A Novel Fuzzy Classifier Using Fuzzy LVQ to Recognize Online Persian Handwriting

Transcription

1 A Novel Fuzzy Classfer Usng Fuzzy LVQ to Recognze Onlne Persan Handwrtng M. Soleyan Baghshah S. Bagher Shourak S. Kasae Departent of Coputer Engneerng, Sharf Unversty of Technology, Tehran, Iran Abstract Fuzzy logc s a powerful tool to represent precse and rregular patterns. Ths paper presents a novel fuzzy approach for recognzng onlne Persan (Fars) handwrtng. In ths approach, a fuzzy classfer s ntroduced that uses a cobnaton of the fuzzy LVQ learnng odel and the expert knowledge. Ths ethod apples an FLVQ network to dstngush between the slar tokens that appear at the end of the strokes. For other tokens, fuzzy lngustc ters are used to descrbe ther features. The purposed ethod was run on a database of Persan solated handwrtten characters and acheved a hgh recognton rate copared to other avalable approaches. Keywords: Persan Handwrtng, Fuzzy Rule-Based, FLVQ, Recognton.. Introducton The Persan character set (along wth slar character sets) are used by ore than 30% of the world's populaton and serve n the wrtng of any wdespread languages such as Arabc and Aurdo []. In contrast to the advances n the onlne Latn and Chnese handwrtng recognton, relatvely few studes have been devoted to the Persan handwrtng recognton. Ths s due, n part, to the cursve nature of the task. In the last two decades a nuber of onlne handwrtng recognton systes have been developed. Most of the eploy stochastc ethods (lke hdden Markov odels), connectve learnng-based ethods (lke neural networks), odel atchng, and structural/syntactcal technques. The ajor drawback of all prevous ethods s ther hgh-dependablty on sall wrtng perturbatons that tends to precse recognton results when workng wth very vast varety of wrtng styles. Consderng the ltaton and drawbacks of the exstng approaches, we defne two ajor characterstcs that a handwrtng recognton syste should possess: fast response and flexblty. To acheve these requreents, the knowledge base ust be sall but robust [2]. Changes n style or orentaton should be handled by the flexble prototypes that contan wdely vald descrptons of character nforaton. In ths paper, n order to overcoe the coplextes of Persan handwrtng and to acheve the above requreents, a practcal fuzzy approach s proposed. In ths approach, the onlne stroke s frst preprocessed and segented nto tokens and the features of each token are extracted. A novel fuzzy classfer s then used to recognze the characters. In ths classfer, the end token of the strokes are classfed by usng the fuzzy learnng vector quantzaton (FLVQ) algorth and the other tokens are characterzed by usng fuzzy lngustc ters that descrbe sple representatve features. The knowledge base of ths classfer s n the for of fuzzy rules that are extracted by an expert. The rest of ths paper s organzed as follows: Secton 2 presents a bref ntroducton to Persan handwrtng and prevous works. Secton 3 explans the preprocessng and segentaton processes. Secton 4 states the feature extracton process. In secton 5 a novel fuzzy classfer s ntroduced and the recognton algorth s presented. The experental results are gven n Secton 6, and the last secton presents the conclusons and future works. 2. Persan Handwrtng and Prevous Works Persan characters are n cursve scrpt. It coprses 32 an characters and s wrtten fro rght to left. Characters have up to four dfferent fors, dependng on ther poston wthn a word (the begnnng, ddle, end and solated fors). Fgure presents saples of dfferent characters and ther fors. Also, the characters ay be wrtten n dfferent fors based on personal handwrtng styles. Persan characters have one an stroke and ost of the have

2 also one to three secondary strokes. Usually the an stroke s wrtten frst and then secondary strokes are wrtten. In Fgure 2, strokes nuber, 3, and 6 are the an strokes. The secondary strokes ay be n the for of dots, "sarkesh", and "daste". There are soe works devoted to recognton of onlne Persan/Arabc handwrtng characters. Al- Ea used a structural analyss ethod for selectng features of Arabc characters and a decson tree for the classfcaton [3]. Al ntroduced a Neuro-Fuzzy approach n [4] and a teplate atchng and dynac prograng approach n [5]. In [6] cobnaton of pruned Kohonen aps and n [7] cobnaton of SOM and Perceptron s used to recognze onlne Arabc handwrtten characters. A Persan handwrtng recognton syste s also ntroduced n [8] whch s based on representng nput data and character patterns usng lngustc ters and coparsons of these ters. Recently we ntroduced a novel fuzzy approach n [9] that uses a fuzzy rule-based approach, where the rules are fored only by an expert. In ths work, we have extended that approach to use the FLVQ learnng odel to prove the recognton results. Snce none of the above approaches were appled on the sae data set, and any of the were not tested on ndependent and extensve test sets, t s not a far atch to copare how these approaches dd aganst each other. flterng steps. Soothng averages the coordnates of a pont usng ts neghborng pxels, and the flterng reduces the nuber of ponts by elnatng very close ponts. Ths flterng technque forces a nu dstance between consecutve ponts. Stroke7 Stroke6 Stroke5 Stroke3 Stroke4 Stroke2 Stroke Fgure 2. Soe saple strokes. After preprocessng our nternal segentaton ethod s appled. It as at separatng each stroke nto a set of sall parts called tokens. In Fgure 4, each star ark shows the end pont of a token. These tokens can be lnes, arcs or loops. Soe features are used to descrbe the propertes of each token. To start the task, we frst convert the lst of (x, y) coordnates of nput ponts nto a set of vectors, each startng fro one of the ponts and endng at the next pont. Fgure 3 shows vectors that lnk the nput ponts. P5 V3 V2 P4 P2 V V0 P P0 Fgure 3. Vectors between ponts. The angle between two consecutve vectors V and V + s noted as A. The sgn of A s consdered as the curvature sde at pont P. For a sequence of ponts, the total curvature sde s the sgn of the su of angles along the sequence. Fgure. Saple of Persan prnted characters and ther fors n dfferent postons wthn the word. 3. Preprocessng and Segentaton In ths secton, we present the proposed preprocessng and segentaton ethods. The nput data are acqured n the for of a lst of (x, y) coordnates. Frst, the nput data are externally separated nto strokes by the help of pen-ups. In Fgure 2, soe saple strokes are shown. Then, the nose reducton process s appled through soothng and Input : V.. V A = V V + + n V, ( V + ( V + ) <= 80 ) >= 80 otherwse () At ths stage, any pont n whch A exceeds a certan threshold, T, can be a canddate for the end of token. Also, when A exceeds another threshold T 2 (less than T ) and curvature sde at pont P s opposed to the total curvature sde, the P s a segentaton canddate. So, we save ths pont and start the process fro the next pont. To fnalze the segentaton task, a loop detecton

3 ethod s used to fnd the loops and deletes addtonal canddate segentaton ponts that locate on the. It also defnes new segentaton ponts. Two procedures are consdered for ths purpose; one that detects closed loops and the other that detects open loops that appear at the start of the strokes. To detect closed loops, the trajectory of the pen oveent s saved. If ths trajectory ntersects tself, and the ponts between the prevous and current vst of the pont construct a crcle lke curve, a closed loop s detected and two new segentaton ponts add at the start and end of ths loop. Open loops are always located at the start of the stroke. To fnd the, we start fro the begnnng of the stroke and for each pont of t copute the dstance between ths pont and the start pont of that stroke and save these values n an array. The frst local na n ths array, f exsts any, s selected. If ths value s sall relatve to the length of the curve up to correspondng pont, and the curve s crcle lke, ths pont s consdered as the end of the open loop. Otherwse, we contnue the process fro the next pont. As descrbed above, n ths work n contrast to the other works [2, 8, 9], a loop detecton process s used to coplete the segentaton process and to ake the resultng tokens ore eanngful. Fgure 4 shows soe segentaton saples. Fgure 4. Saples of segentaton. The end pont of each token s shown wth a ark. 4. Feature Extracton After the nput data s segented, each token s descrbed usng a set of features. These are defned below. ) Start2End_Drecton: The drecton of the straght lne that starts fro the frst pont of the token and end at the last pont. Φ = tan (( Yend Ystatrt ) /( X end X start )) (2) 2) Start2COG_Drecton: The drecton of the straght lne that starts fro the frst pont of the token and ends at the center of gravty of the token. 3) End2COG_Drecton: The drecton of the straght lne that starts fro the last pont of the token and ends at the center of gravty of the token. 4) Straghtness: A new straghtness easure that perfors uch better than the easure ntroduced n [0, ]. Ths easure s the angle between the Start2COG_Drecton and End2COG_Drecton. Value of ths easure for a drect lne s 80 and for a crcle s zero. 5) Horzontal_Moton: The relatve horzontal oton n the wrtten token profle: xax xn HM = (3) N x = + x 6) Vertcal_Moton: The relatve vertcal oton n the wrtten token profle: yax yn VM = (4) N y = + y 7) Curvature_Sde: The drecton of concavty coputed by: Curvature Sde = Sgn( N = A ) (5) where A s defned n (). If ths value s negatve, the curvature sde s assued to be clockwse, and otherwse t s counter clockwse. These two ters are referred to as CWC and CCWC through the paper. 7) Aspect Rato: Is coputed by dvdng the vertcal sze of the token by ts horzontal sze as: y ax y n Aspect Rato = (6) x x ax n 5. Fuzzy Classfer Usng FLVQ The next step after the feature extracton process s to create a classfer. Ths classfer ust contan one or several rules for each character to dentfy the an stroke of t. In ths secton we frst explan our fuzzy classfer and then the recognton algorth s ntroduced to classfy the nputs. 5.. Fuzzy Classfer Foraton In ths secton, we splt the tokens nto two groups: The end tokens and the non end tokens. The tokens that appear at the end of the strokes are end tokens and the other tokens are non end tokens. If a stroke contans only one token, t s trval that ths token s an end token. Snce the classfcaton of end tokens s a cubersoe task (as the expert has not already found

4 approprate set of rules), n ths paper, we use the FLVQ learnng odel to autoatcally classfy the end tokens. The Non end tokens are characterzed by the expert and for descrbng the, the expert uses lngustc ters. In ths subsecton, we frst ntroduce the FLVQ algorth and then we descrbe the proposed rule-based creaton ethod. 5.. FLVQ Algorth Here, we descrbe the FLVQ algorth ntroduced n U = µ k k = N, = c be the fuzzy X = xk k = N, [2]. Let { } c-partton of tranng patterns { } and V = { = c} be the neuron's paraetrc vectors. Frst, assue that the nuber of copetng neurons c s equal to the nuber of pattern classes. The goal of the FLVQ algorth s to nze the below objectve functon: Q N ( U, V ) = µ c k = = Subjected to the constrants: c = µ k = ; k and µ k [ t ] D (7) k 2 k k [0,]; k, (8) Where Dk = xk s the dstance between neuron and tranng pattern k, s a fuzzness paraeter greater than, and t k {0, } s the target class ebershp value of neuron for nput pattern k. The FLVQ learnng law s: ( t + ) = ( t) + α( t)[ t k µ k ][ xk ( t)]; And the ebershp updatng rule s: = c µ k l= D ( D k kl ) (9) (0) The extenson of above entoned learnng technque s llustrated n Fgure 5. It can be seen that by-passng the MIN layer results n the algorth descrbed above. Recall that the nuber of copetng neurons has been assued to be equal to the nuber of pattern classes. Such assupton can be relaxed by ntroducng the MIN layer to handle ultple neurons per pattern class network desgn. Consequently, the neuron underutlzaton proble n LVQ s resolved. Correspondng to the general network archtecture, the learnng algorth s odfed as follows. Frst, the dstance coputaton s redefned as: D k = n x j S k j () Where S s the ndex set of the copetng neurons for pattern class I, and j s jth copetng neuron's paraetrc vector for pattern class. Thus, the FLVQ learnng law wll be: ( t + ) = ( t) + α( t) I j [ tk µ k ][ xk Where: f xk j I j = 0 otherwse ( t)]; l S (2) k l Fgure 5.Typcal FLVQ network [2]. x (3) Here, we use ths FLVQ network to classfy the end tokens. The nput pattern to ths network s a vector of features ntroduced n Secton 4. The nuber of clusters s fxed to 0 (nuber of dfferent types of end tokens). Fgure 6 shows the ebers of dfferent clusters. By usng ths network, we fnd ebershps of the nput to the clusters. The dentfers of these clusters are used n the fnal rules Fuzzy Rule-Based Creaton The knowledge base used n ths paper, s n the for of fuzzy rules whch found by an expert. To overcoe the probles exstng n ult-wrter envronents, the defned prototypes ust antan the syntax of the rules as short as possble to descrbe a ulttude of character styles and sultaneously to process enough seantc nforaton to dstngush the sybols easly

5 []. To defne a rule, at frst, soe tokens are chosen to descrbe the an stroke of the character. The end token of the stroke s dentfed only by the dentfer of the related cluster n the FLVQ network. For non end tokens, soe features are selected by the expert to descrbe the. The Expert uses Straghtness, End2COG_Drecton, and Curvature_Sde features to descrbe crcle-lke curves and uses Straghtness, Start2End_Drecton, and Curvature_Sde features, for other types of curves. Then, for each feature, an approprate lngustc ter s chosen. The ebershps of soe of these lngustc ters are shown n Fgures 7 and 8. The lngustc ters n Fgure 8 are the abbrevatons of the eght drectons: East, North East, North, North West, West, South West, South, South East, and East. ١ ٢ ٣ ٤ ٥ ٦ ٧ ٨ ٩.0 E 0 NE 45 N NW W SW S SE E Fgure 8. Fuzzy sets of the Drecton varables. Soetes ore than one lngustc ter s needed to descrbe a feature of a non end token or several ways are exst to segent a stroke nto tokens. In these cases ore than one rule s used. Table presents three saple rules whch are used to defne three characters shown n Fgure 4. Each row of ths table s related to one of the tokens of the character. Table. Saples of rule defnton. Cluster Straghtness End2COG Start2COG Curvature Drecton Drecton Sde 'Seen' Secrcle W CWC Secrcle W CWC 'Eyn' Secrcle S CCWC 'Sad' Crcle NE Arc W Ф ١٠ 5.2. Proposed Recognton Algorth Fgure 6. The ebers of dfferent clusters. The end pont of each non end token s shown wth a ark. For the characters that have ore than one token, the end token s consdered. Crcle SeCrcle Arc Straghtness Fgure 7. Fuzzy sets of the type varable. In Secton 5., we descrbed the rule-based foraton. Ths subsecton presents the purposed classfcaton algorth that deternes the correspondng class usng the extracted tokens. The aount of slarty between the nput token and a token of a rule s coputed as follows. If a rule token has a cluster nuber, we use FLVQ network and fnd the ebershp of the nput token to the correspondng cluster usng (0); If ths value s less than 0., we replace t by 0. Otherwse, we consder the fuzzy sets correspondng to the lngustc ters n the rule and fnd the nu of the ebershp values of the nput token features to the related fuzzy sets. Snce there ght be extra short tokens n the nput (whch do not atch wth any tokens), to copute the

6 slarty between an nput sequence of tokens wth a rule, we fnd, f possble, the nput tokens that are ost slar to the rule tokens. The other of these tokens ust also be correspondng to the order of the rule tokens. If ths task s not possble, the total slarty value s set to zero, otherwse t s set to the nu of the slarty of the acheved nput tokens to the correspondng rule tokens. Fnally, the total slarty value s consdered as a postve easure (PM) and the su of the relatve lengths of the unused nput tokens s consdered as a negatve easure (NM). To recognze the class of the nput, we fnd the rule that axzes ths equaton: O = PM α NM Where α s a constant coeffcent. 6. Experental Results (4) In ths work, we use the relatvely coplete database ntroduced n [3] that contans the solated characters wrtten by 28 persons. As there s no general bench ark for onlne handwrtng recognton algorth n Persan, we could not copare our result wth other works quanttatvely. The recognton rate of the purposed ethod on ths database s near 88% and proves to about 95% when tunng the paraeters for a query wrter. It ust be noted that the perforance of the proposed algorth are ore accurate when copared wth the ost recent results reported n [8, 9]. In addton, the coputatonal cost of our approach s also uch less than that of the approach ntroduced n [8]; and thus can be used n onlne envronents. The nuber of the rules used n ths test s Concluson and Future Drectons There are very few avalable approaches for recognton of onlne Persan handwrtng. In ths paper, a novel ethod whch s based on representaton of nput tokens wth very sple features, usng cobnaton of a fuzzy rule-based and the FLVQ network s presented and a fuzzy nference s also ntroduced. As opposed to other avalable ethods, an portant advantage of the purposed ethod s ts ablty to segent the strokes nto eanngful tokens. As presented n Secton 6, ths approach has been qute successful n acceptng a wde range of varatons for each letter and has shown prosng results. To follow ths research, we are now workng on recognton of the secondary strokes, to further prove the recognton results. Acknowledgeent Ths work was n part supported by a grant fro ITRC. References [] I. S. I. Abuhaba, M. J. J. Holt, S. Datta, Recognton of Off-Lne Cursve Handwrtng, Coputer Vson and Iage Processng, Vol. 7, No., pp. 9-38, 998. [2] A. Malavya, R. Klette, "A Fuzzy Syntactc Method for On-lne Handwrtng Recognton", Lecture notes n Coputer Scence 2, Sprnger, Advances n Structural and Syntactcal Pattern recognton, SSPR'96, pp , 996. [3] Al-Ea, S. and Usher, M. On-Lne Recognton of Handwrtten Arabc Characters, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 2, No. 7, July 990. pp [4] A. M. Al, A Neuro-Fuzzy Approach to Recognze Arabc Handwrtten Characters, IEEE Internatonal Conference on Neural Network, vol. 3, pp , 997. [5] A. Al, O. Ghorbel, The Analyss of Error n an On- Lne Recognton Syste of Arabc Handwrtten Characters, Proceedngs of ICDAR 995, pp , 4-6 August 995, Montreal, Canada. [6] N. Mezghan, M. Cheret, "Cobnaton of Pruned Kohenon Maps for On-lne Arabc Character Recognton", Proceedngs of the Seventh Internatonal Conf. on Docuent Analyss and Recognton (ICDAR'03), pp , Ednburgh, Scotland, August [7] T. Klassen, "Towards Neural Network Recognton of Handwrtten Arabc Letters", MS. Thess, Dalhouse Unversty, Halfax, Nova Scota, 200. [8] R. Halavat, S. B. Sourak, M. Soleyan, Persan Onlne Handwrtng Recognton Usng Fuzzy Modelng, to be publshed n IFSA 05, Bejng, Chna, July [9] M. Soleyan Baghshah, S. Bagher Sourak, S. Kasae, A Novel Fuzzy Approach to Recognton of Onlne Persan Handwrtng, to be publshed n ISDA 05, Wroclaw, Poland, Septeber [0] Roesh Ranawana, Vasle Palade, G.E.M.D.C. Bandara, "An Effcent Fuzzy Method for Handwrtten Character Recognton", Proceedngs of KES2004, pp , Wellngton, New Zealand, Septeber [] A. Malavya, L. Peters, R. Caposano, "A Fuzzy Onlne Handwrtng Recognton Syste : FOHRES", Second nternatonal conference on Fuzzy Theory and Technology, Durha, NC, Oct.3-6, 993. [2] F. L. Chung, T. Lee, "A Fuzzy Learnng Model for Mebershp Functon Estaton and Pattern Classfcaton", IEEE Internatonal Conference on Fuzzy Systes, :426-43, 994. [3] S. M. Razav, E. Kabr, "A Data base for Onlne Persan Handwrtten recognton", 6 th Conference on Intellgent Systes, Keran, Iran, 2004.