JPS6227883A - Pattern recognizing device - Google Patents

Abstract

PURPOSE:To have a learning function at the time of recognization by having the forward direction for the transition of a direction and executing the correction of a standard order logic by the transition in the forward direction. CONSTITUTION:By inputting the standard order logic, the order logic which comes to be a sample which neglects a noise such as a digitalized noise, from a console, a floppy disk, etc., the preservation of a necessary information is executed, especially, the direction to shown the characteristic of the stroke of a pattern from the transition between conditions in the standard order logic is defined, the learning method can be ruled. For example, when the code is 3 in a condition A1, the transition can be executed to a condition A1, when the code is 2, transition can be executed to a condition A2, and in case of others, the transition cannot be executed. Here, when the condition is started from a condition S and arrives at a condition E in accordance with the permitted transition, a pattern '7' and the order logic are collated with each other.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a pattern recognition device. This paper relates to matching with sequential logic and identification processing using identification functions.

The present invention also relates to a pattern recognition device having a learning function.

The present invention relates to automatic creation of a dictionary when developing OCR.

The present invention particularly relates to a pattern recognition device having a learning function, which includes preprocessing such as pattern size normalization processing, thinning processing, and Cheno coding, and major classification processing using nine topological features. G
related to things.

[Prior art]

BACKGROUND ART Many studies have been conducted on character recognition, and various recognition methods and devices have been put into practical use. but.

Development such as creating a dictionary is difficult; 1. It was also difficult to eliminate misreading. However, since there is no learning opportunity, similar misreadings may be repeated, leading to a decrease in recognition rate and efficiency in using the device, which is one of the reasons why recognition devices are difficult to spread.

Further, as one of the recognition methods, for example, there is a method of analyzing the structure of the strokes of a pattern.

Compared to other recognition methods such as the similarity method, this method has the advantage of obtaining a higher recognition rate, but has the disadvantage that it is difficult and time-consuming to create a dictionary. Therefore, automatic dictionary creation for the structural analysis method is important in terms of recognition rate, and research on learning functions is required.

Although the known technology and device are not related to the learning function,
For example, Hitachi, Ltd.'s handwritten character recognition device (H-8
959OCR), which performs the following processing on character patterns. In other words, convert the character pattern into a line shape,
It is expressed by a direction code called a chain code, and based on that code, it is identified by comparing it with the order logic of a standard pattern that is a dictionary. In addition, a restriction is placed on the number of times the state transition state is passed, thereby improving the recognition rate.

However, depending on the character, the starting point of the chain code cannot be uniquely determined, and there are many things that cannot be expressed using a single sequential logic.For example, it is said that approximately 100 sequential logics are required for handwritten digits. The disadvantage is that it takes a lot of time to create and recognize.

As is clear from the above, the creation of a dictionary with a learning function in character pattern recognition.

Currently, research on learning during recognition is desired.

[Problem that the invention seeks to solve]

As is clear from the above points, an object of the present invention is to eliminate the above drawbacks.

Another object of the present invention is to provide a learning function in the development of OCR or the creation of a dictionary during pattern recognition.

Another object of the present invention is to provide a pattern recognition device with a learning function during recognition.

Another object of the present invention is to provide a pattern recognition device that incorporates the idea of sequential logic (automata) into the stroke structure analysis method and further performs a classification process using a classification function.

An object of the present invention is to automatically create a dictionary for online or offline character recognition.

An object of the present invention is to create a chain code for dictionary creation based on a chain code for pattern recognition.

An object of the present invention is to modify the sequential logic, which is a dictionary, based on the chain code, and to learn better sequential logic.

Another object of the present invention is to create a discriminant function whose variable is the number of times each state of sequential logic, which is dictionary data, is passed.

[Means for solving problems]

As a means to solve this problem, for example, the pattern input device shown in the block diagram of FIG.
Preprocessing is performed in the preprocessing working area of , and the dictionary information is corrected and created in the memory 6 and transferred to the memory 7. The memory 7 stores the sequential logic and the address of the discriminant function used in pair with the sequential logic.

Memory 8 is a part that stores identification functions.

[For production]

In the configuration shown in FIG. 1, the CPU 1 is as follows.

Execute the program stored in memory 4.

The image information read from the scanner 2 is preprocessed in the memory 5, either manually from the console 3 or from an external memory. The basic sequential logic from the optical disk or floppy disk is modified by the preprocessed data and a discriminant function is created.

〔Example〕

One embodiment of the present invention will be described below.

FIG. 1 is a block diagram of an apparatus for implementing the present invention. 4 is a memory in which a program for carrying out the present invention is stored.

Reference numeral 1 denotes a CPU, which controls and operates other parts of the device according to a program stored in a memory 4.

Reference numeral 2 denotes a scanner, which converts image data into information that can be processed by a computer using optical reading means.

Reference numeral 3 denotes a console, which consists of a keyboard and a display, and includes means for interfacing with the CPU, such as data input.

A preprocessing memory 5 is a memory area for storing image data read by the scanner 2 and performing preprocessing such as image processing and coding on the image data.

Reference numeral 6 denotes a memory for creating a dictionary, which stores basic sequential logic input from the console 3 or a floppy disk. Then, based on the image data preprocessed in the memory 5, the standard sequential logic input from the console 3 (or it may be read out from an external memory such as a floppy disk), and the discriminant function Create dictionaries I, I
Create I.

Reference numeral 7 denotes a memory for storing the sequential logic that is part of the dictionary, and stores the sequential logic corrected in the memory 6 and the address of the discriminant function used in pair with it, and stores the sequential logic corrected in the memory 6 and stores the address of the discriminant function that is used in pairs with it.
).

Reference numeral 8 denotes a memory that stores the discrimination function, which is another part of the dictionary.Memory 61 stores the created discrimination function and the recognition result that is discriminated using this, and stores the remaining part of the dictionary for recognition. (Dictionary II).

[Operation flow diagram]

FIG. 2 is a flowchart showing the operation of the present invention.

The operation will be explained in more detail using FIG.

The flow of operations will be explained below.

In this embodiment, patterns belonging to the same category are used intensively, a dictionary is created for that pattern, and then dictionaries are created for patterns belonging to other categories.

First, standard sequential logic regarding patterns belonging to the same category is retrieved from the aforementioned console 3 or an external storage such as a floppy and stored in the memory 6 (step 5,
6) Next, in the image input process of step 1, a process of cutting out one pattern to be the target of pattern recognition from the image read by the scanner 2, which is an image league such as a digital copying machine, hand scanner, word processor, facsimile, etc. In the preprocessing of step 2, binarization processing, size normalization processing, thinning processing, and check 7.
It performs preprocessing that converts image data into encoded data suitable for CPU processing and pattern recognition processing.

The above-mentioned image input processing and pre-processing are processed in the working area of the memory 5, and the Che7-encoded image is stored.

[Che 7 code] An example of Che7 encoding is shown in FIG.

21 is an 8-way code, which expresses the relationship between adjacent pixels by 1
It is expressed using chords from 8 to 8. The image 20, which has been thinned so that the line width is one pixel, is subjected to CH7 coding from pixel S to pixel E using an eight-way code 21, resulting in a chain code 22.

Since the image 20 can obviously be reproduced from the Che7 code 22, the Che7 code 22 stores image information.

The memory 7 stores pattern names corresponding to the data of the previously created sequential logic.

Note that the Che7 code 22 is obtained by orienting adjacent pixels from the start point S to the end point E of the image 20 in FIG.
The direction from pixel to the next pixel is 3, the next pixel is 2, and so on.

In this embodiment, the creation and identification of a dictionary regarding standard logic rA
The creation of a dictionary regarding numbers shall be carried out sequentially, step 1
The image input processing in step 2 and the preprocessing in step 2 are executed at the stage of creating dictionaries for both.

In the process determination process of step 3, a flag (discrimination function creation if flag (F)=1, which will be described later) is checked to determine whether it is a sequential logic creation stage or a discriminant function creation stage. If the step of creating a sequential logic (F=0), proceed to Y. In the step of creating a sequential logic, the next step is to modify the sequential logic in step 4.
Modification of the sequential logic in step 4 involves storing the standard sequential logic in step 5, which was input from the console corresponding to the category, into the sequential logic memory 6 in step 6, using actual data, and following the learning rules. , modify practical sequential logic one after another, that is, modify and modify.

[Modification of sequential logic]

The sequential logic modification process will be described in detail below.

When learning and creating sequential logic.

l) Preservation of necessary information 2) It is necessary to establish rules for the learning method, and for this purpose, in the present invention, standard sequential logic, which is a sample sequential logic that ignores noise such as digitization noise, is stored on a console, floppy disk, etc. By inputting from , the necessary information is stored, and in particular, the direction that represents the characteristics of the stroke of the pattern is defined from the transition between states in standard sequential logic, and this makes it possible to create rules for the learning method. It has characteristics.

A transition diagram of the standard sequential logic of pattern "7" is shown in FIG.

In FIG. 4, AI, A2. ...A5 is a state, and the code attached to the arrow gives the transition conditions between states.

For example, in state AI, it is possible to transition to state aAx when the code is 3, and to state jlA2 when the code is 2,
Otherwise, the transition is not possible (i.e., no matching,
) Here, when starting from state S and reaching state E according to the allowed transitions, pattern "7" and the sequential logic match.

An example of a Cheno code obtained by input processing and preprocessing an image of the actual pattern "7" is 677 E. Since the Cheno code has been described above, a description thereof will be omitted.

This code can transition from state S to state A5 in the standard state transition diagram shown in FIG.
Since transition to code 6 is not allowed, no matching will be performed. The purpose of modifying the sequential logic in step 4 is to modify the state transition diagram to match this.

Definitions of directions are illustrated in FIGS. 5-1 and 5-2.

The forward direction of state B in FIG. 5-1 is determined by the transition conditions for states A and C, that is, the arrangement of direction codes 8 and 7 in FIG. The rotation direction when taking a shortcut from to 7 is the forward direction, and the reverse direction is the reverse direction. Similarly for transition states.

Due to the transition conditions for states C and D, that is, the arrangement of codes 1 and 2, the arrow in FIG. 5-2 is in the forward direction.

[Correction and repair of sequential logic]

FIG. 6 shows a flowchart of the sequential logic correction process.

A detailed explanation of the sequential logic correction process will be given based on FIG. Step 30 is a process of converting the cheno chord. In order to simplify the subsequent processing, the cheno chord obtained in the preprocessing of step 2 is converted into a chord string representing only changes. Note that the original cheno chord is also used backwards for pattern recognition.

For example, the code conversion is cheno code “533321
1118766776777677E'' becomes 532187676767E through the conversion process.

[Verification process]

and The subsequent processing is performed on the converted code string base.

The stored sequential logic is moved to a working area in memory 6 for modification by the storage process of step 38. Then, in the matching process in step 31, a code string representing only the changing points of the converted code string is compared with the sequential logic.

Let's take the code string S32187676767E and the state transition diagram of FIG. 4, that is, the sequential logic, as an example.

The correction process after the verification process will be explained.

First, code 3 is next to the code string S, and in FIG. 4, the code that is allowed to transition from state S is 3, and the transition is allowed for code 3, that is, it cannot be matched in the matching judgment at step 32. Ta. Thus, the state S transitions to the state A1. At the end of the collation process in step 33, it is determined whether the collation process is completed based on whether or not the code is at the last code E of the code string. is transferred to the location of the sequential logic stored in step 6, and the sequential logic is rewritten with the corrected one (step 6-A in FIG. 3).

If the process has not been completed, the process returns to step 31.

Then, for example, following the flow of the above-mentioned story, a process of matching code 3 with the next code, that is, code 2, is performed.

In FIG. 4, code 3 changes from state S to state ?
iA1, but in state iA1, transition is allowed for code 3 and code 2. Therefore, the next code 2 can transition from state 8A1 to state A2, that is, it is verified and the state transitions to state A2.

Similar processing is repeated until state A3. A transition is made from A4 to state A5 by code 7 which appears first in the code string.

The next code after need 7 is code 6, but in state A5, transition is only allowed for codes 7 and E, and matching is not possible. At this time, rules 1 to 5 for correction
(described later), and the correction was possible.

This is fed back to the matching process in step 31. If modification is not possible, the modification process ends and the sequential logic at the location stored in step 38 during the current modification is ignored. That is, the sequential logic stored in step 6 is not rewritten as described in step 6A of FIG. 2 above.

Here, if the standard state transition diagram in FIG. 4 is modified to the state transition diagram in FIG. 7 according to rules 1 to 5, it becomes possible to match the codes after code 6 in the code string, and step 33 It is determined that the matching process is finished, and the correction process for the data is completed. Then, in step 38, the modified sequential logic that has been modified is stored in the working area of the memory 6.

[Correction Processing, Rules 0 to 4] Hereinafter, Rules 1 to 5 for performing sequential logic modification processing will be described.

Before explaining each rule, let us define the language used here.

The auxiliary state refers to a state added by modification to the standard state transition diagram, and is distinguished from the state in the standard state transition diagram (hereinafter referred to as standard state).

State A6 in FIG. 7 is the auxiliary state.

A jump transition is a transition to a state other than the adjacent state, and the number of jumps is 0 calculated based on the standard state, for example, the fourth
In the figure, the transition from state A1 to state A3 is skipped by 1.
This is called the transition.

Suppose there is an auxiliary state adjacent to state A4,
The transition from the auxiliary state to state A5 is called a transition with jump number 1, and the transition to the adjacent state has jump number O.

What is common to Rules 1 to 5 below is [Rule O
] Transitions in the opposite direction are not allowed except for transitions to auxiliary states.

In other words, although the state transitions in the directions of the respective arrows shown in FIG. 5-2, the state does not change, for example, from 8 to 1 or from 1 to 8.

[Rule 1] By adding one auxiliary state from the state S (this is called the starting state), the next auxiliary state is added if a transition to the standard state can be made with the number of skips, for example, 2 or less.

The transition conditions allowed at this time are transition from the initial state to the auxiliary state,
An example of transition from the auxiliary state to the auxiliary state itself and from the auxiliary state to the standard state is shown in FIG. 8, and as is clear from the figure, state aO in the figure is the auxiliary state.

[Rule 2] When rule 1 is not satisfied, if a transition from the initial state to the standard state is possible with a transition of 2 or less jumps, this transition is added.

An example is shown in FIG. 9, in which a and b are attached, indicating the added transition.

[Rule 3] If the code is in the opposite direction from state S to state iE, and by adding an auxiliary state, it is possible to transition to the standard state with the number of jumps less than or equal to 1 in the next code, then the standard When a transition from a state to a standard state is possible with a jump count of 1 or less, an example of adding this transition is shown in FIG. 10. The one with a attached in FIG.

[Rule 4] When Rule 3 is not satisfied, a transition from the auxiliary state and the standard state to the auxiliary state, a transition from the auxiliary state to the auxiliary state itself, and a transition from the auxiliary state to the standard state are added. An example is shown in FIG. 11. State aQ in FIG. 0 is an auxiliary state. In each judgment process of rules 1 to 4 in steps 34 to 37 in FIG. 6, it is determined whether or not each of the previous rules is satisfied, and if it is satisfied, the sequential logic is modified according to the rule. , feeds back to the matching process. If the rules are not satisfied, judgment processing is performed in the order of rules 1 to 4.

Returning to the overall flowchart of FIG. 2, the steps after the sequential logic correction process in step 4 will be explained.

Step 7''+4 judges whether or not the modification of the sequential logic using all the data in one category has been completed, and if it has not been completed, it belongs to the same category.For example, if it is "l", it belongs to the "l" category. Input various next pattern images and modify the sequential logic.

When all the data in one category has been corrected,
The standard sequential logic stored in the memory 6 was modified in step 4 according to the standard sequential logic processing in step 5 and the storage processing of the sequential logic in step 6, but the data is not stored in the memory 6 in step 6-A. It is stored and registered in the memory 7 as a dictionary (1) in step IO. Note that the operation of step 6-A may be performed in step 4. In the determination process of step 8, it is determined whether or not the processing for all categories has been completed. If not, the process returns to step 1. At this time, the standard sequential logic of the next new category to be modified may be stored in the memory 5 by the standard sequential logic processing in step 5 and the sequential logic storage processing in step 6 described above.

[Discrimination function creation process]

When it is determined that the correction of the sequential logic using all data of all categories is completed, the result is "Y II" in step 8, the process proceeds to step 9, a flag is set, and step 3 indicates that a discriminant function will be created in the following process. The information is transmitted to the process judgment processing.

As a result, the subsequent processing through step 3 becomes the discriminant function creation processing from step 11 onwards from "No". Then, the image input processing in step 1 and the preprocessing in step 2 can be used for both modifying the sequential logic and creating the discriminant function.

Next, the discriminant function will be explained.

By using sequential logic in the present invention, the accuracy of pattern identification has become extremely high compared to conventional methods.
A discriminant function is used to identify the following.

This makes the features of the present invention even clearer.

16 By removing the limit on the number of times each state can be passed through, the number of required sequential logics is greatly reduced.

2. Instead of restricting individual states, we find a function that can express the stroke structure of the relationship between states, and use it as a function for identifying patterns, that is, a discriminant function. It becomes a wide recognition device.

Then, by defining a discriminant function whose variable is the number of times each state in the sequential logic is passed through, and finding a method for extracting coefficients, threshold values, and features, it became possible to automatically create the discriminant function.

Next, the principle of automatically creating a discriminant function will be explained.

Consider a case where, in arbitrary sequential logic, each state in the sequential logic represents something that identifies a pattern belonging to a different category compared with the sequential logic (i.e., a discriminant function).

In the simplest case, the number of times X passes through the first state
When fi is the pattern Pa, for example, more than 10 times, and when the pattern pb is less than 10, it can be known from the value of xn whether the input image is the pattern Pa or the pattern pb. Expressing this mathematically, we get
When xn>10, that is, Xn-10>O, the input image becomes pattern Pa; when Xn<10, that is, Xn-10<O, the human image becomes pattern pb, and in this case F (Xn
) =Xn 10 is called a discriminant function, and the reference value 10 for separating pattern Pa and pattern Pb is called a threshold value.

Generally, it is necessary to use more variables for identification, and when expressed as a linear identification function, equation (1) is obtained.

Here, xn is the number of times A passes through the nth state, and smn is a coefficient G is a function consisting of terms of order higher than the third order of Xn (this can be ignored when it is a simple pattern) S is the number of two categories The threshold value (xl) for identifying patterns belonging to
The number of times of passing through the th state is Pan and Pb, respectively.
For any pattern belonging to each category let n, F ((Pan) , S) > O(2) F
((Pbn), S) <0
Once the coefficients An, Bmn, and threshold S that satisfy (3) are found, it is possible to identify whether the input image is pattern Pa or pattern pb using the relationship between equations (2) and (3).

[Coefficients An, Bmn] Next, a case will be described in which the coefficients An and Bmn are obtained.

From equations (1) 2 to (3).

ΣAn(Pan-Pbn)+ (In the following, we will ignore the term G in order to identify it with a simpler function and to simplify the explanation.) Here, Pan-Pbn and PamPan-PbmPb
n represents the difference between pattern Pa and pattern Pb, that is, the feature and feature amount for identification.

In general, if you use something with a clear difference, that is, something with a big difference, it will be easier to notice the difference and make it easier to identify.In order to improve the recognition rate, Pan-Pbn and PamP
When an-PbmPbn is large, the coefficients Am and Bmn need to be large.

From the above, An = C(Pan-Pbn) K
(5) Bmn=C(PamPan-PbmPbn) K
(6) Here, K is an appropriate odd number and C is a positive constant.

Equation (5) is the first and second term on the left side of Equation (4).

Substituting (6) gives (Pan-Pbn) K+
1.

(PamPan-PbmPbn) K" l. This is to make the exponent an even number so that even if the value inside these parentheses is negative, the expression will be positive. Since the pattern can take various values, Equation (5) and (6) must be taken as average values.

, where 　　 represents the average value.

Next, the threshold value S is determined.

In equations (1)2 to (3), F ((Pan) , S) >O>F ((Pbnl
, S).

ΣAnPbn+ΣΣBmnPbmPbn
(9) becomes. The threshold value S that satisfies formula (9) is approximately 5=-(ΣAn(Pan+Pbn)+n):pBmn(PamPan+PbmPbn))
(9-1)n, (Pan) in equation (9-1), (
Since Pbn) takes various values depending on the pattern, it is necessary to average it, 5=-(ΣAn an+ fi+ n ΣΣBmn am an+ m bn )
(10) becomes n.

In equations (7), (8), and (10), C and K can be determined appropriately (for example, C = 1), so from pattern Pa and pattern pb, (Pan) and (P b n) By determining the coefficients An, Bm
n and threshold value S are determined.

In other words, the discriminant function is found.

Although the method for determining the discriminant function for patterns belonging to two categories has been described above, discriminant functions for patterns belonging to two or more categories can be similarly determined.

For example, patterns belonging to three categories are Pa and Pb.
and Pc.

First, the discriminant function F for pattern Pa and pattern pb is
Find 1.

Fl((Pan), St) >O(11)Fl((P
bn), St)<0 (12)
Similarly, the discriminant function F2 is found for pattern Pa and pattern Pc.

F2 ((Pan), S2) >0
(13) F2 ((Pc n) , 52) <O(
14), the discriminant function F3 is similarly determined for pattern Pb and pattern Pc.

F3 ((Pbnl, S3) >0
(15) F3 ((Pen), S3)
<O(16).

Using equations (11) and (16), Fl((Xn), St) >OcF2((Xn), S
2) If >O(17), the input image is identified as pattern Pa.

Similarly, Fl((Xn), St) <OcF3((Xn), S
3) When >0 (18), the input image is pattern p
It is identified as b.

F2((Xn), S2)<0 and F3((Xn), S3
) <0 (19), the input image is pattern Pc
is identified as

FIG. 12 abstractly represents the above relationship.

The category spaces of each pattern Pa, Pb, Pc are divided into discriminant functions Fl, F2. It is F3,
Each category space is defined by equations (17) 2 to (19).

For example, if the input image is within the category space of pattern Pa, the input image is identified as pattern Pa.

Even when the number of categories of patterns matched by sequential logic is larger, the discriminant function can be found in the same way.

From the above, from equations (7) and (8) for calculating the coefficient An and coefficient Bmn, and equation (10) for calculating the threshold value S,
Equation (1) giving the discriminant function is found. It is possible to identify multiple categories that match any one order logic, but by using this discriminant function and the discriminant conditions of equations (2) and (3), it is possible to identify categories that are effective for discrimination, that is, to identify The features improve recognition rate and processing speed.

[Feature extraction method]

Next, a feature extraction method will be explained.

From equations (1)2 to (2), equations (7), equations (8), and equations (10), F ((Pan) , 5) F ((Pbn) , S) is obtained.

In equations (20) and (21), what gives stable characteristics is one that is always positive for all patterns Pa and always negative for all patterns pb.

For example, for the number of passes through the nth state to be a stable feature.

must hold true for all patterns of Pan and Pbn.

This condition may also be true for the sum of multiple terms in equations (20) and (21).

For example, are all patterns of Pan, Pan, Pbm and Pb
If it holds true for n, the linear sum AmXm+AnXn of the number of times of passing through the m-th and n-th states becomes a stable feature.

Generally, in equations (20) and (21),
If the linear sum of one term or multiple terms is probabilistically high (
If it is positive for pattern Pa and negative for pattern pb, it can be used as a feature for identification.

In the method created by the sequential logic modification method of the present invention, if there are states that transition with the same code and the number of jumps between states is l or less, these states (hereinafter referred to as common states) are passed through. For example, in the state transition diagram of pattern "7" in Figure 13, the jump between state 8At and state a1 is The number is 1, and the state is transitioned by code 3, but in distinguishing between pattern "7" and pattern "wa", the discrimination function can be -7,6 (XI+χ1).

Here, xl and χ1 are distinguished by the number of times they pass through state A1 and state a1, respectively.

This is because the state A1 and the state Sat share information on the line segment in the stroke.

The next feature that is likely to be the second-order term of Xn in equation (1) is (Xx+χt)2t7>(X1+χ1 is the state A
1 and state a1) and (Xi + χt) (X2 + χ2) terms (product of the number of passages of adjacent states...corresponds to the curvature of the medium) Here, x2 is the state adjacent to state A1 with respect to xl A2
, and χ2 is the number of times it passes through state a2. These are likely to be characterized by the former being the length of the line segment, and the latter being the sum of the products of adjacent ones, and χ2 being the curvature of the line segment. This is because it is related to.

Next, the features that make a big difference between the patterns, that is, the features that make them easy to identify, are the coefficients (An) and (
Bmn) with a large absolute value or the average value of the sum of a plurality of coefficients has a large absolute value.

In practice, stable and easily distinguishable features are used as the discriminant function.

[Feature extraction procedure]

A procedure for extracting such features and determining a discriminant function will be explained.

1. Find the number of times each state in the sequential logic is passed through a comparison process with the sequential logic.

2. Find the sum of the number of times the common state is passed.

3. Find the square of what was found in 1 and 2.

4. In each state and common state in sequential logic,
Find the product of things that pass through adjacent states.

5. After completing the matching process for each category,
Find the average value of those obtained in step 4 from l.

8. In equations (7) and (8), K=l, C=1
Then, from what was found in step 5 above, the coefficient (An),
Find (Bmn).

7.6 Number the obtained coefficients (An) and (Bmn) in ascending order of absolute value, for example, up to 10.

In the order of the numbers assigned in 8.7, find the ones that satisfy equations (23) 2 to (25) with a probability of 90% or more, and find, for example, up to about 5 in order of probability.

This is the feature used in the discriminant function.

From the features obtained in 9.8, use equation (10) to find the threshold value. Here, the coefficient (An
) and (Bmn) are set to 0 and the threshold value is determined.

10. Perform steps 6 to 9 above for different categories checked against the same sequential logic to obtain a discriminant function.

The process for determining the above-mentioned discriminant function will be further explained based on the flowchart of FIG.

In the flowchart of FIG. 2, the processing after the dictionary search/verification process in step 11 in which the creation of a dictionary related to sequential logic is finished and the creation of a discriminant function is started will be described.

In the dictionary search/verification process of step 11, step lO
The sequential logic is searched from the dictionary related to the sequential logic registered in , and the matching process is performed.The number of times each state is passed through is determined at the stage of the matching process.

In step 12, the matching result is determined, and if there is no matching, search matching is performed on the next sequential logic.When matching is performed, the matching sequential logic and result are stored in step 13.The details of this process are as follows. A flowchart is shown in FIG.

Step 50 is to perform step 2 of feature extraction to obtain a feature extraction and a discriminant function. For example, in the state transition diagram of pattern "7" in FIG. 13, state A1, state al, state A
2 and state a2 and state A4 and state a4, the sums passing through each state are determined.

Step 51 is step 3 of extracting features and determining the discriminant function.
This process performs the following processing.

Step 52 is step 4 of extracting features and determining a discriminant function.
Process. For example, in the state transition diagram of pattern "7" in FIG.
1, the sum of state a1, the sum of state A2 and state a2, state A4
The product of the sum of , state a4 and state A3, the sum of state A4 and state 8a4, and the number of times each state passes through state A5, state A5 and state a6 is found.

In step 53, the number of times each state is passed and the results of steps 50 to 52 are sorted by sequential logic and category and stored.

The above process is performed by the judgment process in step 14 of FIG.
This will continue until all data have been collected.

After data collection is completed, a discriminant function is created in step 15. A detailed flowchart of this process is shown in FIG.

The process of creating a discriminant function in step 15 is performed for each sequential logic between categories matched with the sequential logic. For example, if the number of matched categories is one, the process of creating a discriminant function is not performed. If two categories are matched, a discrimination function is created to identify these categories. The matched category is "A".

"A" and "B" in the three cases of "rB" and "rc"
, "A" and "C", and "B" and "rc", respectively. The same process is carried out in the case of 4 or more.

The flowchart for creating the discriminant function shown in FIG. 15 will be described below.

In the averaging process in step 60, the values stored in step 53 are averaged for each category of sequential logic. Step 61 is a process of calculating coefficients, and from each average value obtained in the average value process of step 60, equation (5) or (6) is calculated between different categories of each sequential logic.
) can be found from the formula.

In the coefficient ranking process of step 62, the coefficients found between different categories of each sequential logic are numbered in descending order of absolute value.

In the feature extraction process in step 63, the coefficients in step 62 are ranked between different categories of each sequential logic according to the numbers assigned in the process, so that each coefficient is between (22)2 and (25).
) that satisfies the conditional expression with a probability of 90% or more, extract at most 5 lateral inflections of those with a high probability and a low rank in step 62 (that is, the absolute value of the coefficient is large), and then Features.

In the threshold processing in step 64, threshold values are determined according to equation (1o) from the features extracted in the feature extraction processing in step 63 between different categories of each sequential logic.

Through the above processing, the constants in the discriminant function can be found, that is, the discriminant function can be created.

The discriminant function created here exists between different categories compared with each sequential logic, so step 1 in Figure 2
In registering the discriminant functions in the dictionary in step 6, the sequential logic stored in the memory 7 is made to correspond to the discriminant function that identifies the category matched with each sequential logic. It also stores the results identified by the discriminant function, that is, the recognition results.

Although the dictionary necessary for pattern recognition can be created through the above processing, each of the memories 4 to 8 in FIG. 1 and their mutual relationships will be explained in detail below.

The memory 4 stores programs for performing preprocessing, sequential logic creation processing, discriminant function creation processing, memory management, etc., and these programs are executed by the CPUI.

Memory 5 and memory 6 are work areas for preprocessing and dictionary creation processing, and the memory contents are constantly rewritten.

The memory 7 is divided in advance, and each of the divided sections stores each sequential logic. This simplifies the sequential logic modification process and search process.

[Memory structure of sequential logic]

FIG. 16 shows the storage structure of sequential logic.

In this example, in order to make it easier to modify the sequential logic, we introduce the code for transitioning to a state, the (relative) address at which transition is allowed from one state to the next, and the fixed length for the number of times each state is passed through. Memory capacity (for example, 1 byte) is allocated. In addition, by storing the starting state, final state, and sample states in the sample transition diagram at predetermined addresses, and by providing a place to add one auxiliary state between the sample states, it is easy to modify the sequential logic. .

After the final state and code E, a predetermined address for storing the discriminant function is stored, and the sequential logic and the discriminant function are associated with each other.

FIG. 16 is a memory map representing a standard state transition diagram of pattern "7" in FIG. 4, and FIG. 17 represents a modified state transition diagram of FIG.

The state transition diagram in Figure 7 has been modified by adding an auxiliary state A6 to state A5 in the standard state transition diagram in Figure 4, and at this time, state A5 transitions to state 8A6 by code 6 and a new state is added. Added state! A transition to state A6 due to code 6 and a transition to state A5 due to code 7 are added to 8 A e.

On the memory map, as you can see from the difference between Figures 16 and 17, there is a 6 (
A6) is transition destination 6 at transition condition 6 of 7(A5)
The addresses of transition destinations 6 (As) and 7 (A5) are stored in transition conditions 6 and 7 of address 6 (A6).

The memory 8 is divided in advance, and each division stores a discriminant function corresponding to sequential logic, a conditional expression using a discriminant function such as equations (20) and (21), and a discrimination result.

[Recognition processing]

A case will be described in which recognition processing is performed using the sequential logic created by this method and a dictionary of one-way functions.

At this time, the configuration of the apparatus remains the same except that a recognition processing program is used instead of the dictionary creation program. FIG. 18 shows a flowchart of the recognition processing.

The operation will be explained below.

The image input process in step 1, the preprocessing in step 2, the dictionary search and collation process for sequential logic in step 11, and the collation determination process in step 12 are the same as those performed in the creation of the dictionary above.

Step 70 determines whether or not the search of the sequential logic dictionary is completed, and if the input image does not match all of the sequential logics, that is, if it cannot be recognized, the process ends;
Resulting in denial of recognition.

In the discrimination process using the discriminant function in step 71, the value of the discriminant function is obtained from the number of times each state in the sequential logic is passed, which was obtained at the stage of the matching process, and the discrimination result is obtained from the above-mentioned conditional expression, and the recognition process is terminated. .

〔effect〕

As described in detail above, according to the present invention, it is possible to provide a learning function in the development of OCR or in the creation of a dictionary during pattern recognition.

Furthermore, according to the present invention, sequential logic (
It becomes possible to provide a pattern recognition device that incorporates the idea of automata (automaton) and further performs classification processing using a classification function.

Additionally, it has become possible to automatically create a dictionary for online or offline character recognition.

Furthermore, it has become possible to create a chain code for dictionary creation based on a chain code for pattern recognition. By modifying the sequential logic that is a dictionary by starting with the chain code, it became possible to learn better sequential logic. Furthermore, according to the present invention, it is possible to create a separate interval number by using as a variable the number of times each state of sequential logic, which is dictionary data created by code data indicating the direction, is passed through.

[Brief explanation of drawings]

Fig. 1 is a block diagram for explaining the present invention, Fig. 2 is a flowchart showing the operation of the present invention, Fig. 3 is a diagram showing one embodiment of Chen Ken coding, Fig. 4 is a transition diagram of standard sequential logic, Figures 5-1 and 5-2 are diagrams showing the definition of directions, Figure 6 is a flowchart of correction processing for sequential logic, Figure 7 is a diagram showing state transitions after modification, and Figure 8 is an auxiliary state. 9 is a diagram showing the state transition for explaining rule 1 which has an auxiliary state. FIG. 10 is a diagram showing the state transition for explaining rule 2 which has an auxiliary state. Figure 11 is a diagram showing the state transition for explanation of rule 4 with auxiliary states, Figure 12 is a diagram showing the identification of category space, and Figure 13 is the diagram showing the pattern "7" state transition diagram, Figure 14 is a flowchart related to data collection, Figure 15 is a flowchart for creating a discriminant function, Figures 16 and 17 are explanatory diagrams of memory maps of sequential logic, and Figure 18 is a flowchart of recognition processing. 1 is a CPU 7 is a sequential logic storage memory 8 is a discriminant function storage memory

Claims (1)

[Scope of Claims] A preprocessing unit that encodes image information converted into a line diagram based on the direction of the line diagram, and storing sequential logic that represents a state of transition in the direction of the line diagram coded by the preprocessing unit. a storage unit that modifies the standard sequential logic in order to obtain the above recognition results for various image information that should be recognized as the same image information; , wherein the control unit modifies the standard sequential logic by forward transition.