JPH07334622A - Character recognizing method and device therefor - Google Patents

Abstract

PURPOSE:To provide a character recognizing method and device which can improve the recognizing efficiency of handwritten characters and also can increase the character recognizing speed. CONSTITUTION:A stroke input part 1 inputs the handwriting of characters to be recognized as the waveforms to show the continuous time changes of the X and Y coordinates, and a stroke normalizing part 2 normalizes these input waveforms. Then, a feature vectorizing part 3 extracts the partial feature value out of the normalized waveforms, and a hashing part 4 hashes the partial feature value to obtain the hash value. A weight totalizing part 6 and a recognizing candidate selecting part 8 select the recognizing candidates based on the hash value. A matching part 9 compares the partial feature value of the recognizing candidates with the partial feature value of various characters which are previously registered in a character feature vector data base 10 to secure the matching between both feature values. Thus the number of characters to be matched with each other can be decreased to some extent. As a result, the deterioration of the character recognizing efficiency can be suppressed and the character recognizing speed can be increased.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a character recognition method and apparatus, and more particularly to an online handwritten character recognition method and apparatus.

[0002]

2. Description of the Related Art Conventionally, in an online character recognition method for recognizing a handwritten character based on an input stroke of a pen, based on the similarity between the feature amount of an input character and the feature amount of a character registered in advance. It was designed to recognize characters.

In this case, as a method for obtaining the feature amount of the input character, fragmentary position data of the pen tip detected while the pen is in contact with the character input surface is used. A typical method is to approximate the feature points of the stroke position coordinates with the strokes, and to analyze the change in the stroke position over time by Fourier transformation. .

Then, the feature amount obtained by the above method and a predetermined character dictionary (dictionary storing a set of various character codes and the feature amount) created in advance are sequentially added. The matching of the feature quantity was performed to obtain the character with the highest similarity as the recognition result.

[0005]

When characters are input using a pen, it is possible that the stroke order of the characters, which should be input in the correct order, may be input incorrectly. In such a case, the characters may not be correctly recognized by simply applying the above method.

However, in the above-mentioned conventional example, in order to cope with such a case, it is possible to take only measures such as predicting a stroke order that is likely to be erroneously mistaken in advance and converting it into a dictionary, which is not very effective. I couldn't raise.

For continuous characters, the strokes of the characters are linearly interpolated to combine the strokes to generate another stroke pattern. A measure has been taken to register in advance in a dictionary as characters corresponding to consecutive characters.

When the above measures are taken, there is a problem that the data amount of the character dictionary increases. Further, there is a problem in that the processing time for character recognition also increases if the sequential matching is performed with the increased character dictionary.

The present invention has been made to solve such a problem, and makes it possible to increase the character recognition rate and to perform the recognition processing at high speed. The purpose is to do.

[0010]

In order to achieve the above object, the character recognition method of the present invention pre-registers handwritings of various handwritten characters as information of X, Y coordinates that continuously change with time. By inputting the handwriting of the recognition target character as the information of the X and Y coordinates that continuously change with time, the input information and the information registered in advance are used to match the characters. Trying to recognize.

Further, the character recognition device of the present invention includes an input means for inputting handwriting of handwritten characters as information of X, Y coordinates which continuously changes with time, and a handwriting of various handwritten characters of X, Y coordinates. Saving means for preliminarily saving as time change information, time change information about the handwriting of the recognition target character input by the input means, and various time change information previously saved in the saving means And a recognition means for recognizing a character by performing matching using and.

[0012]

According to the present invention configured as described above, the movement of the pen tip from the start to the end of writing of the recognition target character is input as coordinate information of continuous time change, and this continuous coordinate information is converted into this coordinate information. Since the matching is performed on the basis of the matching, the error in character recognition is reduced as compared with the conventional case where the matching is performed based on the piecewise coordinate information.

Further, when a hash value is obtained from the coordinate information of the above continuous time change and matching is performed using this hash value, some recognized character candidates are selected from various characters. Since the character having the highest similarity is selected as the final recognized character by the matching process using this recognition candidate, the decrease in the character recognition rate is further suppressed. Further, since the number of matching processing targets is reduced, the processing time is also shortened.

[0014]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT A preferred embodiment of the present invention will be described below in detail with reference to the drawings. FIG. 1 is a functional block diagram showing a schematic functional configuration of this embodiment.

In FIG. 1, reference numeral 1 denotes a stroke input unit, which is used for inputting one character of a handwritten character, that is, from the time when the pen first contacts the character input surface until the pen is finally released from the character input surface. Then, a series X (t), Y (t) of coordinate data sampled at a predetermined time interval is generated. Then, the series X (t), Y (t) of the coordinate data is sent to the stroke normalization unit 2 as character stroke data.

FIG. 2A shows a character stroke in the input area when a character which seems to be the character “A” is input, and FIG. 2B shows the input character stroke as
The x-coordinate and the y-coordinate are plotted on the vertical axis, and the time axis is plotted on the horizontal axis.

Next, in the stroke normalization unit 2, the coordinate series X (t), Y (t) of the input character stroke is represented by the x, y coordinates of the lower left corner of the minimum rectangle enclosing the entire input character as [[ 0,0] and the size of each coordinate is normalized so that the x, y coordinates of the upper right corner are [255,255]. Further, the time direction is also normalized so that the writing start time is 0 and the writing end time is 511. As a result, for example, X shown in FIG.
The graph for the coordinates becomes a graph as shown in FIG. 3 after the normalization. The coordinate series thus normalized is sent to the feature vectorization unit 3.

The feature vectorization unit 3 calculates a feature amount vector sequence by calculating a feature amount vector that is a partial feature amount near time for each of the normalized coordinate sequences x (t), y (t). Ask. As shown in FIG. 4, this feature vector is defined as a feature amount obtained by a predetermined calculation within a so-called time window in which the time section is limited.

That is, the feature vector near time is
It is calculated by taking the dot product with the temporally localized function. Examples of temporally localized functions are: 1. Fourier transform with window, feature axis is frequency 2. Wavelet transform, feature axis is scale of basic function 3. General orthogonal function system, feature axis is expansion of basic polynomial Examples include coefficients (eg, Legendre polynomials), but it is also possible to set an arbitrary function group.

Here, the coordinate sequence sent from the stroke normalization unit 2 is a discrete time function f (t) (t = 0,1 ,,,,).
It is represented by. That is, the function f (t) indicates one of the coordinate series x (t) and y (t). In addition, the function localized around time 0 is
Let (w, t). However, w is a parameter that defines the feature. At this time, the feature quantity near time T is the inner product

[0021]

[Equation 1]

Is defined by However, 2 * a represents the size of the window section centered on time T. That is, the window is a section
It is defined by [Ta, T + a].

The above function g (w, t) is, for example, g (w, t) = 0.5 * exp (-i * w * t) {1 + cos (π * t / a)} | t |
≤ a (i is an imaginary unit) g (w, t) = 0 │t│ ≥ a (Fourier transform with window) or g (w, t) = exp (-t ^ 2 / w ^ 2 + i * 5 * t / w) / √w ｜ t ｜ ≦
It is possible to use a (i is an imaginary unit) g (w, t) = 0 | t | ≧ a (wavelet transform using Gabor function as basic function).

When the function g () is an imaginary function, the absolute value of the inner product I (T, w) is used as the feature quantity. As described above, the feature quantity is calculated on the two-dimensional plane of the Tw coordinate system.
It should be noted that the above calculation can be executed at high speed only by integer arithmetic by converting each value of the function g (w, t) into an integer and storing it in a numerical table in advance.

Next, the feature quantity on the two-dimensional plane of the Tw coordinate system calculated as described above is quantized in the axial direction T, w to be converted into a feature vector series. In this quantization, first, a section is set in each axial direction T, w. For example, [a * i, a * (i + 1)] (i = 0,1 ,,,) along the T axis and [b * j, b * (j + 1)] (j = along the w axis. Set the interval of 0,1 ,,,).

Then, the quantized value D [i,
j] is determined by a predetermined calculation. As this calculation,
For example, 1. Select a representative point (for example, the midpoint) according to a rule for each section, and use the feature quantity at that representative point as it is as the feature quantity of the section. 2. Calculate the average value within the interval. And so on. In this way, the feature plane is converted into a feature vector series. Then, this feature vector sequence is sent to the hashing unit 4 in the next stage.

It is possible to omit quantization in the w-axis direction by using a function group that takes discrete values in the w-axis direction. For example, when the Legendre polynomial is used as the function group, w in the function g (w, t) can be set as the degree of the polynomial.

That is, if w = 0,1,2,3,4,5, the window section length is 32, and p = t / 16, then g (0, p) = 1 g (1, p) = pg (2, p) = (3p ² -1) / 2 g (3, p) = (5p ³ -3p) / 2 g (4, p) = (35p ⁴ -30p ² +3) / 8 g ( 5, p) = (63p ⁵ -70p ³ + 15p) / 8 is a function group, and as shown in FIG. 5, these function groups and the coordinate sequence x sent from the stroke normalization unit 2
The inner product of (t) and y (t) becomes the component value of the feature vector.
Therefore, in this case, the vector becomes a 6-dimensional feature vector.

Next, the hashing unit 4 hashes the feature vector calculated by the feature vectoring unit 3. That is, an integer value is associated with each feature vector.
As a simple method, there is a method of quantizing the components of the feature vector and then assigning integer values in the order of appearance of different vectors. In that case, it is necessary to set the feature vector database 5. FIG. 6 illustrates a part of the correspondence relationship between the feature vector and the hash value obtained as described above.

Further, it is possible to further redefine the hash value from the vector neighborhood (neighborhood of feature vectors or neighborhood of components). That is, when the j-th element of the i-th feature vector is V [i, j], for example,
The feature quantity y [i, j] in this neighborhood is y [i, j] = h (V [i, j], V [i, j + 1], V [i + 1, j], V [ i + 1, j + 1]) (h () is a defined hash function). In that case, a function that does not require the feature vector database 5
It is possible to set h ().

The thus quantized value is used as a hash value of the feature quantity vector. Then, the set of these hash values is sent to the weight aggregation unit 6. In the weight aggregation unit 6,
The character weight list corresponding to each hash value of the set of hash values calculated by the hashing unit 4 is read from the character weight database 7. The weight list is, for example, as shown in FIG. 7, and details thereof will be described later.

The weight totaling unit 6 adds the weight of each character code in the read weight list to the total sum value in the totaling table as shown in FIG. In FIG. 8, the sum of weights of the character code "A" shows a high value. Also, "A
₁ "character code" A ₂ ", since it is the same character type was significantly different handwriting shows a case where had been registered in advance as a separate character type.

Next, when the addition of the weights for all the hash values is completed, the totaling table information is sent to the recognition candidate selecting section 8. The recognition candidate selection unit 8 selects a character code whose total weight exceeds a preset threshold based on the total table generated by the weight total unit 6,
This is sent to the matching unit 9 as a recognition candidate.

It is also possible to adopt a method in which, for example, the character codes with the top 10 weighted sums are used as recognition candidates without setting a threshold value. When all the selected recognition candidates have the same character code, the character code can be used as the recognition result, and the process in the matching unit 9 can be omitted.

The matching unit 9 reads out the feature vector series of the recognized character candidates sent from the recognition candidate selection unit 8 from the character feature vector database 10 in which the feature vector series for each character is registered in advance. . Then, by comparing the read feature vector series with the feature vector series of the recognition target character obtained by the feature vectorization unit 3, the character with the highest similarity is set as the final recognition result. DP matching or the like is used to compare the feature vector series. As described above, the character code of the recognition result for the input stroke of the character can be obtained, and the output unit 9 displays the character of the recognition result.

Next, the structure of the character dictionary will be further described. A weight list corresponding to each hash value is attached to the weight dictionary. In the weight list, as shown in FIG. 7, each character code and the weight of the feature vector for which the hash value has been calculated for the character of the character code, that is, the value indicating the relative importance of the feature vector is stored. Has been done.

For example, a high value is set for a feature vector in the vicinity of the dakuten part in hiragana. Further, as the weight, a value that is inversely proportional to the length of the list can be uniformly set for the characters included in the list. For example,
In FIG. 7, the weight of the feature vector indicated by the hash value 3 is set to 1 for the characters "ru" and "ro", and to 2 for the character "wa".

At the end of each weight list shown in FIG. 7, a so-called mark indicating the end of the set of the character code and the weight is stored. Each list has a variable length, but when the weights of the list are sequentially read by the weight aggregation unit 6, the reading of the mark is a condition for ending the reading of the weight information from the list.

As described above, in the present embodiment, the recognition candidate characters are narrowed down by using the hash value calculated from the feature vector of the local coordinate fluctuation of the input stroke, so that the stroke order, which is different for each user, etc. The effect of can be suppressed to a minimum, and a decrease in the character recognition rate can be suppressed.

The weight list read from the weight dictionary is only the list corresponding to the hash value calculated from the feature vector of the recognition target character. Further, the feature vector series read from the character dictionary is only the feature vector series of the recognition candidate characters, and the characters to be actually subjected to DP matching are sufficiently narrowed down, so that high-speed character recognition can be performed. .

The character recognition method and apparatus according to this embodiment may be applied to a system including a plurality of devices or an apparatus including one device. Further, it goes without saying that the present embodiment may be realized by supplying a program for executing the processing defined by the present embodiment to the above system or apparatus.

[0042]

As described above, according to the character recognition method and apparatus of the present invention, the correct stroke number required for the writer (each stroke of a character must be correctly separated, and Characters can be recognized with a higher recognition rate by relaxing the conditions such as being in a regular style) and the correct stroke order (must be in the same stroke order as the correct characters registered in the dictionary).

Further, in many character types, since the recognition candidates are narrowed down at high speed using the hash value calculated from the feature vector of the character to be recognized, the character recognition can be performed at high speed. Furthermore, since statistical processing is not performed in creating the character dictionary, addition processing to the character dictionary can be easily performed. That is, customization by the writer becomes easy, and as a result, a higher character recognition rate can be obtained.

[Brief description of drawings]

FIG. 1 is a functional block diagram showing the configuration of an embodiment of the present invention.

FIG. 2 is a diagram showing an input example of handwritten characters and a graph of coordinate time series thereof.

FIG. 3 is a diagram showing a graph of normalized coordinate time series.

FIG. 4 is a diagram showing an example of generation of a feature vector sequence by a feature vectorization unit.

FIG. 5 is a diagram showing another example of generation of a feature vector sequence by a feature vectorization unit.

FIG. 6 is a diagram showing a relationship between a feature vector and a hash value.

FIG. 7 is a diagram showing a storage example of a weight list in a character weight database.

FIG. 8 is a diagram showing an example of an aggregation table.

[Explanation of symbols]

 1 Stroke Input Section 2 Stroke Normalization Section 3 Feature Vectorization Section 4 Hashing Section 5 Feature Vector Database 6 Weight Aggregation Section 7 Character Weight Database 8 Recognition Candidate Selection Section 9 Matching Section 10 Character Feature Vector Database 11 Output Section

Claims (6)

[Claims] 1. The handwritings of various handwritten characters are registered in advance as information of X, Y coordinates that continuously change with time, and the handwriting of a recognition target character continuously changes with time, X, Y.
A character recognition method characterized in that a character is recognized by inputting as Y coordinate information, and matching is performed using the input information and the previously registered information. 2. A waveform in which handwritings of various handwritten characters are previously input as continuous time changes of X and Y coordinates is normalized, and a partial feature amount is extracted from the normalized waveform. The extracted partial features are hashed and saved as a set of hash values, and the handwriting of the recognition target character is input as a continuous time change waveform of the X and Y coordinates. Normalization is performed, a partial feature amount is extracted from the normalized waveform, the extracted partial feature amount is hashed to form a set of hash values, and a set of hash values of recognition target characters obtained by the hashing, and A character recognition method characterized in that a character is recognized by comparing with a set of hash values stored in advance and matching is performed. 3. The character according to claim 2, wherein the recognized characters are output in descending order of the number of hash values for the various characters stored in advance that match the hash value for the input recognition target character. Recognition method. 4. The character recognition method according to claim 2, wherein when comparing the hash values, the hash values are weighted and compared. 5. An input means for inputting handwritings of handwritten characters as information of X, Y coordinates which continuously changes with time, and various handwritings of handwritten characters are stored in advance as information of time changes of X, Y coordinates. Matching is performed using a storage unit that is stored in advance, time change information about the handwriting of the recognition target character input by the input unit, and various time change information that is stored in advance in the storage unit. And a recognition means for recognizing a character by the character recognition device. 6. An input unit for inputting a character stroke as a two-dimensional coordinate series obtained by vertically projecting the position of the pen tip from the start of writing the recognition target character to the end of writing on the character input surface, and input by the input unit. Normalizing means for normalizing a two-dimensional coordinate series which is a character stroke, and a feature for calculating a feature vector series which is a set of partial feature vectors from the two-dimensional coordinate time series normalized by the normalizing means. A vector calculation means, a hashing means for calculating a hash value from each feature vector calculated by the feature vector calculation means, and a character having a feature vector from which each hash value calculated by the hashing means is calculated. A weight that stores a character weight database in which a set of a character code and its weight is registered in a list assigned to the hash value. And a weight for calculating the sum of weights by reading the weight information of each character from the character weight database based on each hash value calculated by the hashing means for the recognition target character input by the input means. A totaling means, a characteristic vector storage means for storing a character characteristic vector database in which characteristic vector series for various characters calculated by the characteristic vector calculation means are stored, and a character vector of each character calculated by the weight totalizing means. A feature vector sequence of a character having a higher total sum of weights is read from the character feature vector database, and a matching unit that performs a matching process with the read feature vector sequence and the feature vector sequence of the recognition target character is provided. Character recognition device.