Ouarda Hachour and Nikos Mastorakis
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Recognizability, Learnability and Ambiguity of ACR
OUARDA HACHOUR AND NIKOS MASTORAKIS Development Scientific Center of Advanced Technologies and Technical research For the Development of the Arabic Language (C.R.S.T.D.L.A) 1,rue Djamel Eddine al-Afghani –Bouzareah ALGIERS ALGERIA Phone/fax : (213) (021) 94-12-38
Hellenic Naval Academy Terma Hatzikyriakou, 18539 PIRAEUS , GREECE
Abstract : -In this paper, a flexible and compact architecture for implementing Hybrid Intelligent Systems ( HIS) using a combination of (Fuzzy Logic) FL and ES (Expert system) is introduced. To overcome the problem of Optical Character Recognition (OCR) and to make our system able to recognize all Arabic character shapes presented, a new technique methodology imagery is proposed. We propose a simple and regular architecture for automated reading system. . This architecture uses FL and ES which are necessary to bring the behaviour of Automate Intelligent Reading System. The combination of these technologies (FL,ES) is introduced to improve the powerful of hybrid intelligent systems in the field of OCR and especially ACR ( Arabic Character Recognition). We improve though our study that this combination which is alternate to classical technologies finds a meaning of classification which is based on the visual inspections tasks and image processing to classify and to understand each shape of Arabic character. To decide intelligibly if there are similarities in vector selection features and to find a meaning of classification for each character presented. In this work, we present a new architecture that integrates a Fuzzy classifier and the Expert System to extract topological and contextual informations of each character.The Results gotten of ACR databases are promising.
Key-Words:: Classification, Decision , Fuzzy Logic FL, Expert System ES, Rules Training, Inference, Recognition .
1 Introduction example reading of addresses and automatic To write and to communicate were of all times sorting, treatment of checks in banks are now a first preoccupation of the man. The writing basing on the process of scanned text and was, and will remain, one of the big foundation recognition of each shape of character. So, of civilizations and the fashion by excellence OCR application is supplied in any field on of conservation and transmission of the which the type of text becomes perilous and knowledge. In this context, researches on the hardly over . This intelligent task based on recognition of Print Arabic characters expose a reading text and recognition of shapes is the domain that spreads quickly and evoked overall objective of research areas which is indefinitely by an important research in the last devoted by the efforts of researchers to reduce two decades [1]. The visual, and several constraints and to widen the kingdom of the applications whose existence begins on the Arabic characters recognition. However in paper, more particularly in the : foreclosure, spite of the technological progresses, the indexing and automatic storage of documents, keyboard remains again a means obligated of or in publication attended by computer to communication with the computer [2,3,4]. facilitate the composition from a selection of several documents, in the post office for The problems arising in any OCR (Optical the use of several approaches can complement Character Recognition) system are not only the decision about the patterns under related with data acquisition, but also with the considerations. Moreover, this combining nature of the input. The nature is based on approaches relaxes the need to optimise the feature selection of each shape presented to the rate of recognition of Arabic characters. reader. Furthermore Arabic characters recognition constitutes today a preoccupation Combining decisions from several classifiers whose relevancy is the overall objective of can be used to improve on the result of research areas which is the most research area. character recognition [ 5]. The use of multiple classifiers can complement the task Today, many approaches of classification recognition. Classifier combining schemes proposed to classify Latin characters cannot have been intensively used in handwritten be applied into Arabic characters. Therefore, character recognition problems. For this a the recognition rate of Arabic characters is common theoretical frame work has been lower than that of disconnected characters, proposed for combining classifiers [6]. These such as printed English. Arabic Character is combining strategies are the max, min, median, cursive in general and have these main features and majority vote. Voting scheme, Bayesian formulation, and Dempster-Shafer theory of The shape of each character can have evidence were applied [7,8]. In the same until four distinct versions. context, Our study solution is based on the Diacritic points which make the main combination of several approaches as Fuzzy difficult tasks of segmentation. Logic, Neural Networks, Genetic The problem of PAW (Piece of Arabic Algorithms, .etc. These technologies NN and Word), the presence of horizontal and FL are becoming useful as alternate vertical ligatures which are also the approaches to the classical techniques one. main problem of segmentation tasks Artificial intelligence, including Fuzzy logic before recognition. Today, the and Neural networks, has been actively studied researchers are working about this and applied to domains such as automatically difficult task and many classification control of complex systems [9,10,11]. The approaches to address character Hybrid Intelligent systems which achieve tasks recognition problems are developed to without human operators, are required in many solve this problem . fields. Also, the hybrid intelligent systems with Some characters are presented as different combinations of the FL and NN will buckles shapes which are the main provide the software programs with more specific problem of recognition. intelligence.
The extracted character versions may have Nowadays, The hybrid intelligent systems can one, or more, of the variations such as scaling, carry their task in various environments by rotation, and translation. In this present work, themselves like human. These intelligent we present one way of classification of all systems are very useful in many fields as feature characters selected using the concept of industry and soft competing field programs. imagery in order to recognize a print Arabic Also, some operations in soft computing as in characters, this technique doesn't hold in optical character recognition and intelligent consideration the font, the size and the surface decision systems need to be defined by the of each character. The Arabic character combination of some theories as FL and NN to pictures present the difficulty to understand a solve all point ambiguities. So, to replace the part from the shape. More, the curviness of human being by some intelligent programs these shapes which make differences in all especially when the conditions of working features selected does not meet the may be perilous which need the necessity to be requirements of current world applications, replaced by intelligent programs. especially in automatic document processing such as amount recognition and OCR. This fact More, in the next future; we need to define motivates recent interesting in combining new methodologies of work with some several approaches in order to achieve intelligent tasks without to act the presence of improved recognition results. In other words, human being. Furthermore, Researchers have at their disposal, a computational tools such as Before recognition of picture, a several FL and NN which are more effective in the operations are done for the image as: design and development of software programs filtering, analysis, segmentation and than the predicate logic based methods of recognition [18]. That means that the traditional Artificial Intelligence. Fuzzy Logic need to understand the image itself FL and Neural Network NN are well before finding a function of established as useful technologies that classification for recognition process. complement each other in powerful hybrid The feature extraction aspect of system. image analysis seeks to identify inherent characteristics, or features, The Hybrid intelligent systems are now part of found within an image. the repertoire of computer system developers and important research mechanisms in the In Optical Character Recognition problem, study of Artificial Intelligent. The integration these characteristics are used to of NN and FL has proven to be a way to describe the character, prior to the develop useful real-world applications, and subsequent task of classification. The hybrid systems involving robust adaptive recognition of Arabic characters has control. been relatively sparse. The techniques developed for classifying In this paper we discuss clearly the advantage characters in other languages cannot of use of hybrid intelligent systems. We be used for recognizing Arabic present a new architecture combing Fuzzy characters due to the differences in logic and Expert systems. This hybrid structure. In this work, we present a approach gives the ability to list the Arabic new technique of imagery to characters in parallel with a logical, applicable, recognize a print Arabic characters, and intelligent decision. The new methodology this technique doesn't hold in of conception measures an important consideration the font, the size and opportunity in the measure that the system the surface of each character. marked no dismissal. First, in section1 we essential objective is to recognize the present the image processing. Second, the character in different shapes on recognition and classification form is presented which it is presented, if one wants to in the section 2. Finally, the conclusion is done recognize the “Ba” letter for example in section 3. small, big, or different, the system must recognize it since it is about the II Image Processing “Ba” letter and not another character. In the figure 1 we present Recognition system proposed of Arabic character. Our decision algorithm is based on the main steps which are presented in this figure.
Character Input Treatment
Size
Features Extraction Comparison Prototype
Decision
Recognition Results Fig.2 : Presence of no desired information with diacritic points .
Fig. 1: Recognition system proposed of II.2 Segmentation Arabic character The goal of this stage is to generate a carving II.1 Filtering of the picture in elements susceptible to be recognized by the classifier. The segmentation is a very important operation for the problem As we are interesting into recognition of prints Arabic characters, our process filtering is of character recognition [16] Segment by based on the elimination of dark pixel or no segment, the process of recognition can be qualitative and quantitative informations facilitated while shelling the character into presented in this picture due for : the worse simple coins characters to be identified [17]. In print, photocopies texts , and ancient document our case for the segmentation, we have tested for oldness for example. All these obstacles the dark colour of every pixel of a given perturb the process of recognition. Several character, the principle consists to find a means approaches were developed in order to solve of separation between the last dark pixel sent the problem of noise in image processing. back of a character and the one just present before it of another character in one same word in phase of recognition. Here is an example of The pixels presented with no adequate segmentation of an Arabic word information give to the soft program which is ( (العربية searching these pixels error when the execution of process is done. More, these no desired Sep Sep Sep Sep pixels with their informations ( luminous 4 3 2 1 intensities) are considered as main elements to be taken by the shape, whereas they must no be Fig.3: Segmentation of an Arabic word existed. In this figure we have four means of separation
Furthermore, these pixels are presented as the (Sep4, Sep3, Sep2, Sep1) , in this example we main error programs enter in the execution and test every dark colour of Sepi and we replace manipulation, they present the most difficult this one by white luminous intensity , to program of recognition. So to avoid this separate every shape of each character, that problem, and as we are not interesting into the means to be disconnect characters as in Latin filtering process and clean of image in the characters. We detect all juxtaposed pixels main task, we have tested each pixel with no (between the first and the second pixel situated desired information [12,13,14,15]. That means in the same basis line where there is a relation ) that our study is based on the game of pixels, and we replace their luminous intensities. This we test every luminous intensity of each pixel separation is very useful to separate between and if it is not presented in the main chain of vector feature selections which are the main pixels which are not between those constituting task of recognition. If we ask question why?, a given shape, they are replaced by pixels with simply because every QV (Quantification white luminous intensities as they are not Vector) , that means that the feature selection presented in the picture. In the fig 2 we present vector of each shape must be compared by an example of undesired information which those in the database, and after a decision can be confused with diacritic points. algorithm we can distinguish every presented shape and also classify and list intelligibly the chain of characters. Diacritic points II.3 Classification and Recognition process الكتاب الول ش Undesired information The recognition of an Arabic Character is the Fig. 4: Example of extraction of last stage of vision by computer after the Features Of the Arabic Character decision. Recognition Systems of the writing “Ba” require two stages: a stage of extraction of At the time of the extraction of the primitives, primitive and a stage of classification, this the shape is divided into edges. The number of earlier is done in order to separate the classes pixels of each edge contour and the curvature between the shapes presented. form of each character, belonging to each of the edges is determined. As we take no intelligent system and we show it, first edge Several works are carried on the development after we turn, second the second edge we turn, of new primitive to procure a good and finally we test the presence of diacritic discrimination while minimizing intra-classes points until the complete shape of one variability. These primitives are main tasks of character is done. Step by step, this system comparison shapes by those in the database. after learning becomes intelligent when the More, they are considered the most significant several training give it the ability to learn and elements of QV. These primitive are generally to understand every shape of character. So, classified in two families: the morphological when we realize an intelligent task we must primitive (buckles, …etc) and the statistics begin by the first stage of building into the last primitive that drift measures of spatial one. In other words, how the young child with distribution of pixels (zoning,...etc). Several no experience learns about the writing and the works have shown that there is a reading of Arabic characters, as we say for complementarities in the measure or two ways example you must staring by the first edge, of properties are put in relief between the after you turn , and this shape is always morphological primitive and the statistics presented with this number of edges, primitive. The visual human system seems to curvatures, and diacritic points. The logical fear the same approaches to recognize stages shape itself is presented and interpreted, the or objects. ask questions of each recognition process are:
how it is building ? We have opted for the primitive hybrid how it is interpreting ? combining figures on the contour and on pixels how it is presenting ? defining some morphological shapes in order how it is illustrating ? to characterize our pictures of characters. what are the main versions of this shape ? II.3.1 Morphological primitive there is a rotation ? The morphological aspect is very important in there is a translation ? the perception human, and its spatial tense is How to write this shape ? just as an important measure of local The edge structures are one edge,… ? information, which is not sufficient to be able How many diacritic points presented ? to represent a given motive. It is why it is taken into consideration in our approach. At These are the main questions asked to read and the time of the extraction of the primitive, the to recognize each shape. More, our study is picture is divided into : first lines, second oriented into the understand structure itself words and third character. For each character, before decision and distinction. The understand we extract the number of pixel of each edge of each shape is in the meaning, to create a selection of given shape see figure 4 . build form for main elements constituting the shape itself. The Vector of morphological primitive is compound into n values of edges and m values of curvature of the grid ( see the figure 5).
Number of Number of Number of pixels in the pixels in the pixels in the first edge of I …edge of a M .curvature each each of each character character character training, it will be interpreted in IF-THEN rule. We discuss clearly in the next section the principle of fuzzy system in recognition of Fig. 5: Vector of morphological print Arabic characters. primitive of each character II .3.3 Fuzzy classifier Architecture
II .3.2 Statistics primitive The fuzzy logic proves to be a robust tool to The statistics primitive are used here are based solve all one imprecise problem. The three on the code of chain of the contour of each well-known stages used generally are: the character. This is calculated by a follow-up in fuzzification, the Inference rules and the run-length of each shape presented. This whole defuzzification, which are the stages keys to of primitive is a vector of n values representing realize the fuzzy principle [19,20,21,22,23,24]. statistics on edges, and directions and The fuzzy model used at the time of the curvatures of pixels of each shape. The values conception is illustrated in the figure 7, the of curvature and edges calculated are algorithm to follow during the recognition quantified until four values (curv1,…,curv4) phase is described in the figure 8. The with take into account the diacritic points. See entrances of this model are the primitives Figure 6. described above. The label terms used in fuzzy linguistic are Diacritic Character DC and No Diacritic Character NDC. The membership Curv Curv Curv Curv 1 2 3 4 functions of truth degree of the input of the fuzzy system are shown in the figure 9. The training of the classifier is marked in two stages. A stage of rule generation and another one of adaptation of the adherence feature. Edge Edge Edge Edge 1 2 3 4 Generally, The generation of rules operates itself according to the distribution of the training whole in fuzzy linguistic terms (DC, NDC). The inference rules are illustrated in the Fig 6 : Format of the vector of primitives figure 10. The exit of our fuzzy classifier is N that will take the fuzzy linguistic terms: DC and NDC. To list the Arabic characters in parallel with a logical, applicable, and intelligent decision; we have used a hybrid approach: the fuzzy Input parameters DC, NDC logic and the expert systems to give the ability to classify all shapes presented into our system. Fuzzification of each truth degree The primitives developed are previously the entrances of the fuzzy system (See Figure 7).
Edge 1 Rule inference of each input Edge m N Curv Fuzzy 1 Curv System Estimate the degree of each output m
Fig.7: The fuzzy system Learning and classification of Arabic characters The exit of the fuzzy classifier must be combined with the one of the module of Arabic characters recognition. Our database of is composed of 120 pictures. The rules of Final decision and results classification and the functions of fuzzy whole adherence that define our primitive are learned on the parameters of each picture gotten after Fig. 8: The schematic decision algorithm truth degree membership function correspond for each rule. 1 u: factor of membership correspond for each rule. DC 0 NDC
P This intelligent task uses the fuzzy linguistic Pdp Pndp i terms and calculates for each degree of membership functions under shape of an expert Fig. 9: the membership functions of Pi system ES . IF (N1 is P) Then (N is PC) IF (Edgem is DC and Curvm) Then (N is DC) Today, researchersIF (N1 have is M) at their Then disposal, (N is CM) the IF (Edgem is NDC and Curvm is NDC) required hardware,IF (N1 is software, G) Then and (N is sensor GC) Then (N is NDC) technologies to IFbuild (N2 intelligent is P) Then system. (N is More,PC) they are also inIF possession (N2 is M) of Then a computational (N is CM) tool such as FL and ES that are more effective Fig. 10 : Fuzzy Rule . IF (N2 is G ) Then (N is GC) in the design andIF (N3 development is P ) Then of (Nintelligent is PC) system than theIF predicate (N3 is M logic ) Then based (N methodsis CM) Where 1<=m<=4. of traditional Artificial Intelligence[27]. In our case, the principle of the technique IF (N3 is G ) Then (N is GC) IF (N4 is P ) Then (N is PC) consists in verifying for every unknown shape An ES is a computer program that functions, is character a whole of rules, or each rule is the IF (N4 is M ) Then (N is CM) in a narrow domain,IF (N4 isdealing G ) withThen specialized(N is GC) shape: IF
« Ba » 98,33%
« Del» 97,16% the two techniques to propose structure of a hybrid expert-fuzzy approach. More, This new methodology of conception measures an important opportunity in the measure that the system marked no dismissal.
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