconnassance de L écrture Arabe Manuscrte, Thèse de doctorat, [23] Murphy K., Dynamc Bayesan etworks: Representaton, Inference and Learnng, avalable at: uploads/documents/courses/db-phdthess- LongTutoral-Murphy.pdf, last vsted [24] Pechwtz M. and Maergner V., HMM based Approach for Handwrtten Arabc Word Recognton usng the IF/EIT-Database, n Proceedngs of the 7 th Internatonal Conference on Document Analyss and Recognton, pp , [25] Parvez M. and Mahmoud S., Arabc Handwrtng Recognton usng Structural and

8 Arabc Handwrtten Word Recognton based on Dynamc Bayesan etwork 1031 Syntactc Pattern Attrbutes, Pattern Recognton, vol. 46, pp , Khaoula Jayech s a Phd n the Department of Computer Scence at atonal Engneerng School of Sfax and member of research unt Sage (Advanced System n Electrcal Engneerng), team sgnals, mage and document n the atonal Engneerng School of Sousse, Unversty of Sousse. Her research nterests nclude manly Arabc optcal character recognton, dynamc bayesan network, HMM and data retreval. Her man resarch have been publshed n nternatonal journals and conferences. Mohamed Al Mahjoub s an assocate professor n Sgnal and Image processng at the atonal Engneerng School of Sousse (unversty of Sousse) and member of research unt Sage (Advanced System n Electrcal Engneerng), team sgnals, mage and document. Hs research nterests nclude dynamc bayesan network, computer vson, pattern recognton, HMM and data retreval. He s a member of IEEE and hs man results have been publshed n nternatonal journals and conferences. ajoua Ben Amara receved the MSc, PhD, and HDR degrees n electrcal engneerng, sgnal processng and system analyss, from the atonal School of Engneers of Tuns, Tunsa, n 1985, 1986, 1999, 2004 respectvely. From 1985 to 1989, she was a researcher at the Regonal Insttute of Informatcs Scences and Telecommuncatons, Tuns, Tunsa. In September 1989, she joned the Electrcal Engneerng Department of the atonal School of Engneers of Monastr, Tunsa, as an assstant professor. In 2004, she becomes a senor lecturer at the atonal School of Engneers of Sousse, Tunsa. Her research nterests nclude manly optcal character recognton appled to arabc documents, mage processng, compresson, ancent document processng, bometrc and the use of stochastc models and hybrd approaches n the above domans.