A New Method for Chinese Character Strokes Recognition

doi:10.4236/ojapps.2012.23027

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Open Journal of Applied Sciences, 2012, 2, 184-187

doi:10.4236/ojapps.2012.23027 Published Online September 2012 (http://www.SciRP.org/journal/ojapps)

A New Method for Chinese Character Strokes Recognition

Yan Xu, Xiangnian Huang, Huan Chen, Huizhu Jiang

Xihua University, Chengdu, China

Email: [email protected]

Received July 6, 2012; revised August 4, 2012; accepted August 16, 2012

ABSTRACT

In this paper, the problem of stroke recognition has been studied, and the strategies and the algorithms related to the

problem are proposed or developed. Based on studying some current methods for Chinese characters strokes recog-

nition, a new method called combining trial is presented. The analysis and results of experiments showed that the

method has the advantage of high degree of steadiness.

Keywords: Chinese Character Recognition; Stroke Recognition; Stroke Combining

1. Introduction

Unconstrained handwritten Chinese character recognition

has been a difficult research area of character recognition,

and its stroke recognition is an important part of struc-

tural analysis of Chinese characters. The stroke is con-

stituted by a number of direction strokes(referred to as

strokes). Studies have shown that despite the everchang-

ing of unconstrained handwritten Chinese character,

stroke is very stable. Strokes contained in stroke belong

to the same section of a natural stroke (a written), and

generally, they are not separated in two or more natural

strokes, and this paper uses this feature to complete

stroke recognition. Although the definition of stroke var-

ied, a common feature of stroke is that it can be divided

into two categories of the basic stroke and composite

stroke. Basic stroke refers to the direction strokes, this

segment of space partition is shown in Figure 1. Strokes

are easy to determine and extract algorithm, and this arti-

cle discusses the complex stroke, posed by multiple

strokes [1]. For the Chinese character recognition system

based on structural analysis, effects of stroke recognition

have a major impact on the whole word recognition.

Those more typical stroke recognition methods have di-

rect synthesis method, dynamic programming method

and so on. Based on the analysis of these methods, this

paper presents a new method of Chinese characters

called combining trial [2].

2. Direct Synthesis

Direct synthesis method of stroke, which uses the ex-

tracted HSPN four direction strokes, directly synthesize

strokes according to the definition of given stroke. Algo-

rithm idea: set some determine conditions in the stroke

identification module ,combine the extracted strokes se-

rial number string in the same natural stroke, then com-

pose them to different types of strokes according to pre-

conditions to complete the stroke recognition. The big-

gest feature of this algorithm is simple and quick. Con-

sidering a greater deformation of unconstrained hand-

written Chinese character, the algorithm is generally

suitable for the occasion to write a more standardized,

the high limit handwritten of more restrictive writings

and printed texts.

3. Dynamic Programming

This algorithm is proposed to overcome adverse effects

of the deformation of the stroke to stroke judgment. It

uses a similar matching strategy: first, define a standard

strokes code string (standard stroke) and the distance cal-

culation rules between strings; then, take matching op-

erations between the unknown code string and all of the

defined standard code strings one by one, calculate the

distance between them ;finally, take the smallest as

matching result.

Invalid zone

Zone H

Zone P

Zone S

Zone N

Figure 1. Space division of strokes types.

Y. XU ET AL. 185

Algorithm for real: Through the gradual increasing or

reducing the length of a code string such as X, search the

code string in the standard code string set which has the

minimum distance with the unknown code string as the

results, and transform the stroke matching problem into

solving the best matching problem between two symbol

strings.

There are a lot of attached strokes through the free

writing, and strokes string to be identified is not known

in advance, then we need to take stroke splitting, and

each splitting produces a series of strokes to be knowl-

edge, so this method has multi-step iterative and volume

computation. We recognize that: 1) Long stroke is more

steady than short stroke; 2) Different types of strokes

have different possibility of confusion. Focusing on these

two differences, this paper has proposed to take “fuzzy

numbers of length” and “fuzzy numbers of direction”

treatment strategy. See the following section.

4. Combining Trial

Because there are a large number of connected strokes

that is multiple strokes connected with unconstrained

handwritten Chinese characters (belong to the same

natural strokes), and we can not ask the writer to separate

a painting to each stroke, stroke recognition includes two

processes which are stroke splitting and stroke judgment,

requiring to make a split decision from the entire word

which comes from an unknown mode and then make a

decision. These two processes complement each other,

alternately. So, we propose the combining trial to recog-

nize stroke.

The recognition process of combining trial is a tenta-

tive process of splitting and judgment constantly:

Split→Judge→Split again→Judge again …

Input: the direction strokes code sequence of a natural

stroke (string);

Output: the stroke code sequence of the natural stroke

(string);

Rule number one: Principle of giving priority to take

large;

Rule number two: Folded strokes determine the seg-

ments.

4.1. Strategy of Strokes Splitting-Increasing Test

For strokes codes sequence of a natural stroke (which

may contain multiple strokes), from the first strokes

codes, take a strokes code each time increasing to form a

test stroke code, take the dictionary matching and save

the matching results temporarily; then take the next

strokes code to form a new test stroke code for matching

operations, and so on. If you have extracted a predeter-

mined one of a few more strokes off, that is, the extrac-

tion of strokes contained in all segments is completed,

the stroke has been identified. If the strokes code of

natural stroke code does not end, then clear the data

structures which placed test stroke code (delete the test

stroke code),then take the next strokes code to re-formed

a test stroke code, and to determine the next stroke (the

natural stroke with multiple strokes), Until all of the

strokes code of natural stroke have been taken into the

match, then take the stroke which contains the largest

number of strokes as the match result and return it

(whichever is greater priority), to give priority to extract

folded stroke.

4.2. Strategy of Stroke Judgment-Similar

Matching

Precise matching technology requires that signature code

string to be identified must be equal to the stored signa-

ture code string in the feature dictionary. So, the refer-

ence template of the pattern in the dictionary must have

equivalent coverage, i.e., comprehensive, can cover the

most common deformation of the pattern. Obviously, the

high matching accuracy and the fast determination speed

are obvious advantages of the technology, and recogni-

tion performance mainly depends on the completeness of

the reference template. Because mode deformation range

can not be limited, it may have rejection (the code is not

in the library). In view of this, the establishment of a

more complete feature library is an important task [3,4].

Similarity matching, it is raised by the unpredictable

issues of deformation, it is usually take “distance” or

“similar degree” of model to be knowledge and reference

model as model criterion, and the definition of these cri-

teria varied. The system uses the strategy of accurate

identification first and then similar identification, that is,

when exact match Produces rejection, it is transferred to

produce similar matching module, to add similar identi-

fication of strokes of rejection. The string similarity,

matching techniques are varied, more typical of them are

dynamic programming method, fuzzy property law, the

error correction method and various weighted matching

method. When the stroke code uses non-equal length

strokes code string, in order to reflect these differences of

strokes in length and in type, this paper proposes fuzzy

numbers of length and fuzzy numbers of direction to deal

with them.

Take several written of folded stroke “乙”as an exam-

ple, Figure 2(a) as the standard wording, standard stroke

code is Gaca



and others are stroke variant. The

strokes code set





,,,abcd is the 4 yards direction code

of the strokes. These graphics are similar but clearly

different. The difference is that the String to be identified

has produced a distortion, and we called those strokes

code that out of standard strokes or produced a distortion

as deformed strokes. If you take exact matching and

Y. XU ET AL.

186

stroke variants are not covered by the dictionary, the

recognition algorithm will refuse to identify.

When taking similar code string matching, we con-

sider the differences of ranging symbol in length and

direction, and we take fuzzy number of the length and

direction to define the symbol distance (the distance of

strokes code):

  

ki DL

kkk

dxg

xf xxg











 (1)

where: k

X denotes the first K symbol of strokes

code string to be identified. i

G denotes the first i

s-ymbol of standard strokes code string associated

with k





x　 denotes the fuzzy number of direc-

tion associated with k

symbol.



x denotes the

fuzzy number of le-ngth associated with k

symbol. the

code string distance (stroke distance) is defined as the

sum of distance of each symbol associated with X string:





DXGdx g







(2)

where,

denotes the stroke to be knowledge;

denotes standard stroke; denotes the total number of

strokes has been matched in

(some deformed strokes

may be deleted);

For stroke matching, first, computing the matching

distance of stroke to be matched and the standard stroke

according to (1), and then take the standard stroke with

minimum distance as the judgment of stroke

to be

knowledge. Can be seen, the main task is to calculate the

fuzzy number of direction and the fuzzy

number of length , as follows.



1) Calculate :

Observation and analysis indicate that excess strokes

usually shorter than the previous one and (or) the after

one. So the fuzzy number calculation of length of the

f-irst K strokesk

in X can be defined:

(a) Standard “乙” (b) Deformed “乙” 1

Figure 2. Standard and deformed writings of folded strokes

“乙”.

 



1min ,

Len x

fx Len xLen x



 (3)

where,









0, 1

fx; If , take



fx





fx ;





Len x denotes point length of strokes

2) Calculate





The distance of two different strokes is determined by

the degree of similarity of them, so the fuzzy number

calculation of direction of the first K strokes

in X

can be defined:





fx simxg



(4)

where,





im xg denotes the similarity of strokes xk

and standard strokes i

. It can be determined by query-

ing the strokes similar table (see strokes space subdivi-

sion plans):

Sim N

































































PN

SPNF F



 

(5)

Here are two strokes (stroke code string) matching al-

gorithms and matching rules.

When taking similar match for code string, we need

first select matching starting-point of string, i.e., for code

string to be knowledge, we from which symbol to start to

match. Based on analysis of the discipline of shape varia-

tion, to consider the amount of computational algorithm

as small as possible, we design two algorithms of start-

ing-point selection method: methods of fixed starting

point and floating starting-point. Fixed starting-point

method, that is, no matter the first symbol code string to

be identified and the first standard symbol code string are

the same, we take it as the starting point to match. The

method has no problem in match Figures 2(a) and (b)

“aca” (standard code string) and “adcda” (code string to

be identified) for the first symbol of this two code string

are the same symbol “a”. However, when matching code

string (c) “cadcdad” and standard code string (a) “aca”,

there are dislocation of code string, which leads to

matching errors. so ,we need to design floating-point fast

matching method: First, compare the first symbol, if they

are equal, we chose it as a starting point, or we need cal-

culate the matching distances of the first symbol, second

symbol with the standard string, taking as a starting point

from the smaller one(to ensure the string long enough).

Starting point is chosen, then we need take string symbol

to be knowledge one by one to match with the standard

Y. XU ET AL.

187

symbol. If the two symbols are the same, continue to the

next symbol match, or calculate the matching distances

of the symbol, the next symbol with the standard string,

taking as a matching result from the smaller one. Finally,

we need calculate the matching distance of the two

strokes, and a similar matching of stroke is completed

[5].

5. Conclusion

For changes of unconstrained handwritten Chinese char-

acter, this paper presents a new Chinese character strokes

recognition method: combining trial which is based on

the structural analysis technology. The algorithm uses

increase test technology to splitting and combining strokes,

and the algorithm combines the proposed similarity

matching technique based on the fuzzy number of length

and the direction fuzzy number to complete stroke iden-

tification. The algorithm contains two processes of stroke

splitting and stroke judgment, and this two processes are

conducted alternately, i.e., we take split and composition

as well as judgment, and in accordance with the principle

of priority to take a large to extract the maximum possi-

ble compound stroke. Finally, we collect the first Chi-

nese characters of nation standard written by 500 people

and take tests. The results prove the effectiveness of the

algorithm and the accuracy of stroke extraction, which

lay a good foundation for the development of the fol-

low-up whole word recognition algorithm.

6. Acknowledgements

This work was supported by the research fund of Si-

chu-an key laboratory of intelligent network information

processing (SGXZD1002-10) and the key laboratory of

the radio signals intelligent processing (Xihua Univer-

sity) (XZD0818-09).

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