JPH05324923A - Fuzzy pattern recognizing device - Google Patents

Abstract

PURPOSE:To successively a fuzzy template corresponding to the change of an average input pattern and to automatically set the optimum fuzzy template by providing a function for suitably correcting a parameter to define a pattern for collation while referring to plural input patterns inputted from a pattern input part. CONSTITUTION:A first processor 3 recognizes a read image binarized and corrected in its shape, with a conversion part 2. Namely, the degree of recognition is calculated by comparing the inputted pattern with the pattern for collation (the fuzzy template) stored in a fuzzy template storage part 8. A second processor 12 corrects the fuzzy template based on information in a cumulative input pattern storage part 7 and sends it to the fuzzy template storage part 8. In the case of successive correction, the second processor 12 is activated when an input number counter 6 reaches a designated number.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a fuzzy pattern recognition apparatus having a function of accurately recognizing a pattern transformed from a basic pattern, such as a handwritten character for a typeface, by using a fuzzy theory.

[0002]

2. Description of the Related Art A conventional pattern recognition device generally recognizes a pattern whose shape is strictly defined.
A method using a neural network is known as a means for recognizing an ambiguous pattern that is not limited to a certain shape such as handwritten characters. In this, a neural network is formed by learning a pattern to be recognized in advance, and the input pattern is recognized thereby.

However, the method using this neural network requires a work of learning, and requires re-learning each time the number of pattern candidates to be identified increases. In addition, if the neural network is a black box and changes in the characteristics of the candidate of the pattern to be identified due to secular change of the input device that takes in the pattern signal of the recognition target, it is reflected in the operation of the device. It is difficult to do so, and there is a problem that erroneous recognition may occur.

On the other hand, in recent years, a method of recognizing an ambiguous pattern using a fuzzy theory has been proposed. This method is based on having a plurality of knowledge about the characteristics of the pattern and checking them one by one, such as "there is a vertical line near this portion". However, in the conventional pattern recognition method based on the fuzzy theory, it is necessary to acquire knowledge about pattern features (rule creation work) when developing a system including software, and pattern feature extraction is performed when actual pattern recognition is performed. An algorithm is needed. for that reason,
There is a problem that processing is complicated because many rules and membership functions of fuzzy sets are required.

[0005]

Therefore, as a solution to the problem of pattern recognition based on the conventional fuzzy theory, the inventor combines a preset fuzzy set membership function to collate it with a recognition target. We have proposed a pattern recognition method that prepares a basic pattern that should be used and compares it with the pattern to be recognized to calculate the certainty factor, which is an index for evaluating the pattern ( Japanese Patent Application No. 2-274355
issue). According to this method, a pattern to be collated with a recognition target is formed by combining membership functions indicating a fuzzy region having a boundary of an arbitrary shape (for example, an ellipse). It is possible to efficiently recognize patterns such as characters and figures without needing to do so. The matching pattern formed by combining the membership functions by this method is called a "fuzzy template", and this "fuzzy template" is also used in this specification.

However, in the above method, if the fuzzy template corresponding to the pattern to be recognized is preset, the fuzzy template will not change thereafter.
That is, changing the contour shape of the fuzzy template is not considered. Therefore, if the set fuzzy template deviates from the actual average input pattern, the matching cannot be performed with the optimum fuzzy template, and the recognition accuracy becomes low. Also,
When the average input pattern changes with the passage of time, the recognition ability cannot be guaranteed.

Therefore, in order to avoid this and obtain an optimum fuzzy template, the parameters that define the fuzzy template must be adjusted by trial and error, and the average input pattern changes with time. When it changes, it is necessary to reset the fuzzy template while monitoring the degree of certainty of recognition, which is troublesome.

Therefore, an object of the present invention is to provide a fuzzy pattern recognition apparatus capable of successively correcting a fuzzy template with respect to an average change of an input pattern and automatically setting an optimum fuzzy template.

[0009]

According to the present invention, in a device for pattern recognition based on a membership function representing a fuzzy set, a pattern input section for inputting a pattern to be recognized and one or more membership functions are generated. Then, by combining the membership functions, a matching pattern forming means for forming a matching pattern for recognizing a recognition target pattern input from the pattern input section, and a recognition target input from the pattern input section. And a calculation processing unit for calculating a certainty factor of recognition by comparing the pattern of No. 1 with the matching pattern formed by the matching pattern forming unit, and a parameter defining the matching pattern is input from the pattern input unit. Have the function to modify appropriately by referring to multiple input patterns And it features.

Further, according to another aspect of the present invention, in addition to the above configuration, the matching pattern formed by the matching pattern forming means is converted into a matching modified pattern for recognizing a pattern modified from the basic pattern. The matching pattern converting means for calculating the certainty factor of recognition by comparing the pattern input from the pattern input unit with the matching pattern formed by the matching pattern forming means when recognizing the basic pattern, When recognizing a deformed pattern from a pattern, an arithmetic processing means for calculating a certainty factor of recognition by comparing with the deformed pattern for matching converted by the matching pattern conversion means is provided. The conversion parameters of the above can be adjusted by referring to the plurality of transformation patterns input from the pattern input unit. It characterized by having a modified and go function.

Further, according to another aspect of the present invention, a pattern input section for inputting a pattern to be recognized and one or more of the membership functions are generated, and the membership functions are combined so that the pattern input section is operated. A collation pattern forming means for forming a collation pattern for recognizing an input recognition target pattern; and a collation pattern formed by the collation pattern forming means for the recognition target pattern input from the pattern input section. Computation processing means for calculating the certainty factor of recognition by comparing,
The parameter that defines the matching pattern is appropriately corrected by referring to the pattern input from the pattern input unit, and is executed for the pattern input as a sample to optimize the matching pattern. It is characterized by performing conversion.

[0012]

According to the present invention, a collation pattern (fuzzy template) for recognizing a pattern to be recognized by generating a membership function representing a fuzzy set and combining the membership functions in the collation pattern forming means.
However, the matching pattern is sequentially corrected by referring to the plurality of input patterns input from the pattern input unit and correcting the parameters that define the fuzzy template. Alternatively, the optimum matching pattern is set by executing the parameter modification on the pattern input as the sample.

[0013]

1 is a block diagram showing an embodiment of a fuzzy pattern recognition apparatus according to the present invention. This device
It is configured to recognize characters and consists of the following components.

The image reading device 1 includes a light source for illuminating a recognition target in order to read an image of a typeface or handwritten characters,
A photoelectric converter that reads an image by reflected light of the image and converts the image into an electric signal is included. Alternatively, a pressure-sensitive handwritten character input device may be used.

The signal converter 2 converts the image signal read by the image reading device 1 into a binary signal of, for example, "0" (= "white") or "1" (= "black"), and , Is configured to correct the size of the read image. This correction (scaling) extends the shape up and down, for example, when the character has a shape crushed up and down.

The image reading apparatus 1 and the signal conversion section 2 constitute a pattern input section for inputting a pattern to be recognized.

The first processor 3 recognizes the read image which has been binarized and whose shape has been corrected by the signal converter 2. That is, the certainty factor of recognition is calculated by comparing the input pattern with a matching pattern (fuzzy template) stored in a fuzzy template storage unit 8 described later. Further, the pattern is classified into the pattern with the highest certainty as compared with other patterns with ambiguous boundaries, and the recognition result is output.

The pattern indicating the recognition result by the processor 3 is converted from a binarized signal to an analog signal by the signal conversion unit 4 and output by the output device 5 such as a CRT.

The input number counter 6 counts the number of input patterns read by the image reading device 1 and binarized by the signal conversion unit 2.

In the input pattern cumulative storage unit 7, the read image binarized as described later (for example, the pattern of the numeral "3") is displayed as shown in FIGS. 11 (a), 11 (b) and 11 (c). , The number of dots containing one of the binary signals (for example, “1” of {0, 1}) for each input is calculated as follows.
It is accumulated and stored as shown in 1 (d).

The fuzzy template storage unit 8 stores a fuzzy template serving as a reference for recognizing an input pattern. The shape of the fuzzy template stored in the storage unit 8 and the input pattern stored in the input pattern accumulation storage unit 7 are respectively passed through a CRT used as a terminal device of a computer and a man-machine interface 9 such as a keyboard. It can be adjusted and changed.

This man-machine interface 9 corrects the fuzzy template in the case where the recognition result is erroneous at the time of the optimization of the fuzzy template, which will be described later, in addition to the manual adjustment and modification of the fuzzy template by the operator. Also, it is used to change the number of inputs designated by the input number counter 6.

The three-dimensional membership function generator 10 is
Generate the membership functions needed to form the fuzzy template above.

The fuzzy template storage unit 8, the man-machine interface 9 and the three-dimensional membership function generator 10 constitute a collation pattern forming means.

The fuzzy template optimization algorithm storage unit 11 stores an algorithm for correcting the fuzzy template to an optimum one as described later.

The second processor 12 modifies the fuzzy template based on the information in the input pattern cumulative storage unit 7 and sends it to the fuzzy template storage unit 8. here,
In the case of the sequential correction, the second processor 12 is activated when the input number counter 6 reaches the designated number. Further, in the case of optimizing the fuzzy template by using a reference input pattern described later, the second processor 12 is activated when all the reference patterns have been input.

In the above description, two processors (the first processor 3 and the second processor 12) are used as the arithmetic processing means, but in an actual device (hardware), one CPU is used. The (main processor) 13 can realize the functions of the above two processors.

Next, the principle and method of pattern recognition according to the embodiment will be described.

First, one or more three-dimensional membership functions are combined to create a fuzzy template corresponding to the pattern to be recognized. The three-dimensional membership function used for this purpose will be described with reference to FIGS.

(A) Setting of membership function For example, consider a membership function having an elliptic equi-fitting line as shown in FIG. 2 in a three-dimensional space consisting of xy rectangular coordinates and goodness of fit. Here, the symbols in the figure are defined as follows. For simplicity, the origin is the reference point (the center point for describing the 3D membership function most easily).
And

F (x, y, R _x , R _y ) = 0: a function giving the elliptical shape of the lines of equal fitness t _x = f (x): xt _x -bell-shaped membership function t _y = g (y): Bell-shaped membership function in the yt _y plane R _x : _x of the elliptic iso-fitting line with a fitness of 1
Radius in the axial direction R _y : _y of the elliptic iso-fitting line with goodness of fit 1
Axial radius a _x : Parameter that is proportional to fuzzy entropy about _x a _y : Parameter that is proportional to fuzzy entropy about y r _x : Consistency line including any point (x, y) and goodness of fit with equal fitness line 1, distance on xt _x cross r _y: arbitrary point (x, y) of an equal fitness line fitness 1 with an equal fit lines including, on yt _y cross section Distance t: Goodness of fit of the point (x, y) given by the three-dimensional membership function For convenience of description, the goodness of fit t is divided into t _x and t _y .
t, t _x and t _y are virtually the same coordinate axes.

At this time, the iso-fitting line including an arbitrary point (x, y) is as follows.

[0033]

[Equation 1]

The membership functions for t _x and t _y are

[0035]

[Equation 2]

[0036]

[Equation 3]

Is represented by Here, the double sign − is x, y
Indicates a positive side portion, and + indicates x and y negative side portions.

On the xt _x cross section and the yt _y cross section, the distances between the equal conformity line of interest and the reference point are R _x + r _x and R _y + r, respectively.
_y , so

[0039]

[Equation 4]

[0040]

[Equation 5]

Therefore, from these

[0042]

[Equation 6]

At this time, unless the insides of the denominators of the first term and the second term have the same shape, it cannot be transformed into an explicit function for t. _{Therefore, min (R x / a x} , R y / a y) = s k seek ... (7) to select the value of the appropriate s from a range of 0 <s <s _k,

[0044]

[Equation 7]

_Then , d _x and d _y are obtained. That _{d x = R x -a x s} ···· (9) d y = R y -a y s seeking (10), deforms the function equal fit line as follows.

[0046]

[Equation 8]

Here, the double sign − is a portion where x and y are on the positive side,
+ Represents the part where x and y are on the negative side.

[0048]

[Equation 9]

The geometrical meaning of this equation is that it has a shape approximated by adding a straight line portion to the original elliptical shape, as shown in FIG. From this formula

[0050]

[Equation 10]

However, when | x | <d _x , x = d _x , and when | y | <d _y , y = d _y . Further, as shown in FIG. 2, when the equi-fitness line of interest is outside the ellipse of the fitness 1,
The value in {} is a positive value.

In the same manner, the same formula can be obtained even when the target equi-fitness line is inside the ellipse of the goodness of fit 1. However, in this case, if any point (x, y) exists in the straight line part of the approximate equi-fitting line, | x | <d _x and | y | <d _y
And becomes a positive value in {}, and when it is in the ellipse part
The value inside {} is a negative value.

When the inside (or outside) of the ellipse having the goodness of fit 1 is a region having the goodness of fit 1 uniformly, the value is given by judging according to the above conditions.

As an example, the fuzzy template represented by the three-dimensional membership function for the character figure "8" as shown in FIG. 4A is as follows from the equation (14).

When the range of the goodness of fit of the three-dimensional elliptic membership function with the goodness of fit being a circle having the center point of (6, 14) and the radius of 3.5 is [-1, 1] (Fig. 5)

[0056]

[Equation 11]

Extending the range of goodness of fit of a three-dimensional elliptic membership function with a goodness of fit of a circle with a center point of (6, 6) and a radius of 4.5 to [-1, 1] (Fig. 6)

[0058]

[Equation 12]

As shown in FIG. 7 (c), the following function created by combining the above-mentioned two three-dimensional elliptic membership functions is used as a fuzzy template three-dimensional membership function for the above-mentioned character figure "8". And

T = max (t ₁ , t ₂ ) ... (17) Here, the reason why the range of the goodness of fit is expanded to [-1, 1] is − for improper input coordinate values. This is because a (minus) evaluation value is given, so that, for example, when a completely black image signal is input, the total sum of the degree of conformity described later becomes a small value.

(B) Conversion of image input The image reading apparatus 1 reads an input image, and the signal conversion unit 2 converts it into a binary signal including size correction.

Explaining with the example shown in FIG. 4 (a), in this case, as shown in FIG. 4 (b), there are 12 regions including the character "8" in the x-axis direction and 19 regions in the y-axis direction. It is divided into individual areas, and each area is converted into a binary signal corresponding to the character shape.
Here, a blank area is converted into a binary signal 0, and a black area is converted into a binary signal 1. As the size correction, the second black areas from the upper, lower, left, and right ends are placed in the second column from the upper, lower, left, and right ends, respectively.

At this time, as shown in FIG. 7B, 12 × 19
= 1 of binary signal in n = 35 areas of = 228 areas
(Black) is given.

(C) Calculation of fitness of input image For membership function t set in (A), 1 out of n black areas (binary signal (0, 1)) obtained in (B) Then, the sum T ₁ of the goodness of fit of dots) and the average goodness of fit S ₁ = T ₁ / n per region are obtained.

In the above example, as shown in FIGS.
n = total sum of goodness of fit calculated by coordinate values of 35 black areas T ₁ = 28, average goodness of fit per area S ₁ = 28/35
= 0.8.

(D) Similarity calculation of ideal shape (C) Similar to (C), the sum T ₂ of the goodness of fit and the average goodness of fit S ₂ when a figure of the most ideal shape is input.

The most ideal case of "8" is shown in FIG. 7 (d).
As shown in (c), the sum T ₂ = 3 of the goodness of fit of 40 black regions
8, the average goodness of fit per region S ₂ = 38/40 = 0.95.

(E) Calculation of certainty factor From the results of (C) and (D), the ratio T ₁ / T ₂ of the sum of the goodness of fit and the ratio S ₁ / S ₂ of the average goodness of fit are added and divided by 2. Thus, the certainty factor is calculated.

In the above example, the certainty factor is

[0070]

[Equation 13]

It becomes

The ratio T ₁ / T ₂ of the total sum of the goodness of fit exceeds 1 in some cases, but in that case, it is unconditionally set to 1. That is, min (T ₁ / T ₂ , 1).

As described above, the fuzzy template formed by combining the three-dimensional membership functions generated by the three-dimensional membership function generating device 10 of FIG. 1 is stored in the storage device 8 and is used for the recognition operation. 1 by the processor 3. That is, when recognizing a typeface, it is directly sent to the main processor 3, while when recognizing a handwritten character, it is sent to the algorithm storage unit 7 and converted into a fuzzy template for handwritten character recognition, which is then sent to the main processor 3. Supplied. Hereinafter, this operation will be described with reference to FIGS. 8 and 9.

By the way, handwritten characters such as numbers and alphabets such as alphabets tend to incline to the right rather than typefaces, and handwritten characters such as hiragana, katakana, and kanji tend to move to the right above typefaces. Therefore, when recognizing the numbers or alphabets of handwritten characters, a fuzzy template of a typeface rectangle [0 to x _d , 0 to y _d ] as shown in FIG. As shown in FIG. 8C, the fuzzy template is converted to the right by the angle ψ.
The scaling (reduction) in the horizontal (x) direction is performed so as to fit within the rectangular area [0 to x _d , 0 to y _d ] as shown in FIG.

Describing this operation by a mathematical expression, when the fuzzy template of the typeface is expressed by the equation (19), the fuzzy template tilted to the right by the angle ψ can be expressed by the equation (20), and the lateral scaling ( The fuzzy template (including correction of the center position) can be expressed by equation (21).

T = f (x, y) (19) t = f (x ', y) (20) where x' = x-ytan ψ

[0077]

[Equation 14]

When recognizing handwritten hiragana, katakana, and kanji characters, a fuzzy template of a typeface rectangle [0 to x _d , 0 to y _d ] is displayed as shown in FIG. 9 (a). As shown in FIG. 9 (b), it is converted into a fuzzy template that is inclined upward by an angle ψ, and then, as shown in FIG. 9 (c), it is placed in a rectangular area [0 to x _d , 0 to y _d ]. Scaling (reduction) in the vertical (y) direction is performed.

Describing this operation by a mathematical expression, when the fuzzy template of the typeface is expressed by the expression (22), the fuzzy template rising to the right by the angle ψ can be expressed by the expression (23), and the vertical scaling ( The fuzzy template including the correction of the center position can be expressed by the equation (24).

T = f (x, y) (22) t = f (x, y ') (23) where y' = y-xtan ψ

[0081]

[Equation 15]

As an example, FIG. 10B shows a case where the fuzzy template of the typeface "8" shown in FIG. 10A is converted into a fuzzy template for handwritten numeral recognition.

The display method of such a fuzzy template is as follows. First, the base set plane of the three-dimensional membership function (coordinate plane formed by two coordinate axes other than the coordinate axis indicating the goodness of fit) is divided into a finite number of dots (points of a certain size), and the center of each dot is divided. The goodness of fit of the three-dimensional membership function obtained by the coordinate values of the points is calculated. The calculation of the goodness of fit is performed by the arithmetic processing means based on the stored function formula of the three-dimensional membership function and the parameters. When the goodness of fit is obtained, a black circle having a radius proportional to the size of the goodness of fit for each dot is displayed in each dot. As a result, a place with a high degree of conformity is displayed in black, and a place with a low degree of conformity is displayed in white.

When recognizing a typeface, the first processor 3 compares the typeface with a fuzzy template for recognizing the typeface read from the fuzzy template storage unit 8 to obtain a certainty factor as an evaluation value of the typeface. Is calculated and output, and when recognizing a handwritten character, the certainty factor is calculated and output as the evaluation value of the handwritten character by comparing with the fuzzy template for handwritten character recognition converted as described above. ..

According to the present invention, in order to successively correct or optimize the fuzzy template set as described above, the parameter to be modified is selected in advance from the parameters defining the fuzzy template. As an example, let the selected parameter be "Pi".

Hereinafter, the second processor 1 of the embodiment shown in FIG.
The correction operation executed by 2 will be described.

(1) The above-mentioned parameter Pi is changed in several ways as follows to create a fuzzy template.

Pi −2ΔPi, Pi −ΔPi, Pi, P
i + ΔPi, Pi + 2ΔPi (where ΔPi = kPi, k = 0.05 to 0.1) (2) Each fuzzy template created in (1) and input as the accumulation of a plurality of input patterns as shown in FIG. The data stored in the pattern accumulation storage unit 7 is collated. Note that "0" is assigned to the blank portion of each dot in FIG. 11 (not shown).

The matching is performed by comparing the value of each dot assigned by the fuzzy template with the input pattern cumulative storage unit 7
The value obtained by calculating the sum of the product of each dot and the value of each dot is set as the evaluation value.

(3) When the evaluation values are obtained in the procedure of (2) for each fuzzy template created in (1), the parameter values that maximize those evaluation values are used as the new Pi parameter values. , Change registration.

As an actual example, an example of modifying the parameter ψ of the handwriting character mode conversion described above will be shown.

First, a fuzzy template of a character type "3" as shown in FIG. 12 (a) is created. in this case,
ψ = 0 °.

As the on-line sequential correction, as shown in FIGS. 11 (a) to 11 (c), the correction is made every time three input patterns of "3" are entered, so that the input number counter 6 is set to "3". Is specified.

[Example 1] Assume that three handwritten character patterns as shown in FIGS. 13A to 13C are recognized.

(1) The parameter ψ is ψ = -8 °, -4
Create a fuzzy template by changing to 5 types of °, 0 °, 4 °, and 8 °.

(2) The five fuzzy templates created in (1) are collated with the data of the input pattern cumulative storage unit 7 obtained by the three patterns of FIG.

(3) ψ = -8 °, -4 °, 0 °, 4 °,
Of each 8 ° fuzzy template, the value of ψ that maximizes the evaluation value obtained in (2) is registered as a new ψ value. In the example of FIG. 13, ψ = 4 ° is the maximum, and the fuzzy template is corrected to the shape shown in FIG.

(4) The input number counter 6 is reset.
The designated number 3 for the sequential correction remains unchanged.

[Example 2] Assume that three handwritten character patterns as shown in FIGS. 14A to 14C are recognized.

(1) The parameter ψ is ψ = -4 °, 0
Create a fuzzy template by changing to 5 types of °, 4 °, 8 ° and 12 °.

(2) The five fuzzy templates created in (1) are collated with the data of the input pattern cumulative storage unit 7 obtained by the three patterns of FIG.

(3) Among the fuzzy templates of ψ = −4 °, 0 °, 8 °, 12 °, the value of ψ that maximizes the evaluation value obtained in (2) is set as a new value of ψ. register. In the example of FIG. 14, ψ = 12 ° is the maximum, and the fuzzy template is corrected to the form shown in FIG.

(4) The input number counter 6 is reset (the designated number 3 for successive correction is unchanged).

Although the embodiments have been described above, the present invention is not limited to the case of recognizing characters, but can be applied to the recognition of patterns in which an average input pattern changes with time.

[0105]

Since the fuzzy pattern recognition apparatus of the present invention is configured as described above, even if the average input pattern changes with the passage of time, the recognition accuracy can be improved by sequentially correcting the fuzzy template. Can be maintained. Further, when the input pattern example for reference is obtained, the fuzzy template can be optimized off-line, so that the reliability of pattern recognition is improved.

Further, even when recognizing a deformed pattern such as a handwritten character from the basic pattern, the fuzzy template for recognizing the deformed pattern is modified, so that the certainty factor of recognizing the handwritten character can be further improved. it can.

[Brief description of drawings]

FIG. 1 is a block diagram showing an embodiment of a fuzzy pattern recognition device according to the present invention.

FIG. 2 is a diagram showing an example of a three-dimensional membership function used in the fuzzy pattern recognition device of FIG.

FIG. 3 is a diagram showing a case where a straight line is added to the three-dimensional membership function of FIG.

FIG. 4 is a diagram showing an example of a character figure identified in the embodiment of the present invention and a binary signal corresponding to the shape.

FIG. 5 is a diagram showing the shape of a three-dimensional membership function used in the embodiment.

FIG. 6 is a diagram showing the shape of another three-dimensional membership function used in the embodiment.

FIG. 7 is an explanatory diagram of a procedure for identifying the character graphic of FIG.

FIG. 8 is a diagram showing an operation of converting a fuzzy template of a typeface into a fuzzy template for recognizing handwritten characters when recognizing handwritten characters such as numbers and European characters.

FIG. 9 is a diagram showing an operation of converting a fuzzy template of a typeface into a fuzzy template for handwritten character recognition when recognizing a kana or kanji of a handwritten character.

FIG. 10 is a diagram showing an example of conversion of a handwritten numeral “8” into a fuzzy template for recognition.

FIG. 11 is a diagram showing an example of accumulating and storing input patterns.

FIG. 12 is a diagram showing an example of a fuzzy template according to the present invention before and after modification.

FIG. 13 is a diagram showing an example of an input pattern of handwritten characters.

FIG. 14 is a diagram showing another example of an input pattern of handwritten characters.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Image reading device, 2 ... Signal conversion part, 3 ... 1st processor, 4 ... Signal conversion part, 5 ... Output device, 6 ... Input number counter, 7 ... Input pattern accumulation storage part, 8 ... Fuzzy template storage part , 9 ... Man-machine interface, 10
... three-dimensional membership function generator, 11 ... fuzzy template optimization algorithm storage unit, 12 ... second processor, 13 ... CPU.

Claims (3)

[Claims] 1. An apparatus for pattern recognition based on a membership function representing a fuzzy set, a pattern input section for inputting a pattern to be recognized, and one or more of the membership functions are generated and the membership functions are combined. As a result, a matching pattern forming unit that forms a matching pattern for recognizing a recognition target pattern input from the pattern input unit, and a recognition target pattern input from the pattern input unit by the matching pattern formation unit And a calculation processing unit that calculates a certainty factor of recognition by comparing the pattern with the matching pattern formed in 1., referring to a plurality of input patterns input from the pattern input unit for parameters defining the matching pattern. A fuzzy pattern characterized by having a function of appropriately modifying by performing Emissions recognition device. 2. An apparatus for performing pattern recognition based on a membership function representing a fuzzy set, a pattern input section for inputting a pattern to be recognized, and one or more of the membership functions are generated and the membership functions are combined. By this, the matching pattern forming means for forming a matching pattern for recognizing the basic pattern of the recognition target input from the pattern input section, and the matching pattern formed by the matching pattern forming means, A collation pattern converting means for converting the transformed pattern into a collation deformation pattern for recognizing the pattern, and a collation pattern formed by the collation pattern forming means for recognizing the basic pattern. The recognition certainty factor is calculated by comparing with the When recognizing a deformed pattern from this pattern, it is provided with arithmetic processing means for calculating the certainty factor of recognition by comparing with the deformed pattern for matching converted by the matching pattern conversion means, and transforming the basic pattern for matching. A fuzzy pattern recognition device having a function of appropriately correcting a parameter for conversion into a pattern by referring to a plurality of modified patterns input from the pattern input unit. 3. An apparatus for pattern recognition based on a membership function representing a fuzzy set, a pattern input section for inputting a pattern to be recognized, and one or more membership functions generated and combined with the membership function. As a result, a matching pattern forming unit that forms a matching pattern for recognizing a recognition target pattern input from the pattern input unit, and a recognition target pattern input from the pattern input unit by the matching pattern formation unit And a calculation processing unit that calculates a certainty factor of recognition by comparing the pattern for collation formed by, by referring to the pattern input from the pattern input unit for the parameter that defines the pattern for collation. The function to correct as appropriate is implemented for the pattern input as a sample. And fuzzy pattern recognition apparatus characterized by the optimization of the collation pattern.