CN112487881A - Handwriting content analysis method and device and electronic equipment - Google Patents
Handwriting content analysis method and device and electronic equipmentDownload PDFInfo
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- CN112487881A CN112487881ACN202011268011.XACN202011268011ACN112487881ACN 112487881 ACN112487881 ACN 112487881ACN 202011268011 ACN202011268011 ACN 202011268011ACN 112487881 ACN112487881 ACN 112487881A
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/32—Digital ink
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
- G06F3/033—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
- G06F3/0354—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor with detection of 2D relative movements between the device, or an operating part thereof, and a plane or surface, e.g. 2D mice, trackballs, pens or pucks
- G06F3/03545—Pens or stylus
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
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Abstract
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for parsing handwriting content, and an electronic device.
Background
Dot matrix digital intelligence pen is one kind through printing the invisible dot matrix pattern of one deck on ordinary paper, and the high-speed camera of digital pen front end catches the motion trail of nib at any time, and pressure sensor passes pressure data back data processor simultaneously, finally passes through the novel instrument of writing of the outside transmission of bluetooth or USB line with information.
Different from ordinary paper and pens, the information comprises information such as paper type, source, page number, position, handwriting coordinates, motion trail, pen point pressure, stroke sequence, pen moving time, pen moving speed and the like, and the handwriting recording process is synchronous with the writing process. When writing, the dot matrix digital pen stores the written characters or pictures on the paper in the computer in the form of bitmap to form a document, and if necessary, the characters or pictures can be synchronously displayed by projection.
How to analyze and recognize the content of the characters of the intelligent pen based on the cloud platform, and the processing efficiency of handwriting content analysis is improved, which becomes a problem to be solved.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide a method, an apparatus, and an electronic device for parsing handwriting content, so as to at least partially solve the problems in the prior art.
In a first aspect, an embodiment of the present disclosure provides a method for parsing handwriting content, including:
acquiring a character set formed based on an imaging file, wherein the imaging file is formed based on an intelligent pen handwriting;
judging whether an individualized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file;
when the personalized semantic database corresponding to the graphical file exists, calling the personalized semantic database to analyze the content in the character set to obtain an analysis result;
and re-determining the content in the character set based on the analysis result.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a character set formed based on a graphic file includes:
judging whether a new graphical file is generated in the cloud service platform;
if yes, after the cloud service platform completes character recognition on the graphical file, a character set obtained by the graphical file recognition is obtained.
According to a specific implementation manner of the embodiment of the present disclosure, the determining whether there exists an individualized semantic database corresponding to the graphical file in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file includes:
inputting the handwriting matrix and the storage value into a database in the cloud platform;
and inquiring whether a personalized semantic database corresponding to the handwriting matrix and the storage value exists.
According to a specific implementation manner of the embodiment of the present disclosure, the invoking the personalized semantic database to analyze the content in the character set includes:
performing word segmentation processing on the characters in the character set to obtain one or more word segmentation vectors;
performing vector comparison on the word segmentation vector and a semantic vector in the personalized semantic database;
and determining whether the word segmentation vectors in the character set are correct or not based on the result of the vector comparison.
According to a specific implementation manner of the embodiment of the present disclosure, the re-determining the content in the character set based on the parsing result includes:
and when the word segmentation vector in the display character set in the analysis result is inconsistent with the semantic vector in the personalized semantic database, correcting the content in the word segmentation vector based on the semantic vector in the personalized semantic database.
According to a specific implementation manner of the embodiment of the present disclosure, before the obtaining of the character set formed based on the graphic file, the method further includes:
acquiring an imaging file needing character recognition, wherein the imaging file is generated by handwriting of an intelligent pen;
and searching the handwriting matrix and the storage value for generating the graphical file in the cloud service platform.
According to a specific implementation manner of the embodiment of the present disclosure, the method further includes:
judging whether historical characters corresponding to the handwriting matrix and the stored value exist in a preset character recognition database;
and if so, directly using the historical characters as the characters of the graph to be recognized in the graphical file.
In a second aspect, an embodiment of the present disclosure provides a handwriting content parsing apparatus, including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a character set formed based on an imaging file, and the imaging file is formed based on an intelligent pen handwriting;
the judging module is used for judging whether an individualized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file;
the analysis module is used for calling the personalized semantic database to analyze the content in the character set when the personalized semantic database corresponding to the graphical file exists, so as to obtain an analysis result;
and the determining module is used for re-determining the content in the character set based on the analysis result.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for parsing handwriting content in any of the implementations of the first aspect or the first aspect.
In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for parsing handwriting content in any implementation manner of the foregoing first aspect or first aspect.
In a fifth aspect, the disclosed embodiments also provide a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the method for handwriting content parsing in the foregoing first aspect or any implementation manner of the first aspect.
The handwriting content analysis scheme in the embodiment of the disclosure comprises the steps of obtaining a character set formed based on an imaging file, wherein the imaging file is formed based on an intelligent pen handwriting; judging whether an individualized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file; when the personalized semantic database corresponding to the graphical file exists, calling the personalized semantic database to analyze the content in the character set to obtain an analysis result; and re-determining the content in the character set based on the analysis result. By the processing scheme, the efficiency of analyzing the handwriting content is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for parsing handwriting content according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of another method for parsing handwriting content according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of another method for parsing handwriting content according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of another method for parsing handwriting content according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a handwriting content analysis apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a handwriting content analyzing method. The handwriting content parsing method provided by the embodiment may be executed by a computing device, which may be implemented as software or implemented as a combination of software and hardware, and may be integrally disposed in a server, a client, and the like.
Referring to fig. 1, a method for parsing handwriting content in an embodiment of the present disclosure may include the following steps:
s101, acquiring a character set formed based on a graphical file, wherein the graphical file is formed based on the intelligent pen handwriting.
The method comprises the steps that the writing handwriting of the intelligent pen is displayed in the graphical file in a graphical mode, in order to further obtain the content written by a user in the graphical file, character recognition can be conducted on the writing handwriting of the user in the graphical file, so that a character set corresponding to the graphical file is obtained, and the writing content of the intelligent pen in the graphical file can be determined through the character set.
However, in the process of converting a graphic file into characters, there are cases where recognition errors occur, and for this purpose, it is necessary to perform content analysis and recognition based on a recognized character set.
S102, judging whether an individualized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file.
The handwriting matrix is a feature matrix extracted from elements representing handwriting features such as position coordinates, acceleration values and pressure values contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and features of the user handwriting, and the user handwriting can be restored through the handwriting matrix.
The storage value is a characteristic value of a historical handwriting matrix of the user stored in the cloud service platform, and the characters contained in the graphical file have a one-to-one correspondence with the handwriting matrix or the storage value.
In addition, a personalized semantic database is arranged in the cloud service platform, and the personalized semantic database is generated based on a historical record for character recognition of the graphical file and can record a handwriting matrix and personalized semantic content corresponding to a stored value.
S103, when the personalized semantic database corresponding to the graphical file exists, the personalized semantic database is called to analyze the content in the character set to obtain an analysis result.
Specifically, the characters in the character set may be subjected to word segmentation processing to obtain one or more word segmentation vectors; performing vector comparison on the word segmentation vector and a semantic vector in the personalized semantic database; and determining whether the word segmentation vectors in the character set are correct or not based on the result of the vector comparison.
S104, based on the analysis result, the content in the character set is determined again.
And when the word segmentation vector is inconsistent with the semantic vector in the personalized semantic database, correcting the character vector in the character set by using the semantic vector in the personalized semantic database, and further re-determining the content in the character set.
Referring to fig. 2, according to a specific implementation manner of the embodiment of the present disclosure, the acquiring a character set formed based on a graphic file includes:
s201, judging whether a new graphical file is generated in the cloud service platform;
and S202, if yes, after the cloud service platform completes character recognition on the graphical file, obtaining a character set obtained by the graphical file recognition.
Referring to fig. 3, according to a specific implementation manner of the embodiment of the present disclosure, the determining whether a personalized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on a handwriting matrix and a stored value corresponding to the graphical file includes:
s301, inputting the handwriting matrix and the stored value into a database in the cloud platform;
s302, inquiring whether a personalized semantic database corresponding to the handwriting matrix and the storage value exists.
Referring to fig. 4, according to a specific implementation manner of the embodiment of the present disclosure, the invoking the personalized semantic database to parse the content in the character set includes:
s401, performing word segmentation processing on the characters in the character set to obtain one or more word segmentation vectors;
s402, carrying out vector comparison on the word segmentation vector and the semantic vector in the personalized semantic database;
s403, determining whether the word segmentation vectors in the character set are correct or not based on the result of vector comparison.
According to a specific implementation manner of the embodiment of the present disclosure, the re-determining the content in the character set based on the parsing result includes: and when the word segmentation vector in the display character set in the analysis result is inconsistent with the semantic vector in the personalized semantic database, correcting the content in the word segmentation vector based on the semantic vector in the personalized semantic database.
According to a specific implementation manner of the embodiment of the present disclosure, before the obtaining of the character set formed based on the graphic file, the method further includes: acquiring an imaging file needing character recognition, wherein the imaging file is generated by handwriting of an intelligent pen; and searching the handwriting matrix and the storage value for generating the graphical file in the cloud service platform.
According to a specific implementation manner of the embodiment of the present disclosure, the method further includes: judging whether historical characters corresponding to the handwriting matrix and the stored value exist in a preset character recognition database; and if so, directly using the historical characters as the characters of the graph to be recognized in the graphical file.
As an alternative, a graphical file that needs character recognition may be obtained, the graphical file being generated by handwriting of the smart pen.
The graphical file is formed by converting the writing track of the intelligent pen and is used for displaying the writing track of the intelligent pen in a graphical mode, and the graphical file can be various types of graphical files.
Before character recognition is carried out, a newly generated graphical file can be directly searched in a cloud service platform, and the graphical file needing to be subjected to character recognition in real time is obtained.
As an optional mode, a handwriting matrix and a storage value for generating the graphical file can be searched in a cloud service platform.
The handwriting matrix is a feature matrix extracted from elements representing handwriting features such as position coordinates, acceleration values and pressure values contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and features of the user handwriting, and the user handwriting can be restored through the handwriting matrix.
The storage value is a characteristic value of a historical handwriting matrix of the user stored in the cloud service platform, and the characters contained in the graphical file have a one-to-one correspondence with the handwriting matrix or the storage value.
As an alternative, it may be determined whether there is a historical character corresponding to the handwriting matrix and the stored value in a preset character recognition database.
The character recognition database stores the characters which are recognized before, and simultaneously stores the one-to-one correspondence between the recognized characters and the handwriting matrix or the stored value.
As an optional mode, if yes, the historical characters are directly used as characters of a graph to be recognized in the graphical file.
By the method, the characters of the graph to be recognized can be directly recognized based on the historical recognition records, and character recognition is not needed to be carried out on each graph in each graphical file, so that the character recognition efficiency is greatly improved.
According to a specific implementation manner of the embodiment of the present disclosure, after determining whether there is a historical character corresponding to the handwriting matrix and the stored value in the preset character recognition database, the method further includes:
when the historical characters corresponding to the handwriting matrix and the stored values do not exist in a preset character recognition database, directly recognizing the characters on the graphical file;
and storing the recognized characters and handwriting matrixes or stored values corresponding to the recognized characters into the character recognition database.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a graphical file that needs character recognition includes: searching a newly generated graphical file in the cloud service platform;
and taking the newly generated graphical file as a graphical file needing character recognition.
According to a specific implementation manner of the embodiment of the present disclosure, the searching and generating the handwriting matrix and the stored value of the graphical file in the cloud service platform includes: and inquiring the handwriting matrix and the storage value of the graphical file in a data acquisition module of the cloud service platform.
According to a specific implementation manner of the embodiment of the present disclosure, the determining whether there is a historical character corresponding to the handwriting matrix and the stored value in the preset character recognition database includes:
inputting the handwriting matrix and the storage value into the character recognition database to execute query operation;
and judging whether historical characters corresponding to the handwriting matrix and the stored value exist or not based on the result of the query operation.
According to a specific implementation manner of the embodiment of the present disclosure, the directly using the historical characters as characters of a graph to be recognized in a graphical file includes:
acquiring the position coordinates of the graph to be identified in the graphical file;
and setting the historical characters at the position coordinates of the graph to be recognized in the graphical file to obtain a character recognition result.
According to a specific implementation manner of the embodiment of the present disclosure, before the obtaining of the graphic file that needs character recognition, the method further includes:
acquiring handwriting data needing to be imaged, wherein the handwriting data comprises paper information, a handwriting matrix and a stored value when an intelligent pen writes, the handwriting matrix is generated by an intelligent pen client based on the intelligent pen handwriting, and the stored value is generated by a cloud service platform based on historical handwriting data of a user;
searching a handwriting matrix and a stored value corresponding to the current paper information in a graphical module of the cloud service platform;
sequencing the handwriting matrix and the stored value based on the generation time corresponding to the handwriting matrix and the stored value to form a graphical sequencing result;
and forming a graphical file of the handwriting data on the current page according to the handwriting matrix and the graphical style corresponding to the storage value on the current page in sequence based on the graphical sorting result.
As an optional mode, handwriting data needing to be imaged can be obtained, the handwriting data comprise paper information, a handwriting matrix and a stored value when the intelligent pen writes, the handwriting matrix is generated by the intelligent pen client based on the intelligent pen handwriting, and the stored value is generated by the cloud service platform based on historical handwriting data of a user.
After the handwriting data is generated at the intelligent pen end, in order to improve the recognition efficiency of the handwriting of the intelligent pen, the handwriting data of the intelligent pen can be uploaded to the cloud service platform, the handwriting data is processed through the cloud service platform, as a way of the handwriting data, the handwriting data is converted into a graphic file, and the real shape of the handwriting is displayed through the graphic file.
Therefore, paper information, a handwriting matrix and a storage value formed by the intelligent pen during writing can be acquired from the handwriting data.
The paper information is used to describe on which paper the smart pen has performed handwriting writing, for example, the user writes 10 pages of content through the smart pen, and at this time, the content written by the user can be searched through 1-10 pages.
The handwriting matrix is a feature matrix extracted from elements representing handwriting features such as position coordinates, acceleration values and pressure values contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and features of the user handwriting, and the user handwriting can be restored through the handwriting matrix.
The stored value is a characteristic value of the historical handwriting matrix of the user stored in the cloud service platform, and when the handwriting matrix generated during writing of the intelligent pen is stored in the historical handwriting matrix stored in the cloud service platform, the stored value of the historical handwriting matrix is used for replacing the newly generated handwriting matrix, so that the data processing process is saved, and the system resources are reduced.
As an optional mode, a handwriting matrix and a stored value corresponding to the current paper information can be searched in a graphical module of the cloud service platform.
The cloud service platform can comprise a graphical module, and handwriting matrixes and stored values corresponding to current paper information stored in the database can be inquired through the graphical module, so that the handwriting of a user can be restored based on the inquired handwriting matrixes and stored values.
As an optional manner, the handwriting matrix and the stored value may be sorted based on the generation time corresponding to the handwriting matrix and the stored value, so as to form a graphical sorting result.
Specifically, the handwriting matrix and the handwriting corresponding to the stored value may be sorted in an ascending manner or a descending manner, so that the handwriting of the current page may be sorted according to the actual generation sequence or the reverse sequence of the handwriting.
As an optional manner, based on the graphical sorting result, a graphical file of the handwriting data on the current page may be formed on the current page in sequence according to the handwriting matrix and the graphical style corresponding to the storage value.
After the current page is sequentially sorted according to the handwriting matrixes and the graphic styles corresponding to the storage values, a pressure value or a position coordinate corresponding to each handwriting matrix or the storage values can be further obtained, the thickness characteristics of the handwriting are determined through the pressure values, the position coordinate of the handwriting on the current page is determined through the position coordinate, and finally a graphical handwriting file is formed.
Through the content of the embodiment, the graphical operation can be quickly executed on the handwriting, and the efficiency of analyzing the handwriting content is improved.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring handwriting data that needs to be graphed includes: inquiring newly generated handwriting data in the cloud service platform; and considering the newly generated handwriting data as the handwriting data needing to be imaged.
According to a specific implementation manner of the embodiment of the present disclosure, in the graphical module of the cloud service platform, searching the handwriting matrix and the stored value corresponding to the current paper information includes: executing query operation in a database of the cloud service platform based on the acquired identification of the smart pen; and obtaining a handwriting matrix and a storage value corresponding to the current paper information based on the query result.
According to a specific implementation manner of the embodiment of the present disclosure, the sorting the handwriting matrix and the stored value based on the generation time corresponding to the handwriting matrix and the stored value includes: the handwriting matrixes and the generation time of the storage values are arranged in an ascending order; and determining the arrangement sequence of the handwriting matrix and the storage values based on the ascending arrangement result.
According to a specific implementation manner of the embodiment of the present disclosure, the forming, on the basis of the graphical sorting result, a graphical file of the handwriting data on the current page according to the handwriting matrix and the graphical style corresponding to the storage value in sequence includes: searching the handwriting position coordinates and the pressure values corresponding to the handwriting matrixes or the storage values according to the time sequence; and generating the graphical handwriting corresponding to the handwriting matrix or the storage value based on the handwriting position coordinates and the pressure value.
According to a specific implementation manner of the embodiment of the present disclosure, before acquiring handwriting data that needs to be patterned, the method further includes: and dividing the acquired handwriting data based on the pressure value and the acceleration value to form a plurality of handwriting data sections.
According to a specific implementation manner of the embodiment of the disclosure, after the obtained handwriting data is divided based on the pressure value and the acceleration value to form a plurality of handwriting data segments, the method further includes: packaging the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence corresponding to the handwriting data segment to form a handwriting matrix corresponding to the handwriting data segment; sending the characteristic value corresponding to the handwriting matrix to a data acquisition module in a cloud service platform so that the data acquisition module can inquire whether stored values similar to the characteristic value exist in handwriting data stored in the cloud service platform or not; when the storage values similar to the characteristic values already exist in the cloud service platform, directly calling the storage matrix corresponding to the storage values as the characteristic matrix corresponding to the characteristic values.
The intelligent pen is at the in-process of writing, can generate the writing orbit of intelligent pen through the mode of dot matrix, writes the multiple data that the orbit can include the intelligent pen, for example, the generation time of handwriting, the pressure value of nib, the position coordinate of writing the pen on writing paper, the acceleration value when writing etc.. The data are sampled and arranged according to time training, so that a time sequence, a pressure value sequence, a position coordinate sequence and an acceleration value sequence can be formed, and the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence can be used for describing and restoring handwriting of a user.
As an alternative, the handwriting data may be divided based on pressure values and acceleration values to form a plurality of handwriting data segments.
If the handwriting of the intelligent pen is directly uploaded to a server side for data processing, the processing speed of the data is low due to the fact that the data volume is too large, and therefore the handwriting data of the intelligent pen needs to be processed.
As one approach, the first pressure value threshold and the second acceleration threshold may be set first. The pressure value sequence is divided based on the first pressure value threshold value to form a plurality of pressure value sequences, for example, a pressure value sequence part larger than the first pressure value threshold value can be divided to form one or more pressure value sequences to represent one or more handwriting strokes actually written by the user.
After the pressure value sequences are determined, the acceleration value sequences corresponding to each pressure value sequence can be further searched, and based on a second acceleration value threshold, the acceleration value sequences are cut to form a plurality of acceleration value sequences. Through the second acceleration threshold, handwriting data of the user in a pause state can be filtered, and therefore segmented handwriting data are further simplified. And finally, dividing the handwriting data based on the time sequence corresponding to the acceleration value sequence.
As an optional manner, the time sequence, the pressure value sequence, the position coordinate sequence, and the acceleration value sequence corresponding to the handwriting data segment may be encapsulated to form a handwriting matrix corresponding to the handwriting data segment.
The time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence can be respectively used as row vectors or column vectors, and then one or more handwriting matrixes corresponding to the handwriting data segments are formed.
As an optional mode, the eigenvalue corresponding to the handwriting matrix may be sent to a data acquisition module in the cloud service platform, so that the data acquisition module can query whether stored values similar to the eigenvalue exist in the handwriting data already stored in the cloud service platform; when the stored values similar to the characteristic values exist in the cloud service platform, a storage matrix corresponding to the stored values is directly called as the characteristic matrix corresponding to the characteristic values, and when the stored values similar to the characteristic values do not exist in the cloud service platform, an intelligent pen client generating the characteristic data is informed to upload the handwriting matrix to the data acquisition module.
The storage value is a writing characteristic value formed based on previous writing of a user, whether a storage matrix stored in the cloud service platform is called or not can be determined by comparing whether the characteristic value is similar to the storage value or not, and data in the writing matrix is directly replaced by numerical values in the storage matrix, so that data transmission and calculation amount are further reduced, and the writing processing efficiency is improved.
By uploading the characteristic values, the calculation amount of the data can be further reduced, and the calculation process of the data is simplified.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring handwriting data of the smart pen includes: monitoring whether pressure data generation exists in the smart pen; and if so, acquiring handwriting data generated by the intelligent pen.
According to a specific implementation manner of the embodiment of the present disclosure, the dividing the handwriting data according to the pressure value and the acceleration value includes: dividing the pressure value sequence based on a first pressure value threshold value to form a plurality of pressure value sequences. The pressure value sequence is divided based on the first pressure value threshold value to form a plurality of pressure value sequences, for example, a pressure value sequence part larger than the first pressure value threshold value can be divided to form one or more pressure value sequences to represent one or more handwriting strokes actually written by the user.
Searching an acceleration value sequence corresponding to each pressure value sequence; and based on the second acceleration value threshold value, performing cutting operation on the acceleration value sequence to form a plurality of acceleration value sequences. And based on the second acceleration value threshold value, performing cutting operation on the acceleration value sequence to form a plurality of acceleration value sequences. Through the second acceleration threshold, handwriting data of the user in a pause state can be filtered, and therefore segmented handwriting data are further simplified. And dividing the handwriting data based on the time sequence corresponding to the acceleration value sequence. With the above embodiment, the amount of calculation of data can be further reduced by setting the threshold value.
According to a specific implementation manner of the embodiment of the present disclosure, the encapsulating the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence corresponding to the handwriting data segment includes:
and taking the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence as row vectors of the matrix, and forming a handwriting matrix corresponding to the handwriting data section in a time sequence pair mode.
According to a specific implementation manner of the embodiment of the present disclosure, before the eigenvalue corresponding to the handwriting matrix is sent to a data acquisition module in a cloud service platform, the method further includes:
and respectively calculating the characteristic values of the divided handwriting data to form a characteristic value sequence of the handwriting data.
According to a specific implementation manner of the embodiment of the present disclosure, the method further includes:
and performing graphical processing on the handwriting data obtained by the data acquisition module by using a graphical module in the cloud service platform to obtain handwriting image data of the intelligent pen.
According to a specific implementation manner of the embodiment of the present disclosure, the method further includes: aiming at the handwriting image data, performing character recognition by using a character recognition module in a cloud service platform to obtain character data corresponding to the handwriting image data; and performing content analysis service on the character data through a content analysis module in the cloud service platform to form writing content data corresponding to the handwriting data.
The intelligent pen is used as an electronic equipment terminal, writing data of a user can be collected in the modes of pressure, acceleration value and the like under the use of the user, so that writing data are formed, and the writing data are used as handwriting data of the user and are transmitted to the cloud service platform in a wireless or wired mode.
The cloud service platform is connected with the intelligent pen terminal in a wired or wireless mode, a plurality of data processing modules can be arranged in the cloud service platform, and writing data generated by the intelligent pen are processed and analyzed through the processing modules, so that the handwriting recognition and identification of a user become more accurate and efficient.
As a mode, a data acquisition module is arranged in the cloud service platform, and handwriting data written by a user can be acquired and stored through the data acquisition module.
The data acquisition module can be set to have extremely high flexibility and expandability, and can adjust the resource configuration in time according to the data acquisition requirement, ensure the quick response of the system and avoid the data blockage caused by the quick expansion of the traffic.
The data acquisition module is provided with a data storage service unit and is used for supporting high-concurrency data storage service and providing support for distributed computation by adopting distributed data storage service in a big data architecture.
The handwriting of the user collected by the data collection module is usually stored in the modes of time, position coordinates, pressure values, acceleration values and the like, and therefore, the collected handwriting data needs to be subjected to imaging processing to be restored into the real handwriting of the user.
Therefore, various data such as time, configuration, movement, pressure and the like of the acquired original handwriting data can be subjected to structural processing, the original handwriting data can be calculated into image and video data through a graphical calculation module, and finally the image and video data are output in various output formats such as bitmap, vector diagram and dynamic video, and the handwriting of the user is fixed in the form of the image.
After the handwriting image data corresponding to the writing is obtained, the graphical characters can be identified by utilizing a character identification module arranged in the cloud service platform, so that the character data corresponding to the handwriting image is obtained.
The character recognition function of handwriting can be set in the character recognition module, and data written by a user can be quickly converted into standard characters which can be recognized by a computer, for example, recognition characters of contents such as Chinese characters, letters, symbols and formulas can be set.
As an optional mode, a semantic understanding function based on a natural language processing technology is added in a handwriting recognition module, the probability of character content can be calculated according to text content of context, and the accuracy of character recognition is improved.
After the handwriting of the user is recognized as the standard character, the content can be analyzed by utilizing a content analysis module arranged on a cloud service platform to execute artificial intelligent technologies such as natural language processing, machine learning and deep learning, and the services comprise entity recognition, relation extraction, semantic understanding, abstract extraction, keyword extraction, knowledge map construction and the like of the character content.
By analyzing the content, the total judgment and analysis can be carried out on the writing content of the user by integrating all the context content of the handwriting of the user, and the accuracy of the writing content data is further improved.
Through the content and the scheme of the embodiment, the handwriting of the user can be processed at the cloud end, so that the processing efficiency and the accuracy of the handwriting of the intelligent pen are improved.
According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and performing characteristic analysis on the writing behavior of the user based on the content data to form a writing characteristic font library corresponding to the user. For example, the writing behaviors of the user including the writing characteristics of a single character, the specific drawing writing characteristics, the overall writing habit, the writing speed and other writing characteristics can be extracted and analyzed, a unique character characteristic library of the specific user can be generated, handwriting authentication of the user is realized, each character written by the user through the intelligent pen can identify a writer, and scenes such as file signature authenticity confirmation, examination cheating imitation and the like can be applied.
According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and comparing and analyzing the handwriting image data with preset target handwriting data according to target characteristics, and determining an analysis result of the handwriting image data based on the comparison and analysis result. For example, the system can receive preset written/drawn target characters/graphics, collect the written content of the user, and calculate the similarity between the target and the written result by using the methods of graph hash value comparison, cosine similarity comparison, mutual information comparison, and the like, so as to judge the similarity between the written content of the user and the target, and the system can be applied to scenes such as calligraphy learning, drawing learning, and the like.
According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes:
firstly, comparing the content data with preset target data to form a content comparison result.
As an application scenario, the content data may be answer data written during a process of a user performing an examination or the like, the target data may be answer data corresponding to the examination content, and the comparison result may be formed by comparing the content data with the target data.
Secondly, based on the content comparison result, determining a similarity value between the content data and the target data.
Through the comparison result formed in the steps, the similarity value between the content data and the target data can be determined, and therefore the accuracy of the handwriting data solved by the user can be further determined. By the content of the embodiment, whether the content written by the user is correct can be further judged based on the writing data of the user.
According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and simultaneously sending the handwriting image data and the content data to a client so as to facilitate the client to display the handwriting image data or the content data.
According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: identifying the content data, and judging whether table content data exists in the content data or not; and if so, displaying the table content data in a table form.
By the method, the data which needs to be displayed in the form mode can be identified, and the part of content is displayed in the form mode, so that the processing function of the intelligent pen data is improved.
According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and performing semantic analysis on the content data, and judging whether recommended data responding to the content data exists or not. The recommended data may be data corresponding to content data, and as an example, the content data is case data of a user written by a doctor by handwriting or the like, and by analyzing the case data, prescription data (recommended data) corresponding to the case data can be recommended, thereby facilitating the doctor to select part of the recommended data according to actual needs. And if so, generating recommendation data corresponding to the content data. With this embodiment, the writing efficiency of the written content data can be further improved.
Corresponding to the above embodiment, referring to fig. 5, the embodiment of the present application further discloses a handwriting content parsing apparatus 50, including:
an obtaining module 501, configured to obtain a character set formed based on a graphical file, where the graphical file is formed based on a smart pen handwriting;
a judging module 502, configured to judge whether a personalized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on a handwriting matrix and a stored value corresponding to the graphical file;
the parsing module 503 is configured to, when a personalized semantic database corresponding to the graphic file exists, invoke the personalized semantic database to parse the content in the character set to obtain a parsing result;
a determining module 504, configured to re-determine content in the character set based on the parsing result.
For parts not described in detail in this embodiment, reference is made to the contents described in the above method embodiments, which are not described again here.
Referring to fig. 6, an embodiment of the present disclosure also provides an electronic device 60, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the handwriting content parsing method of the foregoing method embodiments.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the handwriting content parsing method in the aforementioned method embodiments.
Referring now to FIG. 6, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 60 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In theRAM 603, various programs and data necessary for the operation of the electronic apparatus 60 are also stored. Theprocessing device 601, the ROM602, and theRAM 603 are connected to each other via abus 604. An input/output (I/O)interface 605 is also connected tobus 604.
Generally, the following devices may be connected to the I/O interface 605:input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.;output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like;storage 608 including, for example, tape, hard disk, etc.; and acommunication device 609. The communication means 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. While the figures illustrate an electronic device 60 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from theROM 602. The computer program, when executed by theprocessing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (10)
1. A method for parsing handwriting content, comprising:
acquiring a character set formed based on an imaging file, wherein the imaging file is formed based on an intelligent pen handwriting;
judging whether an individualized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file;
when the personalized semantic database corresponding to the graphical file exists, calling the personalized semantic database to analyze the content in the character set to obtain an analysis result;
and re-determining the content in the character set based on the analysis result.
2. The method of claim 1, wherein obtaining a character set formed based on a graphical file comprises:
judging whether a new graphical file is generated in the cloud service platform;
if yes, after the cloud service platform completes character recognition on the graphical file, a character set obtained by the graphical file recognition is obtained.
3. The method according to claim 1, wherein the determining whether a personalized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on a handwriting matrix and a stored value corresponding to the graphical file comprises:
inputting the handwriting matrix and the storage value into a database in the cloud platform;
and inquiring whether a personalized semantic database corresponding to the handwriting matrix and the storage value exists.
4. The method of claim 1, wherein invoking the personalized semantic database to parse content in the character set comprises:
performing word segmentation processing on the characters in the character set to obtain one or more word segmentation vectors;
performing vector comparison on the word segmentation vector and a semantic vector in the personalized semantic database;
and determining whether the word segmentation vectors in the character set are correct or not based on the result of the vector comparison.
5. The method of claim 4, wherein the re-determining the content of the character set based on the parsing result comprises:
and when the word segmentation vector in the display character set in the analysis result is inconsistent with the semantic vector in the personalized semantic database, correcting the content in the word segmentation vector based on the semantic vector in the personalized semantic database.
6. The method of claim 1, wherein prior to obtaining the character set formed based on the graphical file, the method further comprises:
acquiring an imaging file needing character recognition, wherein the imaging file is generated by handwriting of an intelligent pen;
and searching the handwriting matrix and the storage value for generating the graphical file in the cloud service platform.
7. The method of claim 6, further comprising:
judging whether historical characters corresponding to the handwriting matrix and the stored value exist in a preset character recognition database;
and if so, directly using the historical characters as the characters of the graph to be recognized in the graphical file.
8. A handwriting content analysis apparatus, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a character set formed based on an imaging file, and the imaging file is formed based on an intelligent pen handwriting;
the judging module is used for judging whether an individualized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file;
the analysis module is used for calling the personalized semantic database to analyze the content in the character set when the personalized semantic database corresponding to the graphical file exists, so as to obtain an analysis result;
and the determining module is used for re-determining the content in the character set based on the analysis result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of parsing handwriting content according to any of the preceding claims 1-7.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of the preceding claims 1-7.
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| CN202011268011.XACN112487881B (en) | 2020-11-13 | 2020-11-13 | Handwriting content analysis method and device and electronic equipment |
| PCT/CN2020/138153WO2022099869A1 (en) | 2020-11-13 | 2020-12-21 | Handwriting content analysis method and apparatus, and electronic device |
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| CN202011268011.XACN112487881B (en) | 2020-11-13 | 2020-11-13 | Handwriting content analysis method and device and electronic equipment |
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| CN112487881B (en) | 2024-03-29 |
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