Dimauro, G.

Guerriero, A.

Impedovo, S.

Lecce Di, V.

Pirlo, G.

Salzo, A.

English

In the field of Optical Character Recognition (OCR), zoning is used to extract topological information from patterns. In this paper zoning is considered as the result of an optimisation problem and a new technique is presented for automatic zoning. More precisely, local analysis of feature distribution based on Shannon's entropy estimation is performed to determine "core" zones of patterns. An iterative region­growing procedure is applied on the "core" zones to determine the final zonin

ARTS repository - University of Groningen

   University of GroningenZONING DESIGN FOR HAND­WRITTEN NUMERAL RECOGNITIONLecce Di, V.; Dimauro, G.; Guerriero, A.; Impedovo, S.; Pirlo, G.; Salzo, A.Published in:EPRINTS-BOOK-TITLEIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2004Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Lecce Di, V., Dimauro, G., Guerriero, A., Impedovo, S., Pirlo, G., & Salzo, A. (2004). ZONING DESIGNFOR HAND­WRITTEN NUMERAL RECOGNITION. In EPRINTS-BOOK-TITLE s.n..CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 13-02-2023ZONING DESIGN FOR HAND-WRITTEN NUMERAL RECOGNITIONV.DI LECCE1, G.DIMAURO2, A.GUERRIERO1, S.IMPEDOVO2, G.PIRLO2, A.SALZO2(1) Dipartimento di Ing. Elettronica - Politecnico di BariVia Re David - 70126 Bari - Italy(2) Dipartimento di Informatica - Università di Bari Via Orabona, 4 - 70126 Bari – ItalyIn the field of Optical Character Recognition (OCR), zoning is used to extract topologicalinformation from patterns. In this paper zoning is considered as the result of an optimisationproblem and a new technique is presented for automatic zoning. More precisely, local analysisof feature distribution based on Shannon’s entropy estimation is performed to determine“core” zones of patterns. An iterative region-growing procedure is applied on the “core”zones to determine the final zoning.1 IntroductionNotwithstanding hundreds of good recognition algorithms have been proposed so far,machines are still far from achieve the performance of human beings in context-free hand-written character recognition, since different writing styles and changeable writing conditionsmake hand-written characters extremely variable [1].In order to improve the recognition capabilit y of reading machines, many efforts havebeen devoted to the analysis of local characteristics in hand-written characters [2,3,4,]. Asimple way to obtain local information is through zoning [5]. A zoning is a partition of thecontrol box of the pattern (i.e. the smallest rectangle containing the pattern); the elements ofsuch partition are used to identify the position in which features of the pattern are detected. Inother word, a handwritten characters are first normalized and included into a control box,successively, according to the zones of the control box, each feature is labeled with the nameof the zone in which it has been detected. So far, the zoning design, that is the way in whichthe partition of the control box is defined, was carried out exclusively on the basis of intuitivemotivations or personal experiences on the domain of application. In some cases the controlbox is divided into zones of equal size [5,6,7,8,9]: in other cases the control box is non-uniformly divided according to pattern density [10,11].In this paper a new technique for zoning design is presented. The technique firstdetermines the statistical distributions of local features using the set of training patterns.Successively the Shannon’s entropy is used to determine “core” zones of the control-boxshowing high-discrimination capabilit y. An iterative zone-growing process is used to designthe final zoning.583In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.), Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition,September 11-13 2000, Amsterdam, ISBN 90-76942-01-3, Nijmegen: International Unipen Foundation, pp 583-5882 NotationIn this paper the following definitions are used:•  X={ x1, x2, ..., xN} : set of patterns;•  F={ f1,...,fn} : set of features;•  C={  C1 ,..., Cm} : set of pattern classes;•  B:control-box of a pattern,i.e. smallest rectangular image including the pattern;•  bj : a pixel of the control-box, i.e. bj∈  B;•  I(bj) : set of neighbour pixels of bj (see Figure. 1a);•  zi : a sub-image of the control box, i.e. zi connected component,  i=1,2,…,M;•  I(zi) : set of neighbour pixels of zi (see Figure. 1b);•  zi* : extended zone of zi, i.e. zi* = zi ∪  { bj} , being bj ∈  I(zi) (see Figure. 1c);•  Z={ z1, z2, ..., zM}  : zoning of a control box B, i.e. Z is a partition of B;3 Shannon’s entropy for pattern discrimination.Shannon’s entropy has been widely used in Pattern Recognition for decision treeconstruction, image thresholding and segmentation [12,13,14,15]. Shannon’s entropy isdefined as [16]:∑==mk kk ppPH121log)(                                               (1)where P=(p1,p2,…,pm) is a probabil ity distribution.Now, if each element pk of the vector P represents the probabilit y that feature fi isdetected in the image zone zj for the patterns belonging to Ck ,k=1,2,…,m, theI(bj)(gray pixels)I(zi)(gray pixels)zone zi(black pixel)z* i(black pixels)pixel bj Figure. 1cpixel bj(black pixel)Figure. 1a Figure. 1b584Shannon’s entropy can also be used to estimate the discrimination capabilit y of  eachzone zj. In fact, it is easy to verify that:•  min H(P) if  ∃ k=1,2,…,m ∋ ′ î≠==kk   0          1kkpp•  max H(P) if   ∀ k=1,2,…,m : mpk1= .For instance, let be P={p1,p2} , the behaviour of the Shannon’s entropy is provided inFigure. 2 (p1=1-p2).010 0,25 0,5 0,75 1 p1H(P)Figure 2: Shannon’s entropyFigure 2 shows as the Shannon’s entropy can be used to estimate the discriminationcapabilit y of a zone: if the presence of feature fi in the image zone zj is equally probablefor patterns belonging to C1 and C2 , it results P={0.5,0.5} and therefore H(P)=1 (eq. 1);if the presence of feature f i in the image zone zj occurs exclusively for patternsbelonging to C1 , in this case  P={ 1,0} and H(P)=0 (eq. 1); similarly, if the presence offeature f i in the image zone zj occurs exclusively for patterns belonging to C2 , it resultsP={ 0,1} and H(P)=0 (eq. 1).4 The Zoning Design ProblemIn this paper, the zoning { }Z M=  z z z1 2, ,...,  is derived by the followingoptimisation problem in which the Shannon’s entropy H(Pzj) is used to evaluate thediscrimination capability of  zone zj when feature fi is considered:)H(Pmin zM1=jZjZ ∑=                                        (2)where Pzj  =(p1 zj,p2 zj,…,pm zj) is the probabilit y distribution of  the features fi   in  zj,  forpatterns belonging to the classes C1,C2,…,Cm. The probabil ity distribution Pzj =(p1 zj,p2zj,…,pm zj) is computed by using the formula:585∑ =〉∑==∀==mkjzkjkCmkjkCjkCjzk otherwisepzFzFzFpmk11.,0    0;  )(  if,)()(    ,...,2,1where FCk(zj) = XCk(zj) / NCk  , and  XCk(zj) = card { t∈ X | t belongs to Ck, and fi has been detected in t at zone zj } ;  NCk = card { t∈ X | t belongs to Ck } .5 A Technique for Zoning DesignThe technique for zoning design, based on eq. (2), is described in the following:(Preliminary Phase)•  For each one-pixel zone zj of the pattern image compute:  Pzj =(p1 zj,p2 zj,…,pm zj)(Phase 1: Core” zone Definition)•  Detect the M "core" one-pixel zones of the image z1={ b1} , z2={ b2} ,…,zj={ bj} ,…,zM={ bM} with the best discrimination capability (the points bi are localminima for function H) .(Phase 2: Iterative zone-growing procedure))•  Repeat until the set of zones { z1,z2,…, zj ,…,zM} becomes a partition of the patternimage:⇒  For each zj, j=1,2,…,M, select zj*min so that: H(Pzj*min)=min{ H(Pzj*) |zj* is anextended zone of zj}⇒  Select the zone zt*min so that:     H(Pzt*min) = min { H(Pzj*) | j =1,2,…,M} .This zone-growing process continues until the set of zones becomes a zoning, i.e. the setof zones becomes a partition of the control-box.6 Experimental ResultsThe new technique for zoning design has been applied to handwritten numeralrecognition. For this purpose we consider the classes C={  C1 ='0',..., C10='9'}, and theset of features F={f1,...,f9} [17,18]: f1 : vertical-down cavity; f2 : vertical-up cavity; f3 :horizontal-right cavity ; f4 : horizontal-left cavity; f5 : vertical-down end-point ; f6 :vertical-up end-point; f7 : horizontal-right end-point ; f8 : horizontal-left end-point; f9 :hole. The pattern set X used for zoning design consists of the 18468 hand-writtennumerals extracted from the "BR" directory of the CEDAR database [19].The new technique for zoning design has been evaluated with respect to atraditional  zoning based on a 4x4 grid. Numeral recognition has been performed by anholograph-based technique [20]. Table 1 reports the results when a 4x4 grid is used (a),and when the new technique is adopted (b). The set of 2671 hand-written numerals fromthe CEDAR database has been used for the test [19].586This result demonstrates the effectiveness of the new technique even if therecognition rate is not satisfactory for some classes. In fact, several zones provideinformation useful for the classification of patterns belonging to a restricted subset ofclasses, while no zone provides information useful for the other classes. In this senseother optimality functions must be considered able to select zones with high-discrimination capability and with complementary behaviour.Table  1: Experimental ResultsRecognition ratePattern Class Number ofTesting Patterns Zoning (a) Zoning (b)0 355 68% 77%1 288 85% 93%2 220 88% 93%3 206 84% 96%4 179 77% 88%5 116 56% 83%6 243 65% 87%7 217 63% 76%8 189 60% 75%9 176 65% 76%7 ConclusionIn this paper a new technique for the zoning design is presented. Topologicaldistribution of features is used to detect zones of the pattern image with highdiscrimination capabilities. The experimental results, carried out in the field of  hand-written digit recognition, point out the effectiveness of the new approach and makeclear promising research directions.References 1.   C.Y. Suen, M. Bertold, S. Mori, “Computer recognition of Hand-printedCharacters: The State of the Art” , Proc. of IEEE, 68 (4), pp. 469-483, 1980. 2.   C. Nadal, C.Y.Suen, “Applying human knowledge to improve machine recognitionof confusing handwritten numerals” , Pattern Recognition, vol. 26, n. 3, pp. 381-389, 1993. 3.   G. Dimauro, S. Impedovo, G. Pirlo, “Uncertainty in the recognition process, someconsideration on human variable behaviour” , in From Pixels to Features III :Frontiers in Handwriting Recognition, S. Impedovo and J.C. Simon (eds.), ElsevierScience Publ., 1992, 215-221. 4.   C.Y.Suen, J. Guo, Z.C. 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Chang, “Target detection in multispectral imagery usingspectral co-occurrence matrix and entropy thresholding” , Optical Engineering, vol.34, 1995, pp. 2135-2148. 16.   J.Aczel and J. Daroczy, “On Measures of Information and TheirCharacterisations” , New York, Academic Press., 1975. 17.   G. Dimauro, S. Impedovo, G. Pirlo, A. Salzo, “A Multi -expert system forhandwritten digit recognition” , in Progress in Handwriting Recognition, S.Impedovo (ed.), World Scientific, 1997, pp. 363-367. 18.   G. Dimauro, S. Impedovo, G. Pirlo, A. Salzo, “Zoning Design for HandwrittenNumeral Recognition” , Lecture Notes in Computer Science 1311, A. Del Bimbo(ed.), Springer, Berlin, 1997, pp. 592-599. 19.   R.K. Fenrich, J.J. Hull , “Concerns in creation of image databases” , ProceedingsIWFHR-III , Buffalo, New York, May 25-27, 1993, 112-121. 20.   N.D. Gorsky, “Off-line Recognition of Bad Quality handwritten Words UsingPrototipes” , in Fundamentals in Handwriting Recognition, S. 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ZONING DESIGN FOR HAND­WRITTEN NUMERAL RECOGNITION

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ZONING DESIGN FOR HANDWRITTEN NUMERAL RECOGNITION

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ZONING DESIGN FOR HAND­WRITTEN NUMERAL RECOGNITION

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ZONING DESIGN FOR HANDWRITTEN NUMERAL RECOGNITION