KR200331199Y1 - apparatus for generating character attractor - Google Patents

Abstract

The present invention discloses a character attractor generation device. The apparatus of the present invention includes an image scanner for inputting a text image, a preprocessor for preprocessing the input text image, a feature extractor for extracting m primary features from the preprocessed text image, and the extracted m A character attractor that transforms feature values into a character attractor by the following attract conversion function H '(X _{k + 1} , Y _{k + 1} ).

H '(X _{k + 1} , Y _{k + 1} ) = (y _k + 1-a (x _k + cf _i ) ² , b (x _k + cf _i )}

Where k = 0, 1 2, 3, ---, n

i = 1, 2, 3, ---, m

a = 0.55, b = 0.3

Therefore, in the present invention, confused characters with high misclassification frequency can be recognized with high accuracy.

Description

Apparatus for generating character attractor}

The present invention relates to an apparatus for generating a character attractor, in particular, an Eron function that generates a Strate Attractor of Chaos Theory, which can highly sensitively identify a minute difference in order to recognize a minute difference of a feature pattern among similar characters. The present invention relates to a character attractor generating apparatus capable of generating a character attractor.

Character recognition technology can automate the data input method, which was dependent on manual operation, by providing a means for separating and recognizing various character information from an input document image using a scanner or a pen input device.

Therefore, there have been many devises regarding the recognition of characters such as Hangul, Chinese characters, and English, and the devises for recognizing complex documents such as newspapers and magazine reports are also actively underway. Representative character recognition methods designed up to now include the Template Matching Method for recognizing the degree of matching with the stored character image, the Statistical Method for recognizing the Euclidean distance of the feature vector of the character image, and the character. There is a structural method (Structural Method) that recognizes by comparing the structural association of the elements constituting the.

In the 1990s, there have been several attempts to use neural networks to recognize Hangul characters, which work well to some degree of variation or incomplete input.

However, because Hangul is not only a large number of possible characters, but also a relatively small number of basic letters combine to form letters, it is very difficult to recognize Hangul characters because of the similarity between them. Such a similar character is a confusing character that generates a misunderstanding, and is conventionally solved by a post-processing process of correcting a misrecognized character or by setting a confusing character recognition unit. The attractor was constructed by using the histogram value of the network feature, the dimension value of the attractor was used, and the box-counting dimension of the letter itself was used as a feature to show the recognition rate of 96.03%.

The character recognition method of the existing design is mainly a method of extracting the network feature, the projection feature, the cross-distance feature, etc. of the character image and recognizing it using a neural network.

However, since most of them depended on the generalization ability of neural networks, they caused many misunderstandings for confused characters with simple shapes and similarities. Therefore, a new character feature extraction method is needed to recognize such confused characters.

An object of the present invention is to create a Strate Attractor of the Chaos Theory which can identify the minute differences with high sensitivity in order to recognize the minute differences of the feature patterns among the similar characters in order to solve the problems of the prior art described above. The present invention provides a character attractor generator capable of generating a character attractor using an Ennon function.

1 is a block diagram of a character attractor generation device according to the present invention.

2 is a view for explaining the structure of the Hangul 6 form of the present invention.

3 shows a 3x3 mask.

4 is a diagram for explaining connectivity.

5 is a view showing a sub-region structure for extracting network features according to the present invention.

6 is a graph showing the extraction results of the network features of FIG.

7 is a view showing a sub-region structure for extracting the projection feature according to the present invention.

8 is a graph showing an extraction result of the projection feature of FIG. 7;

9 is a view showing a sub-region structure for extracting cross-distance features according to the present invention.

10 is a graph showing an extraction result of the cross-distance feature of FIG. 9;

11 is a time series and attractor trajectory of an enong system.

12 and 13 are views for comparing the character attractor of the confused characters according to the present invention.

* Description of the symbols for the main parts of the drawings *

10: image scanner 20: preprocessor

30: Feature Extractor 40: Character Attractor

In order to achieve the above object, an apparatus of the present invention includes an image scanner for inputting a text image, a preprocessor for preprocessing the input text image, and a feature extractor for extracting m primary features from the preprocessed text image. And a character attractor for converting the extracted m feature values into a character attractor by the next attracting conversion function H '(X _{k + 1} , Y _{k + 1} ).

H '(Xk + 1k + 1k k i2k i

(Where k = 0, 1 2, 3, ---, n

i = 1, 2, 3, ---, m

a = 0.55, b = 0.3)

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. This embodiment is described in sufficient detail to enable those skilled in the art to practice the invention.

Referring to FIG. 1, the character attractor generating apparatus of the present invention includes an image scanner 10, a preprocessor 20, a feature extractor 30, and a character attractor 40.

The image scanner 10 is a general image scanner to generate character image data by scanning letters or numbers printed on documents and the like.

The preprocessor 20 may be configured in software or provided as a dedicated chip for preprocessing. Smooth, linearize, and normalize the inputted individual character image.

The feature extractor 30 performs a feature extraction algorithm from the preprocessed individual character images to extract the network, projection, and cross-distance features.

The character attractor 40 organizes the extracted feature values into a one-dimensional time series and converts the summarized time series into a two-dimensional attractor trajectory through a conversion function.

Hereinafter, a description will be given of the character attack generation algorithm according to the present invention.

1. Structural Analysis of Hangul

Hangul is a combinatorial character consisting of 24 basic Jamo characters (14 consonants and 10 vowels). Theoretically, there are 11,172 Korean characters that can be combined, but there are about 1,500 embroidery characters that are used a lot.

Hangul is a combination of characters, and unlike Hanja, the characters are classified into six types according to the arrangement of vowels and the presence of a support. The Hangul 6 form is a definition proposed by Lee Myung-geun in 1972, and is divided into 6 forms of FIG.

As can be seen in <Table 1>, the two forms of horizontal gathering letters are used about 45.5%, and the three forms of vertical gathering letters are only 91 characters.

In this way, Hangul is a letter that is arranged with consonants and vowels according to the six forms mentioned above, and is very scientific in character, but its structural similarity is very high, making it difficult to extract and recognize phonemes.

The similarity of letters refers to the fact that one letter is completely superimposed on another letter and the difference is very small. Such similarity can be divided into structural similarity and topological similarity. In other words, as an example of structural characteristics, "angle, spatula, gig" is completely overlapped except for the horizontal line connected to the vowel, and it is difficult to recognize. Therefore, there is a difficulty in recognition.

Therefore, in the present invention, a confusion character having a high misrecognition frequency according to the similarity of the Hangul characters is selected and classified according to the above six types is shown in <Table 2>.

As shown in <Table 2>, out of 2,350 KSC-5601 Hangul standard, the total number of confusing characters is 305, accounting for 12.9%. Table 3 shows the confusing group of characters to be used in the present invention.

2. Pretreatment

In the preprocessing step, the character recognition error is minimized by reinforcing the information necessary in the character feature extraction step and reducing unnecessary information. Therefore, in the preprocessing step, the hardware miscellaneous generated when the document is input is removed, and the incomplete pattern of the input image is straightened and right angled so that the feature points of the characters can be found more accurately. In this design, three kinds of preprocessing are performed: smoothing, linearization, and normalization.

2-1. Smoothing

First, the center pixel for the 3X3 mask and neighboring pixels in eight directions are defined as shown in FIG. 3.

When the center pixel P (0) is 0, if there are three or more black pixels among the four-direction neighbor pixels {P (2), P (4), P (6), P (8)}, it is determined as noise. Set P (0) to 1.

When the center pixel P (0) is 1, the eight-way main shift pixels {P (1), P (2),... If there are less than two black pixels in P (8)}, P (0) is set to 0 by judging as noise, and in case of two, is it a thin line component to be preserved as shown in FIG. 4 in consideration of its connectivity? If it is a thin line component, the black pixel is kept as it is, otherwise P (0) is set to zero.

To determine whether the 8-direction neighboring pixel is a thin component to be preserved, set the C value C = ((P (1) + P (7) + P (8))-(P (3) + P (4) + P (5))] * [(P (1) + P (2) + P (3))-(P (5) + P (6) + P (7))], so that the C value is 0 In this case, it is judged as a thin line component, and when it is not 0, it is judged as noise.

2-2. Linearization

The purpose is to improve the recognition rate by minimizing unnecessary feature points due to incompleteness of the input image data. When representing neighboring pixels as shown in the following <Table 4>, for the point S (n) = 1

① R (n-2) + R (n-1) + R (n) + R (n + 1) = 0

② S (n-2) + S (n-1) + S (n) = 1

③ T (n-2) + T (n-1) + T (n) = 3

If is satisfied, the critical columns n-i and n + j are connected to R in row R and connected to 0 centered on R (n) and 1 on T (n) in row T. When i and j are found, the total pixel counts from S (n-i) to S (n + j) are converted to all pixels in the majority surface critical column to 0, otherwise to 1.

2-3. Normalization

Character Extraction Characters As a preliminary step, individual extracted characters should be normalized in order to be able to adapt to various character types and sizes. The text image of the input recognition object is normalized to 40 × 40 image data.

The 40 x 40 size is the size of a 10-dpi character that is most of the text of a normal document when entered with a 300 dpi scanner.

In general, the normalization method may consider a linear normalization method and a nonlinear normalization method. The nonlinear normalization method has an advantage of preventing deformation of a character image, but the processing time is high because the algorithm is very complicated. In addition, the conventional linear normalization method deletes thin strokes when the character image is reduced in size, or conversely, in the case of a complex character having a thick stroke, adjacent strokes are attached to each other to accurately extract the characteristics of the character. have. Therefore, in the present invention, a stable normalization pattern is obtained by using an improved linear normalization method.

3. Extraction of Primary Characters of Characters

The character recognition method can be divided into a method of using a two-dimensional character image by itself and a method of extracting and recognizing a feature that can describe the shape of the character well according to the type of the feature. The method of recognizing the text image as it is can simplify the feature extraction process and prevent the loss of information that may occur in the feature extraction process, but it is inefficient because the computational amount increases during the recognition process.

Many designs have been developed for various character feature extraction methods, and typical features include network features, projection features, and cross-distance features.

3-1. Network features

The mesh feature divides the character image into N x M sub-areas on the vertical and horizontal axes, and then represents the density of the black pixels in each area. This method is useful for partial image analysis.

In the present invention, as shown in FIG. 5, the number of black pixels is obtained for all 47 regions by dividing the region into the following sizes with respect to 40 × 40 normalized data.

(1) 40 x 40: 1 area

(2) 20 x 20 and center (20 x 20): 5 zones

(3) 10 x 10: 16 areas

(4) 10 x 20: 8 areas

(5) 20 x 10: 8 areas

(6) 20 x 20 including virtual areas: 9 areas

Then, a value between 0 and 1 obtained by dividing by the total number of pixels that may exist in the mesh of the size defined above is characterized by the network of FIG. 6.

3-2. Projection features

The projection feature is a feature that indicates the characteristics of the stroke by accumulating the number of black pixels that are encountered when scanning N times on the vertical axis and M times on the horizontal axis. It is less susceptible to noise and expresses long strokes on the same axis.

In the present invention, after dividing the region into the following sizes for normalized data of 40 × 40 in the same manner as in FIG. 7, 600 features are obtained from 22 regions in total. 8 shows the extraction results of the obtained projection features in time series.

(1) 40 x 40: 1 area (80)

(2) 20 x 20 and center (20 x 20): 5 zones (200)

(3) 10 x 10: 16 areas (320)

3-3. Intersection features

The cross distance feature is a method of determining the distance until the first black pixel is encountered after scanning N times on the vertical axis and M times on the horizontal axis. This method expresses the overall appearance well, but may be sensitive to noise. In the present invention, 160 features are obtained in four directions of up, down, left, and right with respect to 40 × 40 normalized data using the method shown in FIG. 9. In Fig. 10, the extraction results of the obtained cross-distance features are shown in time series.

3-4 Resetting Primary Feature Values

Individual character features extracted first to reduce the effects of system noise on a total of 807 network, projection, and cross-distance feature values extracted from text images, and to deliver high-quality information to the next-generation attractor extraction model. Reset the value (cfi) step by step as follows: Where i = 1, 2, 3, .., 807.

(1) If cfi <0.1, reset to cfi = 0.0

(2) If 0.1 cfi <0,2, reset to cfi = 0.15

(3) If 0.2 cfi <0.3, reset to cfi = 0.25

(9) If 0.8 cfi <0.9, reset to cfi = 0.85

(10) If cfi 0.9, reset to cfi = 1.0.

In this way, a total of 807 feature values are obtained for each individual character, and the obtained feature values are arranged in time series.

4. Attractor Extraction Model

In this design, after reconstructing the character attractor by substituting the reconstructed primary feature obtained from the character image to the modified eneon system to apply the chaos theory to the character recognition, we first confuse it by qualitatively analyzing the distribution of the attractor trajectories. See if the characters are distinguishable.

4-1 Attractor Extraction Model of Proposed System

The Ennon Attractor is a boomerang-like model that simplifies the Lorentz system's dynamic system. The Ennon system derives a high-dimensional range attractor by two-dimensional transformation in one-dimensional dynamics. In addition, the Ennon system is similar to the analysis of chaos that occurs in log transformation, while also including features of more complex attractors, such as the Lorentz attractor. The stretch-and-fold action of the eneon system in two dimensions can be expressed by two-dimensional coordinates of x and y. Therefore, the Ennon system's transform function behaves similarly to Affine Transformation occurring in the plane. The conversion function H (X, Y) of the Enong system is shown in Equation 2 below.

H (X, Y) = (y + 1-ax2, bx), a = 1.4, b = 0.3

The Enon system is sensitive to random constants a, b and the starting point (x0, y0), similar to the log transformation. In other words, it shows a dependence sensitive to the initial conditions that characterize chaos theory. Enones were a = 1.4, b = 0.3 and (x0, y0) = (0, 0) as initial conditions. The attraction is reconstructed from the starting point (x0, y0) and the points generated by the feedback. The conversion function H (Xk + 1, Yk + 1) with such feedback is shown in Equation 3 below.

H (Xk + 1, Yk + 1) = (yk + 1-axk2, bxk), (k = 0, 1, 2, 3,.)

FIG. 11 shows the x, y-axis time series data and the reconstructed attractor of the Enon system.

In this design, the character attractor was reconstructed by modifying the model that generated the Enon attractor to reflect the first feature (cfi) of the extracted text image using the network, projection, and cross-distance features. The transform function H '(Xk + 1, Yk + 1) of the modified Enon system is as shown in Equation 4 below.

H '(Xk + 1, Yk + 1) = {yk + 1 a (xk + cfi) 2, b (xk + cfi)}

Where k = 0, 1, 2, 3,. . , n, and i = 1, 2, 3, .. 807 (primary feature extraction).

4-2. Parameters for the system

In addition, the present invention was repeated to find the parameters of the system that well reflects the character features. In the experiment, the parameter b was fixed at 0.3, and a method of adjusting a was selected. Table 5 shows the results of the parameter setting experiment.

In this design, we selected a = 0.55 and b = 0.3 as parameters to reconstruct the optimal character attractor.

12 and 13, the points constituting the attractor between the confused characters according to the present invention draw a different trajectory and can be distinguished from each other qualitatively.

Experimental results of the present invention showed a 99.49% classification rate for 2,350 Korean characters, showing the effectiveness of the proposed method. In this design, we tried to find the chaos phenomenon in the character pattern differently from the existing method of constructing the attractor. It is expected to be widely applied to personal authentication fields such as fingerprint recognition, face image recognition and signature recognition as well as the character recognition field.

Claims (2)

An image scanner for inputting a text image; A preprocessor for preprocessing the input text image; A feature extractor for extracting m primary features from the preprocessed text image; And And a character attractor for converting the extracted m feature values into a character attractor by a next attracting conversion function H '(X _{k + 1} , Y _{k + 1} ). H '(X _{k + 1} , Y _{k + 1} ) = (y _k + 1-a (x _k + cf _i ) ² , b (x _k + cf _i )} Where k = 0, 1 2, 3, ---, n i = 1, 2, 3, ---, m a = 0.55, b = 0.3 The apparatus of claim 1, wherein the characteristic values include a network, a projection, and an intersection distance of each individual character.