CN111931672A - Handwriting recognition method and device, computer equipment and storage medium - Google Patents
Handwriting recognition method and device, computer equipment and storage medium Download PDFInfo
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- 238000004590 computer program Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/30—Writer recognition; Reading and verifying signatures
- G06V40/33—Writer recognition; Reading and verifying signatures based only on signature image, e.g. static signature recognition
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
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Abstract
The invention discloses a handwriting recognition method, a handwriting recognition device, computer equipment and a storage medium, wherein the handwriting recognition method is implemented by acquiring an image of a handwriting to be recognized; performing feature extraction on the image of the handwriting to be recognized to obtain the features of the handwriting to be recognized; comparing the handwriting features to be recognized with preset handwriting features in a preset handwriting library; and when the similarity between the handwriting feature to be recognized and the preset handwriting feature is greater than a preset value, determining that the handwriting image corresponding to the image of the handwriting to be recognized and the preset handwriting feature is the same as the handwriting. The handwriting recognition method, the handwriting recognition device, the computer equipment and the storage medium improve the efficiency and the accuracy of handwriting recognition.
Description
Technical Field
The present invention relates to the field of computers, and in particular, to a handwriting recognition method, apparatus, computer device, and storage medium.
Background
Identity authentication is an indispensable part in modern production and life, and generally adopts ways such as signature and signature to carry out identity authentication. When a signature is used as an identity authentication, since the signature can be counterfeited, it is usually necessary to identify the signature to confirm whether the signature is a handwriting of a signer. However, in the current judicial identification or other application scenarios, the identification of the signature mainly depends on the visual identification, which is inefficient and less accurate.
Disclosure of Invention
The embodiment of the invention provides a handwriting recognition method, a handwriting recognition device, computer equipment and a storage medium, and aims to improve the efficiency and accuracy of handwriting recognition.
The embodiment of the invention provides a handwriting recognition method, which comprises the following steps:
acquiring an image of a handwriting to be recognized;
performing feature extraction on the image of the handwriting to be recognized to obtain the features of the handwriting to be recognized;
comparing the handwriting features to be recognized with preset handwriting features in a preset handwriting library;
and when the similarity between the handwriting feature to be recognized and the preset handwriting feature is greater than a preset value, determining that the handwriting image corresponding to the image of the handwriting to be recognized and the preset handwriting feature is the same as the handwriting.
Preferably, the handwriting features to be recognized include: the length of the stroke, the inclination angle of the stroke and the turning radian of the stroke.
Preferably, the performing feature extraction on the handwriting image to be recognized to obtain the handwriting feature to be recognized includes:
converting the handwriting image to be recognized into a gray image;
extracting handwriting edge information in the gray level image of the handwriting image to be recognized to obtain a handwriting graph;
splitting strokes of fonts in the handwriting edge graph to obtain the strokes of the handwriting to be recognized;
and comparing the strokes of the handwriting to be recognized with the strokes of the standard font corresponding to the handwriting to be recognized, and calculating the length of the strokes of the handwriting to be recognized, the inclination angle of the strokes and/or the turning radian of the strokes.
Preferably, the splitting the strokes of the fonts in the handwriting edge line graph to obtain the strokes of the handwriting to be recognized includes:
identifying overlapping locations in strokes of the font;
and splitting the strokes of the font according to the overlapped part and the direction of the strokes of the font to obtain the strokes of the handwriting to be recognized.
Preferably, the preset handwriting features include: the length of stroke, the inclination of stroke and/or the radian that turns round of stroke, will treat to discern the handwriting characteristic and the preset handwriting characteristic in the preset handwriting storehouse and contrast, include:
and respectively comparing the length of the strokes, the inclination angle of the strokes and/or the turning radian of the strokes of the handwriting characteristics to be recognized with the preset handwriting characteristics.
Preferably, the image of the handwriting to be recognized includes a plurality of texts, and before the feature extraction is performed on the image of the handwriting to be recognized, the method further includes:
and dividing the image of the handwriting to be recognized of a plurality of characters into the image of the handwriting to be recognized of a single character.
The embodiment of the invention also provides a handwriting recognition device, which comprises:
the acquisition unit is used for acquiring an image of the handwriting to be recognized;
the characteristic extraction unit is used for extracting the characteristics of the image of the handwriting to be recognized to obtain the characteristics of the handwriting to be recognized;
the comparison unit is used for comparing the handwriting features to be recognized with preset handwriting features in a preset handwriting library;
and the determining unit is used for determining that the handwriting image corresponding to the image of the handwriting to be recognized and the preset handwriting feature is the same as the handwriting when the similarity between the handwriting feature to be recognized and the preset handwriting feature is greater than a preset value.
The embodiment of the invention also provides computer equipment which comprises a memory and a processor and is characterized in that the memory is stored with a handwriting recognition program, and the processor is used for realizing the steps of the handwriting recognition method when executing the handwriting recognition program.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the steps of the handwriting recognition method.
According to the handwriting recognition method, the handwriting recognition device, the computer equipment and the storage medium, after the image of the handwriting to be recognized is obtained, the handwriting feature to be recognized is extracted, then the handwriting feature to be recognized is compared with the preset handwriting feature, and the similarity between the handwriting feature to be recognized and the preset handwriting feature is compared to determine whether the handwriting to be recognized is the same as the handwriting corresponding to the preset handwriting feature.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flow chart of a handwriting recognition method in an embodiment of the invention;
FIG. 2 is a flow chart of a handwriting recognition method in another embodiment of the invention;
FIG. 3 is an image and a handwriting map of a handwriting to be recognized;
FIG. 4 is a schematic block diagram of a handwriting recognition apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "first", "second", "third", and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.
The embodiment of the invention provides a handwriting recognition method, which carries out image processing on an image of a handwriting to be recognized on the basis of a neural network so as to recognize the handwriting. Specifically, as shown in fig. 1, the handwriting recognition method includes the following steps:
s10: and acquiring an image of the handwriting to be recognized.
In this embodiment, the image of the handwriting to be recognized refers to an image of handwritten handwriting. For example, the handwriting to be recognized may be photographed or scanned to generate an image of the handwriting to be recognized. In a specific application scenario, the handwriting to be recognized may be a handwritten signature of the user.
S20: performing feature extraction on the image of the handwriting to be recognized to obtain the features of the handwriting to be recognized;
specifically, the image of the handwriting to be recognized may be input into a pre-trained convolutional neural network model to perform feature extraction, so as to obtain the characteristics of the handwriting to be recognized, and of course, other manners may also be used to perform feature extraction of the handwriting to be recognized, which is described herein in detail.
S30: comparing the handwriting features to be recognized with preset handwriting features in a preset handwriting library;
in this step, the preset handwriting library can be obtained by storing the handwriting collected in the database in advance, and the preset handwriting library contains various different handwriting characteristics, that is, preset handwriting characteristics. The preset handwriting characteristics can be obtained after characteristic extraction is carried out on the collected handwriting.
Specifically, after the handwriting feature to be recognized is provided, the handwriting feature to be recognized needs to be recognized, and the recognition method may be to compare the corresponding handwriting feature to be recognized with a preset handwriting feature, and output a similarity between the handwriting feature to be recognized and the preset handwriting feature.
S40: and when the similarity between the handwriting features to be recognized and the preset handwriting features is greater than a preset value, determining that the handwriting images corresponding to the images of the handwriting to be recognized and the preset handwriting features are the same.
In the embodiment, after the image of the handwriting to be recognized is obtained, the handwriting feature to be recognized is extracted, the handwriting feature to be recognized is compared with the preset handwriting feature, and the similarity between the handwriting feature to be recognized and the preset handwriting feature is compared to determine whether the handwriting corresponding to the handwriting to be recognized and the preset handwriting feature is the same or not.
In this embodiment, since the characteristics of the text are generally reflected on the strokes of the text or the intervals between the texts, the characteristics of the handwriting to be recognized may include one or more of the length of the strokes, the inclination angle of the strokes, the turning radian of the strokes, the intervals between the texts, whether the strokes exist, the trend of the handwriting, and the like. As shown in fig. 2, in order to extract the handwriting features to be recognized, the step S20 may include the following steps:
s21: and converting the image of the handwriting to be recognized into a gray image.
S22: and extracting the handwriting edge information in the gray level image to obtain a handwriting graph.
Since the image of the handwriting to be recognized contains a lot of useful information and also contains a lot of redundant information, and the features which can represent the handwriting most are extracted from the viewpoint of handwriting recognition, so that the recognition can be better performed, in the step S21, the image of the handwriting to be recognized is converted into a gray image, and the influence of the background color on the process of extracting the features of the handwriting to be recognized can be effectively reduced.
In step S22, the handwriting graph contains many original forms of handwriting, so that the edge information of the extracted note can be filtered to the greatest extent to extract valid information. The handwriting graph may be as shown in fig. 3, wherein fig. 3(a) is an image of the handwriting to be recognized, and fig. 3(b) is the handwriting graph.
S23: and splitting strokes of the fonts in the handwriting edge graph to obtain the strokes of the handwriting to be recognized.
It should be noted that the strokes of the handwriting to be recognized include repeated strokes, such as the text of fig. 3, which includes two "|" and two "-".
S24: comparing the strokes of the handwriting to be recognized with the strokes of the standard font corresponding to the handwriting to be recognized, and calculating the length of the strokes of the handwriting to be recognized, the inclination angle of the strokes and/or the turning radian of the strokes.
In this step, the standard font may be a song style, a regular style, a bold style, and so on, to name a few. The degree of tilt of the strokes is relative to the strokes corresponding to the standard font; the length of the strokes refers to the length of the strokes written along one direction, such as the length of I and I; the turning radian of the strokes refers to the radian of strokes needing to be turned, such as the corner radians of the strokes of Chinese character, , Jiong and the like.
In the step S23, splitting the strokes of the font in the font edge map may include the following steps:
s231: overlapping locations in strokes of the font are identified.
During writing, the overlapping part reflects the stroke superposition of the font during writing, and exemplarily, as shown in fig. 3, the overlapping part in the stroke refers to the point O (including O1, O2, O3) in the figure. In particular, a convolutional neural network may be employed to identify overlapping sites in the strokes.
S232: and splitting the strokes of the font according to the overlapped part and the direction of the strokes of the font to obtain the strokes of the handwriting to be recognized.
In this embodiment, in order to implement the comparison of the handwriting features, the preset handwriting features may be stored in a preset handwriting library in advance, and the preset handwriting features also include: the length of the stroke, the inclination angle of the stroke and/or the turning radian of the stroke. Specifically, when the handwriting characteristics are compared, the similarity between the handwriting characteristics to be recognized and the preset handwriting characteristics can be determined by respectively comparing the lengths of the strokes, the inclination angles of the strokes and/or the turning radians of the strokes of the handwriting characteristics to be recognized and the preset handwriting characteristics.
In some cases, the image of the handwriting to be recognized may include a plurality of characters, and therefore, before feature extraction is performed on the image of the handwriting to be recognized, the image of the handwriting to be recognized of the plurality of characters needs to be divided into the images of the handwriting to be recognized of the single character. Certainly, since the space between the characters can also reflect the characteristics of the handwritten characters, in order to increase the accuracy of handwriting recognition, before dividing the image of the handwriting to be recognized of a plurality of characters into the image of the handwriting to be recognized of a single character, the space between the characters can be recognized first, and the space can be used as one of the characteristics of the handwriting to be recognized.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In one embodiment, a handwriting recognition apparatus is provided, and the handwriting recognition apparatus corresponds to the handwriting recognition method in the above embodiments one to one. As shown in fig. 4, the handwriting recognition apparatus includes:
an acquisition unit 10 for acquiring an image of a handwriting to be recognized;
the feature extraction unit 20 is configured to perform feature extraction on the image of the handwriting to be recognized to obtain features of the handwriting to be recognized;
the comparison unit 30 is used for comparing the handwriting features to be recognized with preset handwriting features in a preset handwriting library;
and the determining unit 40 is configured to determine that the handwriting image corresponding to the image of the handwriting to be recognized is the same as the handwriting image corresponding to the preset handwriting feature when the similarity between the handwriting feature to be recognized and the preset handwriting feature is greater than a preset value.
For the specific limitations of the handwriting recognition device, reference may be made to the above limitations of the handwriting recognition method, which are not described herein again. The modules in the handwriting recognition device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the handwriting recognition method when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned handwriting recognition method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (9)
1. A handwriting recognition method, comprising:
acquiring an image of a handwriting to be recognized;
performing feature extraction on the image of the handwriting to be recognized to obtain the features of the handwriting to be recognized;
comparing the handwriting features to be recognized with preset handwriting features in a preset handwriting library;
and when the similarity between the handwriting feature to be recognized and the preset handwriting feature is greater than a preset value, determining that the handwriting image corresponding to the image of the handwriting to be recognized and the preset handwriting feature is the same as the handwriting.
2. The handwriting recognition method according to claim 1, wherein the handwriting features to be recognized comprise: the length of the stroke, the inclination angle of the stroke and the turning radian of the stroke.
3. The handwriting recognition method according to claim 2, wherein the extracting the features of the handwriting image to be recognized to obtain the features of the handwriting to be recognized comprises:
converting the handwriting image to be recognized into a gray image;
extracting handwriting edge information in the gray level image of the handwriting image to be recognized to obtain a handwriting graph;
splitting strokes of fonts in the handwriting edge graph to obtain the strokes of the handwriting to be recognized;
and comparing the strokes of the handwriting to be recognized with the strokes of the standard font corresponding to the handwriting to be recognized, and calculating the length of the strokes of the handwriting to be recognized, the inclination angle of the strokes and/or the turning radian of the strokes.
4. The handwriting recognition method of claim 3, wherein the splitting the strokes of the font in the handwriting edge line graph to obtain the strokes of the handwriting to be recognized comprises:
identifying overlapping locations in strokes of the font;
and splitting the strokes of the font according to the overlapped part and the direction of the strokes of the font to obtain the strokes of the handwriting to be recognized.
5. The handwriting recognition method of claim 2, wherein the preset handwriting features comprise: the length of stroke, the inclination of stroke and/or the radian that turns round of stroke, will treat to discern the handwriting characteristic and the preset handwriting characteristic in the preset handwriting storehouse and contrast, include:
and respectively comparing the length of the strokes, the inclination angle of the strokes and/or the turning radian of the strokes of the handwriting characteristics to be recognized with the preset handwriting characteristics.
6. The handwriting recognition method according to claim 3, wherein the image of the handwriting to be recognized comprises a plurality of characters, and before feature extraction of the image of the handwriting to be recognized, the method further comprises:
and dividing the image of the handwriting to be recognized of a plurality of characters into the image of the handwriting to be recognized of a single character.
7. A handwriting recognition apparatus, comprising:
the acquisition unit is used for acquiring an image of the handwriting to be recognized;
the characteristic extraction unit is used for extracting the characteristics of the image of the handwriting to be recognized to obtain the characteristics of the handwriting to be recognized;
the comparison unit is used for comparing the handwriting features to be recognized with preset handwriting features in a preset handwriting library;
and the determining unit is used for determining that the handwriting image corresponding to the image of the handwriting to be recognized and the preset handwriting feature is the same as the handwriting when the similarity between the handwriting feature to be recognized and the preset handwriting feature is greater than a preset value.
8. A computer device comprising a memory and a processor, wherein the memory has stored therein a handwriting recognition program, and the processor is configured to implement the steps of the handwriting recognition method according to any one of claims 1 to 6 when executing the handwriting recognition program.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the handwriting recognition method according to any one of claims 1 to 6.
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| CN112800961A (en) * | 2021-01-28 | 2021-05-14 | 北京有竹居网络技术有限公司 | Stroke writing sequence detection method, device, medium and electronic equipment |
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