APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION

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1 ISSN: (ONLINE) DOI: 0.297/jvp ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, NOVEMBER 205, VOLUME: 06, ISSUE: 02 APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION Khuat Thanh Tung and Le Th My Hanh 2 DATIC Laboratory, The Unversty of Danang, Unversty of Scence and Technology, Vetnam E-mal: thanhtung09t2@gmal.com, 2 ltmhanh@dut.udn.vn Abstract Optcal Character Recognton plays an mportant role n data storage and data mnng when the number of documents stored as mages s ncreasng. It s expected to fnd the ways to convert mages of typewrtten or prnted text nto machne-encoded text effectvely n order to support for the process of nformaton handlng effectvely. In ths paper, therefore, the technques whch are beng used to convert mage nto edtable text n the computer such as prncpal component analyss, multlayer perceptron network, self-organzng maps, and mproved multlayer neural network usng prncpal component analyss are expermented. The obtaned results ndcated the effectveness and feasblty of the proposed methods. Keywords: Optcal Character Recognton, Prncpal Component Analyss, Multlayer Perceptron, Self-Organzng Maps. INTRODUCTION The computer has become the frst choce n terms of data storage because of ts enormous utltes such as easng to manage, search, edt, and store. Nevertheless, papers are stll the rreplaceable materals used to storage the documents due to ts necessty n lfe such as books, newspapers, magaznes, book notes, contracts, etc. As a result, people want to put the documents stored on paper n partcular and other materals n general to the computer by typng the content for computer drectly n order to explot them effectvely. For the small number of documents, ths work can be acheved. For the huge volume of documents, however, ths s a dffcult problem because t wll take a lot of tme. Optcal Character Recognton (OCR) s the process of extractng characters from the mage to create the edtable and searchable documents. OCR s a branch of mage processng. Though ths s stll new feld compared to many other areas of scence, t quckly acheved sgnfcant progress. Based on the actual demand s to put the documents whch are stored on paper nto the computer wthout typng, many OCR programs were released and wdely appled n the felds related to recognton. Wth the development of recent studes, the accuracy rate of recognton result can reach to around 99% [] for mage clarty and regular typeface. Nonetheless, for poor qualty mages, specal typeface, handwrtng, or Vetnamese documents, the exact percentage s not hgh. Therefore, n addton to OCR there are some attractve research felds such as Intellgent Character Recognton [2], Optcal Mark Recognton [3], Magnetc Ink Character Recognton [4], and Barcode Recognton [5]. In ths paper, we wll study the ways to detect text n a photo or scanned mage. Several machne learnng algorthms such as prncpal component analyss (PCA), Mult-layer Perceptron (MLP), Kohonen's Self-organzng Feature Maps, and the combnaton of MLP wth PCA are employed to recognze the optcal character recognton. The focus of ths paper s to study the process of detect text n the mage, preprocessng and verfy the effectveness of four proposed approaches for optcal character recognton wth respect to the varous examned cases. Some Open source or commercally avalable engnes such as FreeOCR tool can only detect text n an mage contanng only paper and text. They cannot recognze text n the mage wth the complex background ncludng table surfaces, floors, etc. Wth regard to mages that have varous tlts, text s also not detected and recognzed. In our work, we wll enhance some preprocessng technques to handle these drawbacks and text n mages wth complcated background or havng tlts can be recognzed successfully. The rest of paper s further organzed as follows: Secton 2 ntroduces preprocessng and the methods to detect text n the mage. The way to buld the tranng set s represented n secton 3. Proposed approaches for optcal character recognton are shown n secton 4. Secton 5 s experments and obtaned results. Concluson and future work wll be dscussed n the secton PREPROCESSING AND DETECTING TEXT IN THE IMAGE The process of recognton of paper frame n the mage s a large problem. Therefore, n ths work, we only take nto account mage wth paper frame tltng on a dark background (partal or full text). The process of detectng text and preprocessng for mage s llustrated n Fg. ncludng the followng steps: a) Input mage. b) Dynamc threshold bnarzaton for mage: teraton means defnes the threshold of a pxel wth the grey level values of ts own and neghbor s pxels and the coordnate of the pxel [6]. Grey mage s splt nto the small parts; each part s appled dfferent bnary thresholds. Therefore, the dfference of grey level between the paper frame and background can be recognzed. c) Fnd border surroundng paper frame. d) Employng mask mage to separate the paper frame from the background. e) Background neutralzaton for the mage by usng morphologcal methods such as eroson and dlaton. f) Dvdng mage (d) by mage (e) to remove nose and fnd floatng ponts on the background color. These are characters. 5

2 KHUAT THANH TUNG AND LE THI MY HANH: APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTION AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION g) Image bnarzaton and fndng the frame around the text n the paper frame to reduce tme processng and data for steps later. h) The frame surroundng text allows us knowng the tlt and center of document on the mage. Then, the mage wll be rotated wth obtaned tlt. It can be used Bcubc nterpolaton method to rotate mage. Lowercase letters: a, b, c, d, e, f, g, h,, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z. Uppercase letters: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z. Dgts: 0,, 2, 3, 4, 5, 6, 7, 8, 9 A number of specal characters: ), (, #, %,@. (a) (b) (c) Fg.2. Constructng the tranng character However, the mage drawn by Graphcs class has the large whte background. In order to tran and recognze, tranng mages need to be narrowed background to barely contan tranng character and same sze for all. An example of the tranng set constructon s shown n Fg.2. (d) (e) (f) 4. THE PROPOSED APPROACHES FOR OPTICAL CHARACTER RECOGNITION 4. MULTILAYER PERCEPTRON (g) (h) Fg.. The process of detectng text and preprocessng for the mage However, before carryng out the optcal character recognton step, mage Fg.(h) must be separated nto ndvdual lnes and characters n each lne. Heren, we use the approach of travelng through pxels, so there are some lmtatons n the type of characters whch cohere vertcally or ponts underneath the current lne concde wth the ponts of the next lne. It s dffcult to determne the format of orgnal document. In ths work, the space between words s recognzed usng eroson method for each lne of text wth the sze computed by the followng formula (ths formula s created by tral and error): where, H s the heght of a lne of text. 2* H Sze () 7 3. CONSTRUCTING A TRAINING SET OF OPTICAL CHARACTERS In ths work, we create the lst of tranng set automatcally n order to draw the mage of character based on tranng typeface n opton. Ths work can be done by usng Graphcs class n Java. The tranng database used n ths work s 64 Latn characters wth Aral typeface. The detals of tranng set are represented as follows: Artfcal neural networks [7] are a famly of statstcal learnng models nspred by bologcal neural networks and are employed n many felds such as medcne, economcs, recognton systems, etc. The calculaton of the output value s the sum of products of nputs and weghts and then usng ths value for actvaton functon. The actvaton functon conssts of some features such as lower and upper bounded, beng monotone, contnuty, and smoothness. In ths paper, actvaton functon s the sgmod functon gven by, Image Data Input Layer 50 neurons e Hdden Layer 250 neurons Output Layer 6 neurons Fg.3. Multlayer perceptron dagram Uncode code (2) 6

3 Consder a sngle layer of N 0 artfcal neuron unts, wth each unt fully connected to each of the N common nputs (plus the bas nput unt). Ths network provdes mappngs of the nput patterns to mult-dmensonal output vectors y = (y,., y M) and s mentoned as a sngle-layer perceptron network. The Mult- Layer Perceptron network allows the addtonal extenson to multple layers of unts, wth the outputs of the frst layer of unts becomng the nputs of the next, and so on. Whatever the structure of the network, nformaton flows n one drecton, from the network nputs, forward towards the output layer of unts, and produces the network output assocated wth that nput. For optcal character recognton problem, a 3-layer neural network s desgned: 50 neurons of nput layer correspondng to the sze of bnary mage 0 5, hdden layer wth 250 neurons whch s determned by tral and error method, 6 neurons of output layer correspondng to 6-bts Uncode of tranng character. The Fg.3 llustrates the multlayer neural network desgned for the problem n ths work. In the process of optcal character recognton through mage, because nput bnary mage wll be skewed n pxels when comparson of tranng mage as well as the lmtaton of tranng set, so the output of network s 6 bts Uncode unexpected. Ths leads to the recognton error and confuson among characters. In order to overcome ths problem, nstead of usng a set of lnkng weghts between layers, we wll store each tranng mage wth specfc weghts and employ error coeffcent as a threshold to analyze the features of the mage. The neural network tranng process s to fnd the set of weght values that wll cause the output from the neural network to match the actual target values as closely as possble. A learnng rule s appled n order to mprove the value of the MLP weghts over a tranng set accordng to a specfc algorthm. The algorthm whch s used to tune weghts n ths paper s Backpropagaton. The Backpropagaton learnng algorthm can be dvded nto two phases: propagaton and weght update. Phase : Forward propagaton of a tranng pattern's nput X s = {x, x 2,., x n}, s, N (N s the number of tranng patterns) through the neural network n order to generate the propagaton's output actvatons. - Output of j th neuron n hdden layer y j n - Output of k th neuron n output layer y k w j x (3) m w j jk y j (4) Phase 2: Calculate Mean Squared Error for tranng pattern s: p Es pk 2 yk tk In whch t k s expected value of k th neuron at the output layer. backward propagaton of the error through the neural network to adjust weghts at l th teraton. Devaton of lnkng weghts between hdden layer and output layer: (5) where, where, l l wjk k y j (6) k(l) = (t k y k)( y k)y k (7) Devaton of lnkng weghts between nput layer and hdden layer: j l x w j (8) p j l y j y j k k w jk (9) After updatng weghts, pattern X s contnues to be nput of network at (l + ) th teraton and weghts are updated untl E s < or the number of predefned teratons has been reached. The above process s conducted untl the network learns by heart tranng set. After that, network confguraton s stored to use later. 4.2 SELF-ORGANIZING MAPS Kohonen neural network [8] s a typcal network n selforganzng map model (SOM) based on compettve unsupervsed learnng. Ths one s the modelng of the behavor of the human bran. The dfferent knds of neural networks are usually only concerned wth the value and nput of the network but not exploted the structural relatonshp n the neghborhood of the sample data or entre sample space. However, Kohonen network takes nto account to these factors. The man dea of ths algorthm s to map topologcal features of self-organzng to preserve the orderly arrangement of the sample n multdmensonal space nto new space wth dmensonalty less, usually two dmensons. Ths s a non-lnear projecton provdng a mappng feature two-way, whch s appled to detect and analyze the characterstcs of the nput space. The output from the Kohonen neural network does not consst of the output of several neurons. When a pattern s represented to a Kohonen network, one of the output neurons s chosen as a wnner. Ths wnnng neuron s the output from the Kohonen network. Often these wnnng neurons show groups n the data that s presented to the Kohonen network. Wnner Neuron Input Layer 50 neurons Output Layer 64 neurons Fg.4. Self-organzng neural network model 7

4 KHUAT THANH TUNG AND LE THI MY HANH: APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTION AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION For optcal character recognton problem, we construct the network model wth 50 neurons for nput layer correspondng to the sze of bnary mage 0 5. The tranng process wll classfy the nput data set randomly, and each character n tranng set corresponds to each of the specfc output layer neurons. Tranng set for each character s only one mage, so the number of output neurons s equal to the sze of tranng set (64 neurons). The Fg.4 s a model of the Kohonen neural network employed n ths work. Kohonen Learnng Algorthm [8] s presented as follows: Step : Intalzaton. Choose the dfferent random values for ntal weght vectors w j = [w j, w j2,, w jm], j =, 2,, L, where L s the total number of neurons n the lattce, M s the dmenson of the nput space. Step 2: Samplng. Draw a sample x from nput space wth a certan probablty; the vector x presents the actvaton pattern that s appled to the lattce. The dmenson of x s equal to M. Step 3: Smlarty Matchng. Fnd the wnnng neuron at a tme step n by usng mnmum dstance Eucldan crteron: x arg mn x w j L (0) j m, Step 4: Updatng. Tunng the synaptc weght vectors of all neurons by employng the update formula: w j(n + ) = w j(n) + (n)h j,(x)(n)[x(n) w j(n)] () where, (n) s the learnng rate parameter, and h j,(x)(n) s the neghborhood functon centered around the wnnng neuron (x); both η(n) and h j,(x)(n) fluctuated dynamcally durng learnng for best results. Step 5: Contnue wth step 2 untl no notceable changes n the feature map are observed. 4.3 PRINCIPAL COMPONENT ANALYSIS PCA s a mathematcal procedure that employs transformaton to convert a set of observatons of possbly correlated features nto a set of values of uncorrelated features called prncpal components [9]. PCA s a well-known technque for extractng representatve features for character recognton and s used to reduce the extent of the data wth mnmal loss of nformaton. In ths algorthm, the entre dataset (d dmensons) s projected onto a new subspace (k dmensons where k < d). Ths method of projecton s useful n order to reduce the computatonal costs and the error of parameter estmaton. The new subspace s composed of k unt vector wth dmenson N. Each ths vector s called as an egenvector. Let M s the number of nput mages, each mage s converted to vector of dmenson N (the mage area), and we obtan the set of nputs X = {x, x 2,, x M}{x R N }. Then, nput vectors are translated to the center of tranng set A = [, 2,, M]. The formula determnng ths center s represented as follows: x M M tb x (2) = x x tb (3) The covarance matrx of A s C = A.A T has egenvalues sorted by descendng > 2 > m and correspondng egenvectors u, u 2,, u m. Each u s an egenvector. A set of orgnal vectors s presented n the space created by n egenvectors as follows: x x tb wu w2u2 wnun w u (4) Selectng K egenvectors u and correspondng K maxmal egenvalues λ, we have: x x tb where, K << N wu w2u2 wkuk w u (5) The vector of coeffcents [w, w 2,, w k] s the new representaton of mage created n the space PCA. It can be seen that the number of dmensons of nput data s reduced, but most of the basc features are retaned based on egenvectors correspondng to large egenvalues. K egenvalues n M values are chosen based on the followng formula [0]: K e.g. s 0.9or 0.95 N N K (6) As a result, each mage n the tranng set can be presented as a lnear combnaton of K maxmal egenvectors T w u j, j,2... K. 4.4 USING PRINCIPAL COMPONENT ANALYSIS FOR MULTI-LAYER PERCEPTRON In the process of prncpal component analyss as mentoned above, let s the th tranng mage after translatng to the center of tranng set. Ths mage s represented n new space as follows: T u T u2 T u 3 T u K We apply PCA to extract the features of the mage and create the vectors of the tranng mage, and then use ths vector of features for the nput layer of MLP network. PCA s employed to extract the features from mage and then these features are vectors of nput layer to enhance the qualty of MLP. The vectors of tranng mages presented n PCA s space n the dmensonalty less than orgnal ones must be parttoned on determnng doman [-, ] to sgnfcantly reduce the coeffcent of error n the tranng process and ncrease the effectveness of Backpropagaton algorthm. Only the drecton of these vectors s consdered and magntude s gnored (magntude of the vector s, or called the unt vector). The transformaton of a vector to unt vector s also known as vector normalzaton and usng the formula as follows: (7) Vectors n tranng set normalzed have become the nput vectors for MLP network. The dmensonalty of the tranng set whch s reduced by usng PCA results n the decrease of the 8

5 number of nput neurons K << 50. The dentfcaton number of neurons n the hdden layer s very mportant because the number of neurons of nput layer s not fxed. Determnng the number of hdden layers wthout affectng to tranng tme and ensurng the suffcency of nformaton of tranng set are carred out by the followng formula []: where, N O s the number of neurons of output layer N I s the number of neurons of nput layer N H s the number of neurons of hdden layer N 5. EXPERIMENTATION H 2 NO NI (8) 3 5. THE OBJECTIVE OF THE EXPERIMENTS Usng mage acquston devces to scan, photograph the document and put the mages nto the computer to handle cannot avod the nose or tlt of paper due to Impact from outsde. Therefore, n ths experment, the effectveness of 4 proposed approaches whch are PCA, MLP, SOM, and MLP usng PCA s evaluated on standard nput mage and mage wth dfferent tltng. 5.2 EXPERIMENT FOR THE CASE OF STANDARD INPUT In ths experment, standard nput mage wth no tlt and no nose s employed to recognze the optcal characters. Fg.5 s an example for a standard nput mage and Fg.6 s the obtaned results of experments. Some nputs: The number of characters n the emprcal mage s 253 ncludng lowercase letters, bold letters, and specal characters. Tltng ~ 0 degrees. The average heght of each lne s 45 px. The tranng set contans 64 Latn characters wth Aral typeface. Fg.5. Standard nput mage The Table. shows the obtaned results of 4 approaches for the standard nput mage. The above results have not yet been performed post-processng step to repar the errors such as confused between characters whch have smlar shapes or between lowercase and uppercase letters. Table.. The obtaned result for recognton n the case of standard nput Sl. No. Approach No. Wrong Characters Error Rate (%) SOM MLP PCA PCA-MLP pca - Results for SOM: - Results for MLP: prncpal component analyss (pca) s a statstc6l procedure that uses an ohogonal transformaton to conv6r a s6t of obseratons of possbly corr6lated varablos nto a sot of values of lnearly uncorrelated varables called prncpal components, thc number of prncpal components fs foss than or equat to tho number of orfgfnal varables, ths transformaton fs d6fn6d n such a way that the frst prncpal compon6nt has the lauest possble varanco (that s, accounts for as much of the varabtty fn th6 data as possble), and each succeedng componont n turn has the hghest varfance possble undor the oonstrant that t s orhogonaf to (,e,, uncorrefated wth) the precedng components. the prncfpal components are ohhogonaf because they are the egenvectors of the covaranco matrx, whch s symmetrc, pca fs senstve to the relatve scalng ofthe orgnal varables. pca s tho smpfost of the true egenvectofbased multvarato analyses. cxcn, jts operatfon can be thought of as revealng the fnternal structure of the data jn a way that best explans the varance n the data, lf a mujtvarate datasot s vfsualsed as a set of coordnates n a hgh.dmensonal data space ( axs pcr varable), pca can supply th6 user wth a lower.dmensonal pcture, a pr(6cton or..shadow.. of ths ohect when vfewed from ts (n some sense, soo below) most nhrmatvo vowpont, ths s done by usng only the frst fow prncpalcompononts so that the dmonsonalty of tho transhfmod data js foduced. lcl lrllclpll colpolelt llllllll (lcl) s a statstcll procedure that uses an ohogonal transformaton to conver a set of obseratons of possbly correlated varables nto a set of values of lnearly uncorrelated varables called lrllcllll colloleltl, the number of prncpal components s less than or egual to the number of orgnal varables, ths transformlton s defned n such a way that the frst prncpal component hle the lalest possble varance (that s, accounts for as much of the varablty n the data as possble), and each succeedng component n turn hls the hghest varance possble under the constrant that t s olhogonal to (,e,, uncorrelated wth) the precedlng components. the prncpal components lre ohhogonll because they are the egenvectors of the covarance matrx, whch s symmetrc, pca s senstve to the relatve scllng ofthe orgnal varlblee. pca s the smplest of the true egenvectofbased multvarlte analvses. cxen, ts operaton can be thought of as revealng the nternal structure of the data n a wav that best explans the varance n the data, lf a multvarate dataset s vsualsed as a set of cocrdnates n a hgh.dmensonal data space ( axs per varable), pca can supply the user wth a lower.dmensonal pcture, a prhecton or..shadow.. of ths ohect when vewed from ts (n some sense, see below) most nhrmatve vewpont, ths s done bv usng onlv the frst few prncpal components so that the dmensonalty of the transhrmed data s reduced. 9

6 KHUAT THANH TUNG AND LE THI MY HANH: APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTION AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION - Results for PCA: - Results for PCA-MLP: pca prncpal component analyss (pca) s a statstcal procedure that uses an orthogonal transformaton to convert a set of observatons of possbly correlated varables nto a set of values of lnearly uncorrelated varables called prncpal components, the number of prncpal components s less than or equal to the number of orgnal varables, ths transformaton s defned n such a way that the frst prncpal component has the largest possble varance (that s, accounts for as much of the varablty n the data as possble), and each succeedng component n turn has the hghest varance possble under the constrant that t s orthogonal to (,e,, uncorrelated wth) the precedng components. the prncpal components are orthogonal because they are the egenvectors of the covarance matrx, whch s symmetrc, pca s senstve to the relatve scalng ofthe orgnal varables. pca s the smplest of the true egenvector-based multvarate analyses. often, ts operaton can be thought of as revealng the nternal structure of the data n a way that best explans the varance n the data, lf a multvarate dataset s vsualsed as a set of coordnates n a hgh.dmensonal data space ( axs per varable), pca can supply the user wth a lower.dmensonal pcture, a projecton or..shadow.. of ths object when vewed from ts (n some sense, see below) most nformatve vewpont, ths s done by usng only the frst few prncpal components so that the dmensonalty of the transformed data s reduced. pca prncpal component analyss (pca) s a statstcal procedure that uses an o4hogonal transformaton to conve4 a set of obsewatons of possbly correlated varables nto a set of values of lnearly uncorrelated varables called prncpal components, the number of prncpal components s less than or equal to the number of orgnal varables, ths transformaton s defned n such a way that the frst prncpal component has the lamest possble varance (that s, accounts for as much of the varablty n the data as possble), and each succeedng component n turn has the hghest varance possble under the constrant that t s okhogonal to (,e,, uncorrelated wth) the precedng components. the prncpal components are o4hogonal because they are the egenvectors of the covarance matrx, whch s symmetrc, pca s senstve to the relatve scalng of the orgnal varables. pca s the smplest of the true egenvectofbased multvarate analyses. o#en, ts operaton can be thought of as revealng the nternal structure of the data n a way that best explans the varance n the data, lf a multvarate dataset s vsualsed as a set of coordnates n a hgh.dmensonal data space ( axs per varable), pca can supply the user wth a lower.dmensonal pcture, a pr(ecton or..shadow.. of ths onect when vewed from ts (n some sense, see below) most nhrmatve vewpont, ths s done by usng only the frst few prncpal components so that the dmensonalty of the transhrmed data s reduced. Fg.6. The recognton results of the algorthms 5.3 OTHER TEST CASE In order to acheve more objectve assessment, ths secton wll examne the results of four proposed methods wth respect to nput mages that have varous tlts and the partal mage of page. The emprcal results are evaluated by observng and beng statstc for the error rate. The Fg.7 s the cases of an nput mage and the emprcal results are presented n Table.2. The Fg.8 s the case of an mage wth a lumnous paper frame. In ths case, lumnous areas wll create connected regons to other external detals of the paper frame when employng dynamc threshold bnarzaton. Ths results n nose and affects to the process of preprocessng for the mage. Therefore, we cannot use the proposed approach to detect text n the mage. (a) (b) (c) (d) (e) Fg.7. The nput mage for examned cases Table.2. The error rate of the examned cases Sample Tlted mage 5 o (a) Tlted mage 5 o (b) Tlted mage 30 o (c) Tlted mage 50 o (d) Partal mage (e) Method SOM 6.38% 6.78% 0.38% 00% 0.53% MLP 3.9% 3.03% 5.% 00% 6.97% PCA 0.08% 0.6% 0.48% 00% 0% PCA-MLP.52% 0.72% 0.96% 00%.88% FreeOCR N/A N/A N/A N/A 0.% Because test cases n Fg.7(a), 7(b), 7(c), 7(d) nclude the floor background and have the tlts, FreeOCR, whch s one of the famous OCR tools, cannot detect and recognze characters n the document. In contrast, by usng the preprocessng technques to remove the background and handle the tlt of the mage, our work can detect characters wth the low error rate. Ths ndcates that our methods mproved the accuracy of optcal character recognton compared wth some open source products n the same feld. Fg.8. The fault mage In order to elmnate the gloss of the paper frame, we can convert orgnal mage from RGB to HSV color space. HSV color space gves us to know the nformaton: the hue, saturaton, and lghtness of a specfc range of colors. The range of glare n the mage can be recognzed based on lghtness value receved. The Fg.9 llustrates the way to elmnate the lumnance nose n the mage. 5.4 EVALUATION THE OBTAINED RESULTS We can see that PCA recognzes the bold characters effectvely. Ths approach s based on the specfc features of character and not depends on the background color of page. In general, multlayer neural network recognzes characters exactly; however, ths method depends on results from the mage bnarzaton and background color of paper. For MLP network, the result of the recognton of bold characters wthout beng 20

7 tranng s not good, but wth the use of SOM model ths s resolved despte untraned bold characters. The volatlty of travelng through pxels employed n ths paper results n the case of some characters cohered s msdentfed. Nonetheless, the use of PCA can overcome ths dsadvantage by tranng wth mages whch have two coherng characters. The output layer of MLP model s fxed 6-bt Uncode of each of ndvdual characters, so cannot tran two lgatures smultaneously. Ths s the reason why the result of MLP usng PCA s not as hgh as that of PCA. (a) Fg.9. Elmnatng lumnance nose n a paper frame: (a) HSV mage, (b) Nose areas s detected Applyng PCA for mult-layer neural network enables the process of tranng network faster, speeds up for Backpropagaton algorthm, reduces the number of employed neurons sgnfcantly, and ncreases computng speed but stll provdes postve results. The problem of how to rotate text skewed has not been fully resolved yet. Wth the large tlt of document, the recognton results are not correct. In addton, the results depend on the qualty of mage. From there, t can be seen the mportant role of preprocessng to narrow the dversty n the mage. 6. CONCLUSION Based on the obtaned results of four proposed approaches, t can be seen that the accuracy of optcal character recognton n the mage s farly hgh. Prncpal component analyss not only gve the best result n recognzng text from the mage but t also sgnfcantly enhance the proprety of mult-layer neural network. However, the proposed means have not been good yet when applyng them for accented letters such as Vetnamese characters. From the emprcal results, the problem of optcal character recognton needs the post-processng for the document as well as the effectve approach for preprocessng before recognton to mprove the effcency. The proposed methods n ths paper can (b) be appled for other recognton problems such as faces, lcense plates, and traffc sgns. REFERENCES [] L.R. Blando, J. Kana and T.A. Nartker, Predcton of OCR Accuracy Usng Smple Image Features, Proceedngs of the Thrd Internatonal Conference on Document Analyss and Recognton, Vol., pp , 995. [2] D. Deodhare, N.N.R.R. Sur and R. Amt, Preprocessng and Image Enhancement Algorthms for a Form-based Intellgent Character Recognton System, Internatonal Journal of Computer Scence and Applcatons, Vol. 2, No. 2, pp. 3-44, [3] Sabyasach Das, Optcal Mark Recognton Technology for Rural Health Data Collecton, Techncal Report, IKP Centre for Technologes n Publc Health, 200. [4] S. Souza, J.M. Abe and K. Nakamatsu, MICR Automated Recognton based on Paraconsstent Artfcal Neural Networks, Proceda Computer Scence, Vol. 22, pp , 203. [5] R. Adelmann, M. Langhenrch and C. Floerkemeer, A Toolkt for Bar Code Recognton and Resolvng on Camera Phones Jump Startng the Internet of Thngs, Proceedngs of the Workshop on Moble and Embedded Interactve Systems at Informatk 2006, pp , [6] Jagroop Kaur and Rajv Mahajan, A Revew of Degraded Document Image Bnarzaton Technques, Internatonal Journal of Advanced Research n Computer and Communcaton Engneerng, Vol. 3, No. 5, pp , 204. [7] S. Haykn, Neural Networks. A Comprehensve Foundaton, 2nd Edton, Prentce-Hall, 999. [8] T. Kohonen, Self-Organzed Formaton of Topologcally Correct Feature Maps, Bologcal Cybernetcs, Vol. 43, No., pp , 982. [9] Munsh Kumar, M. K. Jndal and R.K. Sharma, PCA-based Offlne Handwrtten Character Recognton System, Smart Computng Revew, Vol. 3, No. 5, pp , 203. [0] D.H Jeong, C. Zemkewcz, W. Rbarsky and R. Chang, Understandng Prncpal Component Analyss Usng a Vsual Analytcs Tool, Techncal Report, UNC Charlotte, [] Jeff Heaton, Introducton to Neural Networks for Java, 2 nd Edton, Heaton Research Inc.,