a stroke-based neuro-fuzzy system for handwritten chinese character recognition

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A stroke-based neuro-fuzzysystem for handwrittenchinese characterrecognitionJue-Wen Lin , Shie-Jue Lee & Hsin-Tai YangPublished online: 30 Nov 2010.

To cite this article: Jue-Wen Lin , Shie-Jue Lee & Hsin-Tai Yang (2001) A stroke-based neuro-fuzzy system for handwritten chinese character recognition,Applied Artificial Intelligence: An International Journal, 15:6, 561-586, DOI:10.1080/088395101753199579

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Applied Arti® cial Intelligence, 15:561 ± 586, 2001Copyright # 2001 Taylor & Francis0883-9514/01 $12.00 ‡.00

& A STROKE-BASEDNEURO-FUZZY SYSTEM FORHANDWRITTEN CHINESECHARACTER RECOGNITION

JUE-WEN LIN, SHIE-JUE LEE, andHSIN-TAI YANGDepartment of Electrical Engineering, National SunYat-Sen University, Taiwan

In this article, a stroke-based neuro-fuzzy system for o� -line recognition of handwrittenChinese characters is proposed. The system consists of three main components: strokeextraction, feature extraction, and recognition. Stroke extraction applies a run-length± basedmethod to extract strokes from the image of a given character. V arious fuzzy features of theextracted strokes, including slope, length, location, and cross relation, are obtained by thefeature extraction module. An ART-based neural network, using a two-stage trainingalgorithm, is used to recognize characters. This system extracts strokes in only two passes,and is free from the presence of spurious and thick strokes. The neural model used provides afast convergence rate. Nodes are allowed to be shared to reduce the size of the resultingnetwork. Features need not be classi® ed in advance by the user. Furthermore, thearchitecture of the network is self-constructed without the intervention of the user.Experiments have shown that this system is e� ective.

Character recognition has become more and more important in areas ofdocument processing, language translation, electronic publication, and o� ceautomation. Recognizing Chinese characters by computers remains a hardtask due to the following reasons:

(1) Chinese characters are very artistic in shape;(2) there exist a great number of characters;(3) there are many similar characters;(4) handwriting styles vary greatly among different people.

Partially supported by the National Science Council under grant NSC-86-2213-E-110-031 . A pre-liminary version of this paper appeared in Proceedings of IEEE International Conference on Systems,Man, and Cybernetics, Orlando, FL, October 1997.

The authors would like to express their sincere appreciation to the anonymous referee for thevaluable comments.

Address correspondence to Jue-Wen Lin, Department of Electrical Engineering, National SunYat-Sen University, Kaohsiung 804, Taiwan. E-mail: [email protected]

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Roughly speaking, character recognition methods can be divided intotwo categories: statistical approaches (Maeda, Kruosawa, Asador, Sakai, &Watanabe, 1982; Gu, Wang, & Suen, 1983; Kimura, Takashina, Tsuraokar,& Miyake, 1987; Kim, Kim, Kim, & Lee, 1997; Chang & Yang, 1999) andstructural approaches (Hsieh, Fan, Fan, & Ho, 1995; Leung, Cheung, &Wong, 1987; Chau, Fan, & Fan, 1997; Hap, Xiao, & Dai, 1997; Lee & Chen,1997; Chang & Yen, 1998). Unlike English alphabets, Chinese characters havestructural characteristics that are regarded as important in the recognitionprocess (Chan & Cheung, 1992). Because of the use of structural features,structural approaches can usually achieve higher recognition rates andbecome more and more popular. However, good, but usually complex,feature extraction methods should be designed to comply with underlyingrecognition methods. Strokes are basic structural units in Chinese characters,and have been proven to be more consistent than geometrical features(Chang & Wang, 1994; Chang & Yang, 1999; Leung et al., 1987;Wakahora & Odaka, 1997; Yamamoto & Rosenfeld, 1982).

In this article, a stroke-based neuro-fuzzy system for handwrittenChinese character recognition is proposed. We assume that an input char-acter is normalized and oriented appropriately. This system contains threemain components: stroke extraction, feature extraction, and recognition, asshown in Figure 1. Stroke extraction applies a run-length± based method toextract strokes from the image of a given character. First, the given image isscanned and divided into stroke segments and fork sections. Then strokesegments and fork sections are combined into strokes. Feature extractionconcerns extracting features from the obtained strokes. To consider varia-tions among handwritten characters due to di� erent writing styles, the cap-ability of tolerating transitional and rotational displacements is necessary(Chan & Cheung, 1992). The fuzzy concept is applied (Zadeh, 1965; Yeung& Fong, 1996; Malaviya & Peters, 1997) to describe various features, such asslope, length, location, and crossing relation, for the obtained strokes. AnART-based neural network (Kasuba, 1993; Markuzon, Reynold, Carpenter,Grossberg, & Rosen, 1992; Carpenter & Grassberg, 1988), using a two-stage training algorithm, is used to recognize characters. Parts ofcharacters are self-classi® ed with an unsupervised learning scheme, and a

562 J.-W . L in et al.

FIGURE 1. Flow diagram of our system.

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character composed of speci® c parts is recognized by a supervised trainingalgorithm.

This system has some advantages. Strokes can be extracted in only twopasses, and spurious and thick strokes can be avoided. The ART-basedneural model provides a fast convergence rate. Features for strokes of eachindividual character can be analyzed automatically and categorized intomultiple classes. Therefore, they need not to be classi® ed in advance by theuser. Furthermore, nodes are added and shared automatically, and the userneed not build the network by trial-and-error. The e� ectiveness of our systemis demonstrated by experiments on two databases: one consists of Chinesecharacters with the same radical and the other contains similar characters. Arecognition rate of over 90% is achieved.

STROKE EXTRACTION

The purpose of stroke extraction is to extract strokes correctly from thebinary image of a given character. In this system, blocks of contiguous pixelsare found. Then spurious blocks, which are possibly caused by artistic writ-ing, are removed. Then blocks are classi® ed into stroke segments or forksections. Finally, stroke segments and fork sections are combined intostrokes. Details of each step are presented below.

Scan Blocks

The ® rst step of stroke extraction is to use a run-length± based method todivide the image into blocks each of which is a collection of contiguouspixels. This is done by scanning the image in two directions, horizontallyand vertically. A horizontal run is a set of connected pixels in a row. Similarly,a vertical run is a set of connected pixels in a column. Therefore, a set ofhorizontal runs and another set of vertical runs for the image is obtained. Thelength of a run is the number of pixels in this run. A set of adjacent runs aregrouped into a block if their lengths di� er within L DB pixels and the centralpixels of two adjacent runs have a shift within CDB pixels. Horizontal runsare grouped into H-scan blocks and vertical runs are grouped into V-scanblocks, respectively. For example, consider the image of Figure 2(a) and letLDB = 2 and CDB = 1. Figure 2(b) shows the pixels obtained from scanningthe image horizontally. Note that the two lower runs are 11 pixels long andtheir centers are on the sixth pixel. The other two runs at the right-up cornerare three pixels long and their centers are on the second pixel. Since thelengths of the lower two runs di� er within two pixels, they form a H-scanblock. Similarly, the two runs at the right-up corner form another H-scanblock. Figure 2(c) shows the pixels obtained from scanning the image verti-cally. Note that the eight runs on the left are two pixels long and the three

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runs on the right are three pixels long. Since the lengths of all the runs di� erwithin two pixels and the centers of adjacent runs di� er within one pixel, allthe runs form a V-scan block.

For convenience, the width of a block is de® ned to be the number of runsin the block and the length of a block to be the average length of runs in theblock. For example, the width and the length of the lower H-scan block inFigure 2(b) are 2 and 11, respectively, and the width and the length of the V-scan block in Figure 2(c) are 11 and 28/11, respectively.

Stroke and Fork Blocks

After scan blocks are found, spurious blocks are removed that arepossibly caused by artistic writing. Blocks are also roughly divided intostroke blocks or fork blocks, depending on their size and relative positions.An H-scan block is:

° an H-scan stroke block if

Ð it is an isolated block, orÐ it is wide enough and shorter than its adjacent H-scan blocks.

° an H-scan boundary block if it is narrow and short, and appears to thecorner of another H-scan block.

° an H-scan fork block if it is neither an H-scan stroke block nor an H-scanboundary block.

Two parameters, MW S and ML S, denoting minimal width and length,respectively, of a stroke block are used as thresholds for the above categor-ization. Consider an image shown in Figure 3(a) and its three H-scan blocksshown in Figure 3(b). Let both MW S and ML S be three. The top block is® ve runs wide and ® ve pixels long, and is classi® ed as an H-scan stroke block.The lower block is two runs wide and 23 pixels long, and is classi® ed asan H-scan fork block. The up-right block is two runs wide and 1.5 pixelslong, and is classi® ed as an H-scan boundary block. Similarly, a V-scan blockis classi® ed into a V-scan stroke block, a V-scan boundary block, or a V-scanfork block.


(a) (b) (c)

FIGURE 2. (a) An example image; (b) horizontal runs with two H-scan blocks; (c) vertical runs with oneV-scan block.

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An H-scan or a V-scan boundary block is regarded as a spurious partcaused by artistic handwriting or by noise disturbance. To avoid deviationswhen extracting strokes, boundary blocks are eliminated as soon as they arefound.

Stroke Segments and Fork Sections

Next, we would like to classify blocks into stroke segments and forksections, which are elements of a stroke. To do this, one more step is neededto consider the context of each block. One block type may need to bechanged to another type if it is necessary in the underlying context.

We de® ne a region to be the intersection of one H-scan block and one V-scan block. There are only three cases to be considered for a region: a forkblock intersects with another fork block, a fork block intersects with a strokeblock, and a stroke block intersects with another stroke block. By consider-ing these cases, we have the following rules for determining stroke segmentsand fork sections. For convenience, we assume MW S to be three for allexamples in this section.

1. If a region is the intersection of an H-scan fork block and a V-scan forkblock, then the region is called a fork section. For example, considerFigure 4. The long horizontal block is an H-scan fork block and thelong vertical block is a V-scan fork block. Therefore, the central regionin the ® gure is a fork section.

2. If a V-scan stroke block passes through an H-scan stroke block, an H-scanfork block, and another H-scan stroke block in sequence, as shown inFigure 5(a), then the V-scan stroke block is changed to a V-scan forkblock. In this way, a fork section will be formed in the middle as desired.Similarly, if an H-scan stroke block passes through a V-scan stroke block,a V-scan fork block, and another V-scan stroke block in sequence, asshown in Figure 5(b), then the H-scan stroke block is changed to an H-scan fork block.

3. If a region is the intersection of an H-scan fork block and a V-scan strokeblock, but is not in the situation of case (3), then the region is a V-scan

A Neuro-Fuzzy System for Character Recognition 565

(a) (b)

FIGURE 3. Examples of stroke blocks, fork blocks, and boundary blocks.

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stroke segment. Similarly, if a region is the intersection of a V-scan forkblock and an H-scan stroke block, then the region is an H-scan strokesegment. For example, consider Figure 4 again. The left wing of the ® gureis the intersection of the long H-scan fork block and a V-scan strokeblock. Therefore, it is a V-scan stroke segment. Similarly, the right wingof the ® gure is another V-scan stroke segment, and the top and the downwings are H-scan stroke segments.

4. If a region is the intersection of an H-scan stroke block, h, and a V-scanstroke block, v, consider the following cases:° If h is a subset of v, then the region is a V-scan stroke segment. For

example, the dark region shown in Figure 6(a) is a V-scan stroke seg-ment. Similarly, if v is a subset of h, then the region is called an H-scanstroke segment.

° If neither h is a subset of v nor v is a subset of h and the orientations ofthe two stroke blocks match well, then the union of the two strokeblocks is a stroke segment. The orientation of a block is de® ned to bethe gradient of the straight line connecting the midpoints of the two


FIGURE 4. Stroke segments and fork sections.

(a) (b)

FIGURE 5. One stroke block intersects one fork block.

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end-runs of the block. In this case, the scan direction of the obtainedstroke segment can be either horizontal or vertical. For example, theshaded area (the dark area and the two slightly shaded areas together)in Figure 6(b) is a stroke segment.

° If the region is small, the region is regarded to be spurious and isdeleted. This case is shown in Figure 7(a). If the region is large, butthe orientations of h and v do not match well, then the region is alsodeleted. This case is shown in Figure 7(b).

Strokes

The ® nal step is to combine a set of adjacent stroke segments and forksections into a stroke if their orientations match well. The whole procedurefor stroke extraction can be summarized below.

° Step 1. Scan the character image horizontally to obtain H-scan blocks.° Step 2. Divide H-scan blocks into H-scan stroke blocks, fork blocks, and

boundary blocks.


(a) (b)

FIGURE 6. One stroke block intersects another stroke block.

(a) (b)

FIGURE 7. Spurious regions are deleted.

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° Step 3. Scan the character image vertically to obtain V-scan blocks.° Step 4. Divide H-scan blocks into V-scan stroke blocks, fork blocks, and

boundary blocks.° Step 5. Determine stroke segments and fork sections.° Step 6. Combine adjacent stroke segments and fork sections into strokes.

An example for illustration is given. Consider Figure 8(a), which is theChinese character `̀ .’ ’ By ® nding boundary blocks, the spurious partscaused by artistic writing are removed. Then stroke segments and fork sec-tions are identi® ed. As shown in Figure 8(b), there are four V-scan strokesegments (indicated by ` |̀ ’ ’ ), one H-scan stroke segment (indicated by ``± ’ ’ ),and two fork sections (indicated by ``+ ’ ’ ) . After combining stroke segmentsand fork sections, we have three strokes as shown in Figure 8(c) in which astroke is indicated by the midpoints of the runs in the stroke. Note that eachfork section is shared by two strokes.

FEATURE EXTRACTION

After strokes are obtained, they are represented by features that describethe characteristics of individual strokes and the relative positions amongthem. In this way, a character will be described by a set of features.The later recognition phase will be performed based on these features.The features are classi® ed into four groups: STROKE_TYPESTROKE_LOCATION, LENGTH, and CROSS_RELATION, which arefuzzy variables (Zadeh, 1965; Chan & Cheung, 1992; Yeung & Fong, 1996;Malaviya & Peters, 1997). The fuzzy values that each fuzzy variable may haveare listed in Table 1. Details of the four groups are described below.

STROKE_TYPE

This group concerns the slope of strokes. It contains four feature values:horizontal (H), vertical (V), right-slanting (RS), and left-slanting (LS), with


FIGURE 8. An example for stroke extraction.

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membership functions shown in Figure 9. The angle of a stroke is measuredwith respect to a horizontal line passing through the left end of thestroke. For example, a stroke with the angle of 708 would have the featurevector (0/H, 0.67/V, 0/RS, 0.33/LS).

STROKE_LOCATION

The feature values to be extracted in this group are associated with thelocation of a stroke. We use the position of the center of a stroke to representthe location of the stroke. As shown in Table 1, there are nine feature valuesin this group. The image of a character is divided into nine zones as shownin Figure 10. Six fuzzy values with the membership functions representing left(L ) , horizontally middle (M h), right (R), up (U), vertically middle (M v),and down (D) , respectively, are de® ned in Figure 10. The central point ofa stroke is associated with these six fuzzy values according to its position.Then the nine feature values to be extracted are derived by the following nineequations:


TABLE 1 Features and Values

Fuzzy Variables Fuzzy Values

STROKE_TYPE vertical (V), horizontal (H), right-slanting (RS), left-slanting (LS)STROKE_LOCATION middle-zone (MZ), down-zone (DZ), up-zone (UZ), left-zone (LZ),

right-zone (RZ), leftup-zone (LUZ), rightup-zone (RUZ),leftdown-zone (LDZ), rightdown-zone (RDZ)

LENGTH long (L), short (S), normal (N)CROSS_RELATION crossi (Ci), down-termi (DTi) , up-termi (UTi), left-termi (LTi),

right-termi (RTi), leftup-corneri (LUCi), rightup-corneri (RUCi),leftdown-corner i (LDCi), rightdown-corneri (RDCi), ( i = 1; . . . ;6)

FIGURE 9. Membership functions for STROKE_TYPE.

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middle-zone (MZ) = M h ^ M v; down-zone (DZ) = M h ^ D;

up-zone (UZ) = M h ^ U ; left-zone (LZ) = L ^ M v;

right-zone (RZ) = R ^ M v; leftup-zone (LUZ) = L ^ U ;

rightup-zone (RUZ) = R ^ U ; leftdown-zone (LDZ) = L ^ D;

rightdown-zone (RDZ) = R ^ D:

The symbol ^ stands for fuzzy AND operation. For example, the center ofthe stroke in Figure 10 is represented by (0:3=L, 0:7=M h, 0=R , 0:3=U ,0:7=M v;0=D), and the feature values for STROKE_LOCATION are(0.7/MZ, 0/DZ, 0.3/UZ, 0.3/LZ, 0/RZ, 0.3/LUZ, 0/RUZ, 0/LDZ, 0/RDZ).

LENGTH

In this group, three feature values, long (L), short (S), and normal (N),concerning the length of strokes are to be extracted. The membership func-tions for these three values are shown in Figure 11. Given a character, theLENGTH of a stroke of this character is calculated as follows. First, theratio of the length of the stroke to the average length of the character is


FIGURE 10. Nine zones and participating membership functions.

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calculated. The average length of the character is derived by the followingequation:

average_length = total length of strokes/number of strokes.

Then, by referring to Figure 11, the three feature values associated with thestroke are obtained.

For example, assume that the average stroke length of a character is50 pixels. For a stroke of 30 pixels long. the ratio of the stroke lengthto the average length is 0.6. As a result, the feature values for LENGTHare (0/L, 0.8/S, 0.2/N). Intuitively, this stroke is considered to be short.

CROSS_RELATION

This feature contains values concerning cross points of strokes. Twoaspects are considered: (1) the location of a cross point, and (2) the slopesof the two crossing strokes. For two strokes that cross each other, the strokewith an angle of smaller absolute value is considered to be the horizontalstroke and the other to be the vertical stroke. For a horizontal stroke, the leftend of the stroke is the head and the right end of the stroke is the tail; for avertical stroke, the upper end of the stroke is the head and the down end ofthe stroke is the tail. By normalizing the length of each stroke to be one, thedistance from the head to the cross point for each stroke is measured. Strokesare allowed to be elongated to obtain a cross point if they are very close butdo not intersect. Then, by the membership functions shown in Figure 12, thetwo distances we transformed (maybe negative or larger than one due to theelongation of a stroke) associated with the two crossing strokes to fuzzyvalues that represent the degree to which the cross point is close to theleft/up (L /U) , middle (M h=M v) or right/down (R/D) part of the area.Using the same equations listed in STROKE_LOCATION we get the nine


FIGURE 11. Membership functions for LENGTH.

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values, MZ, DZ, UZ, LZ, RZ, LUZ, RUZ, LDZ, and RDZ, for the locationof each cross point.

For the slopes of strokes, six ordered cases are used as the prototypes, H-V, H-RS, H-LS, V-RS, V-LS, and LS-RS, of two crossing strokes. H-Vmeans one of the two crossing strokes is vertical and the other is horizontal,and so on. Any pair of crossing strokes can be evaluated to be similar to eachof those six cases to some degree. Consider two crossing strokes whosefeature values in the STROKE_TYPE group are (H1;V1;RS1;LS1) and(H2;V2;RS2;LS2) , respectively. Then the degree of similarity to the ith pro-totype, X-Y, can be computed by the following equation:

S i(X;Y ) = max(X1 ^ Y2;X2 ^ Y1)

where i;1 µ i µ 6 denotes the cardinal position of X-Y in the sequence of theprototypes i.e., i = 1 for H-V, i = 2 for H-RS, etc. Then the nine feature valuesin this group for the ith case, X-Y, is derived by the following equations:

crossi (Ci) = MZ ^ S i(X;Y);

down-termi (DTi) = DZ ^ Si(X; Y);

up-termi (UTi) = UZ ^ Si(X; Y);

left-termi (LTi) = LZ ^ Si(X;Y);

right-termi (RTi) = RZ ^ S i(X;Y);

leftup-corneri (LUCi) = LUZ ^ S i(X;Y);

rightup-corner i (RUCi) = RUZ ^ S i(X;Y);

leftdown-corneri (LDCi) = LDZ ^ S i(X;Y);

rightdown-corner i (RDCi) = RDZ ^ S i(X;Y):


FIGURE 12. Membership functions for CROSS_RELATION.

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Note that when a stroke crosses more than one stroke, the ® nal feature vectorneeds to be divided by the number of cross points on the stroke.

Let us illustrate the above idea with an example. Consider the twostrokes in Figure 13. The distances from the head to the cross pointfor strokes 1 and 2 are 0.7 and 0.1, respectively, with (1=L;0=M h;0=R) and(0=U ;0:67=M v;0:33=D) for the cross point, and (0/H1;0=V1;0=RS1, 1/LS1)for stroke 1 and (1/H2;0=V2;0=RS2;0=LS2) for stroke 2. Notice thatS3(H;LS) = 1. Therefore, the cross relation for prototype H-LS is (0=C3,0=DT3, 0=UT3, 0:67=LT3, 0=RT3, 0=LUC3 , 0=RUC3 , 0:33=LDC3, 0=RDC3).

RECOGNITION USING NEURAL NETWORKS

Now that we have a set of features for a character, we may performrecognition based on these features. The order of strokes is important fora stroke-based character recognition system. However, it is not trivial todetermine the order for an o� -line recognition system (Lin & Chen, 1996;Wakahara & Odaka, 1997). In this system, the order of strokes for a char-acter is simply determined by scanning the image from the left-up corner tothe right-down corner. Nevertheless, other stroke ordering methods (Leow,1986; Tai, 1984) are allowed. Character recognition is a problem of classi® -cation, and any method for classi® cation can be applied to character recog-nition if features are extracted adequately. Recently, neural networks arepopular and have a good performance on this problem domain. Therefore,neural techniques are used to recognize Chinese characters.

Because Chinese characters can be divided into many parts according tothe relationship among strokes, Chinese character recognition can be viewedas a task of combining speci® c parts together. The neural network embeddedin our system is an ART-based neural network (Kasuba, 1993; Markuzon etal., 1992; Carpenter & Grossberg, 1988). The ART-based neural network


FIGURE 13. Two crossing strokes.

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provides a fast convergence rate. Features from characters can be analyzedand categorized into multiple classes automatically. Therefore, they need notbe classi® ed in advance by the user. Furthermore, nodes are added andshared automatically, and the user need not build the network by trial-and-error.

Neural Architecture

The neural architecture is a three-layer network, as shown in Figure 14.The ® rst layer of the network is the input layer, which accepts as inputs all thefeature values obtained by feature extraction. The second layer is called theparts layer. These two layers can be viewed as consisting of several fuzzyART (FA) units. Fuzzy Art units are a variation of ART1 networks(Carpenter & Grossberg, 1988) which accepts fuzzy inputs. The number ofFAs is identical to the maximal number of strokes that a character may have.For each FA, the links between layers are fully connected. Besides, each FA


FIGURE 14. Our neural architecture.

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includes a parameter » that is used for a vigilance test. The last layer is thewords layer, i.e., the output layer of the whole neural network. The linksbetween the parts layer and the words layer are also fully connected.

Training Phase

The neural network uses a two-stage training technique. In the ® rst stage,the unsupervised training algorithm similar to ART1 (Carpenter &Grossberg, 1988) is used to train FAs. All the FAs are trained independently.For brevity, only the training process for the ® rst FA is explained, and theother FAs are trained in a similar way. Assume that the ® rst stroke of eachtraining character is available. At the beginning, the FA only has nodes in theinput layer. To start training the FA, some value has to be set to the vigilanceparameter » and one output node has to be generated with its weights beingthe feature values of the ® rst stroke of the ® rst training character. This is anindication that the input pattern is learned and coded in the FA. Then thefeature values of the ® rst stroke of the next character are presented and thenode with the largest activation is selected as the winning node in the partslayer. The activation is evaluated by the following equation:

Ti(I ) =|I ^ wi |¬ ‡ |wi |

;

where I stands for the vector formed by the input feature values, wi representsweights between the input layer and node P i, and ¬ is a constant close tozero. Apparently, Ti can be regarded as the measurement of similaritybetween I and wi . The more similar I and wi, the larger Ti . For the winnerP k , a match degree can be evaluated by the following equation:

MP k(I ) =

| I ^ wk ||I |

:

If the match degree of the winner is greater than the vigilance, learningshould occur. The adjustment of the weights associated with the winner isde® ned to be

w(new)

k = (I ^ w(old )

k ) ‡ (1 ± )w(old )

k :

In general, is set to be one and the above equation is simpli® ed to

w(new)

k = w(old )

k ^ I :


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Then the next training pattern is presented. If the winner cannot pass thevigilance test, the node with the next-to-largest activation is taken to be thewinner and so on. In case no winning node passes the vigilance test, a newoutput node is created with weights being the feature values of this trainingpattern. This training process is iterated until all FAs are stable.

In the second stage of training, the words layer is trained with a super-vised learning algorithm similar to the instar neural networks (Freeman &Skapura, 1991). Each node in the words layer functions as an instar andrepresents a speci® c character. An instar can learn a number of input vectors,which are fairly close together and form a cluster. For brevity, it shall not bedescribed in detail in this article; readers may refer to (Freeman & Skapura,1991) for more details. For each node in the words layer, the character itrepresents is composed of those parts whose weights are large.

Recognition Phase

When training is over, the neural network is ready for recognition. Theinputs of each FA are the feature values of an extracted stroke. Each node inthe parts layer of an FA would be activated to some degree. The more a nodeis activated, the more possible the part it represents is a part of the characterto be recognized, as shown in Figure 15. Similarly, nodes in the words layerwould be activated with di� erent degrees. The winner is the character that isrecognized. Figure 16 shows how parts are combined to form di� erent words.If the ® rst four parts are activated heavily and the last part is activatedslightly, then `̀ ’ ’ is recognized. However, if the last part is also activated


FIGURE 15. The features of a stroke recognized by a fuzzy ART (FA).

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heavily, `̀ ’ ’ is recognized instead. Figure 17 shows the character `̀ ’ ’ andits ® ve parts.

Figure 18 demonstrates how the neural network recognizes the character`̀ . ’ ’ First, the feature values of strokes are presented to the input layer of® ve FAs. The outputs are the parts of the character, which were learned bytraining the FAs previously. The parts then activate the nodes in the wordslayer with di� erent degrees. The maximal output is the character recognized.

EXPERIMENTAL RESULTS AND COMPARISONS

In an on-line recognition system, the stroke structure can be obtainedaccording to the sequence of writing via a pen-based input device such as atablet (Kim et al., 1997; Malaviya & Peters, 1997; Wakahara & Odaka, 1997;


FIGURE 16. The words layer.

FIGURE 17. A character formed by its ® ve parts.

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Wu, Jou, & Lee, 1997). However, in an o� -line recognition system, the inputcharacters are scanned optically and saved as raster images, so the strokeinformation is not available. Since our system is an o� -line recognition system,we will not compare our system with on-line recognition systems in this section.

Many stroke extraction algorithms su� er from the presence of spurioustails in the obtained skeletons. The algorithm, SPA, proposed by Naccacheand Shinghal (1984) was claimed to be better than many other methods.However, a skeleton obtained by SPA may not be unique. The choice ofthe scanning sequence may lead to di� erent skeletons. Also, two to fourpasses are needed to obtain a skeleton. Lin and Chen (1996) developed astroke extractor that performs two tasks: ® nding certain adjacent segmentalstrokes for being merged into a complete stroke, and searching the cornerpoint to divide the bent segmental stroke into two or more individual strokes.However, this method can only be applied to printed Chinese characters.This stroke extraction is compared with these two methods on the printedcharacter `` ’ ’ which was exempli® ed in the papers. Figure 19(a) shows theoriginal character and Figure 19(b) shows the result obtained by this method.Note that these results are obtained in two passes, horizontal and vertical.The results obtained by the other two methods are shown in Figures 19(c)and (d), respectively. It is easily seen that the results obtained from these twomethods di� er from the original characters, and thus it may be di� cult toextract correct features using these methods. Apparently, our method isbetter in this regard.


FIGURE 18. An example of recognition.

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FIGURE 19. Comparison among di� erent methods: (a) original character; (b) our method; (c) Lin andChen’s method; (d) Naccache and Shinghal’s method.

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Next, we present results of two experiments on character recognitionobtained by our system. Two databases are used: one containing Chinesecharacters with the same radical and the other containing similar Chinesecharacters. First, we tested this system on four sets of 15 characters, all ofwhich have the same radical ``

[

.’ ’ These sets are respectively written bydi� erent people. One set is shown in Figure 20 Three out of the four setsare used as training data and the rest as test data. Therefore, four experi-ments are done. In the four experiments, only two characters in total aremisclassi® ed: `̀ ’ ’ misclassi® ed to `̀ ’ ’ and `` ’ ’ misclassi® ed to `̀ ,’ ’ andthe average recognition rate is 97%. The two misclassi® cations occur indi� erent experiments. Let us take a look at these misclassi® cations. In the® rst case, the input character of `̀ ’ ’ is shown on top of Figure 21. Note thatthe fourstroke (node 4) and the ® fth stroke (node 5) are disconnected.However, these two strokes are connected in all the written words for `` ’ ’in the three training sets, as shown in the left bottom of Figure 21. Therefore,


FIGURE 20. One set of 15 characters with the same radical.

FIGURE 21. Misclassi® cation due to disconnection.

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the cross relationship between these strokes is missing and node 4 and 5cannot match nodes 9 and 10 well. On the contrary, nodes 1, 2, 3, and 4match nodes 11, 12,13,14 well and misclassi® cation occurs. To overcome thisproblem, the misclassi® ed character should be used as a training pattern andthen train the neural network again. Similarly, the misclassi® cation of `` ’ ’to `` ’ ’ is due to the fact that no training patterns match closely enough tothe test pattern.

The second experiment on recognition is done with a database containing23 similar Chinese characters written by 40 di� erent people (HCCRBASE,provided by CCL, ITRI, Taiwan) (Tu et al., 1991). Figure 22 shows one set ofthese characters written by one person. The recognition rate obtained is 91%with 40 training samples for each character. The most often misclassi® edpairs of similar characters are (`` ’ ’ , `̀ ’ ’ ) and ( `̀ ’ ’ , `̀ ’ ’ ). For (`̀ ’ ’ ,`̀ ’ ’ ), the most important feature values to tell them apart are the valuesin the LENGTH group. However, their fuzzy values are almost the samebecause of the symmetric property in membership functions, as shown inFigure 23. To solve this problem, more delicate fuzzy membership functionsneed to be designed for this group. For (`` ’ ’ , `̀ ’ ’ ), the features in theSTROKE_TYPE group play the most important role. As shown in Figure24, the misclassi® cation is due to the slow decreasing edge of the trapezoidalmembership functions for STROKE_TYPE. To improve the result, modify-ing the fuzzy membership functions to be fast decreasing might be a goodidea. However, in some cases, they cannot be recognized apart even byhuman, and a semantic analysis on the context might be the only way tosolve the problem.


FIGURE 22. The 23 similar characters.

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Yamamoto and Rosenfeld (1982) developed a relaxation method forrecognizing hand-printed Kanji characters. The system was tested with32 training patterns and ® ve test patterns. The recognition rate is about98%. However, this system can only be used for Kanji characters, whichare less complicated than Chinese characters. Leung et al. (1987) proposeda knowledge-based stroke-matching method for Chinese character recog-nition. It incorporates speci® c knowledge about the Chinese characters intothe training system to remove inconsistencies to speed up the matchingprocess. About 95.3% was obtained for recognition rate with 32 trainingpatterns and ® ve test patterns. However, these methods employ relaxationmatching algorithms and are too much computation-demanding to be ofpractical use.


FIGURE 23. Misclassi® cation due to stroke length.

FIGURE 24. Misclassi® cation due to stroke slope.

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Chou et al. (1997) proposed a Chinese character classi® cation system.A character is characterized by string representation using peripheral andglobal feature vectors. Peripheral features include the structure segments intop, bottom, left, and right directions. Global features include the numberof horizontal segments in the top direction and bottom direction, and thenumber of stroke segments in a character. However, this system can onlyreduce the number of candidate characters. It was shown that for a set of5401 candidate characters, about 97% on average in the system can reducethe number of candidate characters to 40 for a given input character.However, it is not clear whether the correct candidate gets the best score inthe reduced candidate set. Also, picking up the best candidate from thereduced candidate set must be done by another system which might not beless sophisticated than the original system.

Hao et al. (1997) proposed a network integration method based on super-vised learning. The recognition rate obtained is about 90% for an excellentsample set. However, the system used a multilayered perceptron to learnnonlinear class boundaries. The back-propagation learning algorithm itused is time-consuming, since it takes a large number of iterations for thetraining data to converge to the stable state (Lippman, 1989; Wu et al., 1997).Chiu and Tseng (1997) used fuzzy min-max neural networks (Simpson, 1992)to classify the ring-data vectors that are extracted from thinned or non-thinned characters. The recognition rate is between 88% and 94% forsingle-writer characters, and is between 27% and 58% for multiwritercharacters. Apparently, the recognition rate is low for multiwriter characters.Also, supervised learning is used and nodes cannot be added dynamicallywhen constructing min-max neural networks. The user has to construct thenetworks by trial-and-error and this may cause an annoying burden on theuser.

The values of parameters used in our system are set by experiments.These values cannot be set permanently and should be di� erent with di� erentwriting styles and writing tools. For example, the words written with hairpencils are quite di� erent from the words written with pens. Also, di� erentwriting styles may lead to di� erent shapes of strokes. Other systems also usepredesigned parameters and the values of these parameters are set by experi-ments too. For example, in Chiu and Tseng’s system (1997), one parameter,the ring width w, was set between 70% and 90% by experiments, and anotherparameter, the growth parameter ¶, was also set accordingly.

OTHER RELATED WORK

Chuang and Tseng (1995) proposed a reduced special interval graph(RSIG) for representation of an image pattern. The method allows the


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enlargement of the scope of vision and the processing of a much greater zoneof the image, to help the extraction of the features of the image. However,feature extraction cannot be done automatically and some knowledge aboutthe structure of Chinese characters should be incorporated. Also, the methodmay lead to thick strokes. Chang and Yang (1999) provided a methodfor extracting strokes from handwritten Chinese characters. In thismethod, di� erent techniques, such as gradient-oriented tracking, curvaturesegmentation, directional analysis, and geometrical analysis, are applied.Apparently, this method is complicated and time-consuming.

Yeung and Fong (1996) proposed a fuzzy substroke extractor by com-puting a fuzzy score for every feasible substroke. The obtained edges aregrouped into clusters with an edge-clustering algorithm. Then, every possiblepartition on each cluster is considered. By choosing one partition from eachcluster, the union of the chosen partitions forms a possible output set of theextractor. Obviously, this method is computation-demanding since a lot ofcombinations should be considered. Malaviya and Peters (1997) introducedfuzziness in the de® nition of the proposed features for the on-line hand-writing recognition system, FOHRES. Many features, including positional,global, and geometrical features, are de® ned. Start-point, end-point, and pen-ups of subpatterns are required. Clearly, some of the de® ned features cannotbe applied to o� -line systems.

Many researchers (Cheng, 1998; Cheng, Hsu, & Kuo, 1993; Chou & Tsai,1991) proposed stroke relaxation matching methods for recognizing compli-cated Chinese characters rather than Kanji characters (Yamamoto &Rosenfeld, 1982). However, iterative stroke-matching algorithms are toomuch computationally intensive to be implemented. Other stroke-basedrecognition systems were also developed (Hsieh et al., 1995; Lee & Chen,1997). However, one disadvantage associated with these methods is the lackof a ¯ exible mechanism for tolerating variations. Chan and Cheung (1992)proposed fuzzy-attribute graphs (FAGs) to overcome this disadvantage. Allattributes are described by fuzzy values, and recognition of characters is doneby FAG matching. However, graph monomorphism is known to be NP-complete (Leung et al., 1987). In the worst case, exponential time is required.Also, null strokes are introduced to handle missing strokes during subgraphmatching. Moreover, automatic learning of radicals is very di� cult. Correctradicals must be extracted from di� erent characters and the process shouldbe under the supervision of a human being.

Remero, Touretzky, & Thibadeau (1997) developed a Chinese characterrecognition system using probabilistic neural networks. Statistical measuresare taken on a set of features computed from the distortion-modeledcharacters. The space of feature vectors is transformed to the optimaldiscriminant space for a nearest neighbor classi® er. However, this methodwas only applied to printed Chinese characters.

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CONCLUSION

A stroke-based neuro-fuzzy system has been described for the recognitionof handwritten Chinese characters. Stroke extraction applies a run-length±based method to extract strokes from the image of a given character. Variousfeatures of the extracted strokes are obtained by the feature extraction module.Fuzzy concepts are applied to allow the tolerance of transitional and rotationaldisplacements. Finally, an ART-based neural network, using a two-stagetraining algorithm, is used to recognize input characters.

There are options to improve our recognition system. Some of them arediscussed below.

° In our system, features in various groups are equally important. But insome cases, certain characteristics of characters are critical to correctrecognition. Therefore, it would be necessary to weigh the importance offeatures differently for different groups.

° In our system, strokes are horizontal, vertical, or slanting lines. However,Chinese characters contain curves. Representing curves by straight linesinevitably leads to errors. If curves can be described appropriately, thefeatures obtained by the stroke extraction module may be improved.

° Currently, the features extracted tend to be local. Incorporating features ofa larger scope, e.g., the location of a speci® c structure, would be bene® cialto the system performance.

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a stroke-based neuro-fuzzy system for handwritten chinese character recognition

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