WO2022099872A1 - 智能笔字符识别方法、装置及电子设备 - Google Patents

智能笔字符识别方法、装置及电子设备 Download PDF

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Publication number
WO2022099872A1
WO2022099872A1 PCT/CN2020/138493 CN2020138493W WO2022099872A1 WO 2022099872 A1 WO2022099872 A1 WO 2022099872A1 CN 2020138493 W CN2020138493 W CN 2020138493W WO 2022099872 A1 WO2022099872 A1 WO 2022099872A1
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WIPO (PCT)
Prior art keywords
handwriting
matrix
stored value
character recognition
data
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PCT/CN2020/138493
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English (en)
French (fr)
Inventor
卢启伟
陈方圆
张淮清
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深圳市鹰硕教育服务有限公司
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Publication of WO2022099872A1 publication Critical patent/WO2022099872A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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; CALCULATING OR 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

Definitions

  • the present disclosure relates to the technical field of data processing, and in particular, to a method, device and electronic device for character recognition of a smart pen.
  • the dot matrix digital smart pen is a kind of invisible dot matrix pattern printed on ordinary paper.
  • the high-speed camera at the front of the digital pen captures the movement trajectory of the pen tip at any time.
  • the pressure sensor transmits the pressure data back to the data processor.
  • a new type of writing instrument that transmits information through bluetooth or USB cable.
  • this information includes paper type, source, page number, position, handwriting coordinates, motion trajectory, nib pressure, stroke order, pen running time, pen running speed and other information.
  • the handwriting recording process is synchronized with the writing process.
  • the dot matrix digital pen stores the words or pictures written on the paper in the computer in the form of bitmaps to form a document, which can also be synchronously displayed by projection if necessary.
  • embodiments of the present disclosure provide a smart pen character recognition method, device, and electronic device to at least partially solve the problems existing in the prior art.
  • an embodiment of the present disclosure provides a method for character recognition of a smart pen, including:
  • the historical character is directly used as the character of the graphic to be recognized in the graphic file.
  • the method further includes:
  • the recognized characters and their corresponding handwriting matrices or stored values are stored in the character recognition database.
  • the obtaining of a graphical file requiring character recognition includes:
  • the newly generated graphic file is used as a graphic file that needs to perform character recognition.
  • the searching for a handwriting matrix and a stored value for generating the graphical file in the cloud service platform includes:
  • the handwriting matrix and the stored value of the graphic file are queried.
  • judging whether there are historical characters corresponding to the handwriting matrix and the stored value includes:
  • the directly using the historical characters as the characters of the graphic to be recognized in the graphic file includes:
  • the historical characters are set at the position coordinates of the graphic to be recognized in the graphic file, so as to obtain a character recognition result.
  • the method before the acquisition of the graphical file requiring character recognition, the method further includes:
  • the handwriting data includes the paper surface information when the smart pen writes, the handwriting matrix and the stored value, the handwriting matrix is generated by the smart pen client based on the handwriting of the smart pen, and the stored value is provided by the cloud service.
  • the platform is generated based on the user's historical handwriting data;
  • a graphical file of the handwriting data on the current page is formed in sequence according to the handwriting matrix and the graphic style corresponding to the stored value on the current page.
  • an embodiment of the present disclosure provides a smart pen character recognition device, including:
  • an acquisition module used for acquiring a graphical file that needs to be recognized by character, and the graphical file is generated by the handwriting of the smart pen;
  • a search module used for searching the handwriting matrix and the stored value for generating the graphical file in the cloud service platform
  • a judgment module for judging whether there is a historical character corresponding to the handwriting matrix and the stored value in a preset character recognition database
  • the execution module is configured to directly use the historical characters as the characters of the graphic to be recognized in the graphic file when there are historical characters corresponding to the handwriting matrix and the stored value.
  • an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:
  • the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the intelligence of the foregoing first aspect or any implementation of the first aspect Pen character recognition method.
  • embodiments of the present disclosure further provide a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the foregoing first aspect or the first A smart pen character recognition method in any one of the implementations of an aspect.
  • an embodiment of the present disclosure further provides a computer program product
  • the computer program product includes a computer program stored on a non-transitory computer-readable storage medium
  • the computer program includes program instructions, and when the program instructions are executed by a computer When executed, the computer is made to execute the smart pen character recognition method in the first aspect or any implementation manner of the first aspect.
  • the smart pen character recognition solution in the embodiment of the present disclosure includes acquiring a graphic file that needs to be recognized for character, and the graphic file is generated by the handwriting of the smart pen; searching for a handwriting matrix for generating the graphic file in a cloud service platform and stored value; in the preset character recognition database, determine whether there is a historical character corresponding to the handwriting matrix and the stored value; if so, directly use the historical character as the character of the graphic to be recognized in the graphic file.
  • the efficiency of character recognition of the smart pen is improved.
  • FIG. 1 is a flowchart of a method for character recognition of a smart pen according to an embodiment of the present disclosure
  • FIG. 3 is a flowchart of another smart pen character recognition method provided by an embodiment of the present disclosure.
  • FIG. 5 is a schematic structural diagram of a smart pen character recognition device according to an embodiment of the present disclosure.
  • FIG. 6 is a schematic diagram of an electronic device provided by an embodiment of the present disclosure.
  • Embodiments of the present disclosure provide a method for character recognition of a smart pen.
  • the smart pen character recognition method provided in this embodiment can be executed by a computing device, which can be implemented as software, or a combination of software and hardware, and the computing device can be integrated in a server, a client, or the like.
  • the method for character recognition of a smart pen in an embodiment of the present disclosure may include the following steps:
  • the graphic file is converted from the track written by the smart pen, and is used to display the writing track of the smart pen in a graphic manner, and the graphic file can be various types of graphic files.
  • the handwriting matrix is a feature matrix extracted from the elements that characterize the handwriting features, such as position coordinates, acceleration values, and pressure values contained in the handwriting after the user has finished writing the handwriting. It is used to identify the specific information and features of the user's handwriting. Ability to restore user's handwriting.
  • the stored value is the eigenvalue of the user's historical handwriting matrix that has been stored in the cloud service platform.
  • the characters contained in the graphic file have a one-to-one correspondence with the handwriting matrix or the stored value. Therefore, you can directly search for the corresponding relationship based on this relationship.
  • a handwriting matrix and stored values of the graphic file are generated.
  • the character recognition database saves the characters that have been recognized before, and also saves the one-to-one correspondence between the recognized characters and the handwriting matrix or stored value. Describe the handwriting matrix and the historical characters corresponding to the stored values.
  • the characters of the graphics to be recognized can be directly recognized based on the historical identification records, without performing character recognition on each graphics in each graphic file, thereby greatly improving the efficiency of character recognition.
  • the method further includes:
  • the obtaining a graphical file requiring character recognition includes: searching the cloud service platform for a newly generated graphical file;
  • the newly generated graphic file is used as a graphic file that needs to perform character recognition.
  • the searching for the handwriting matrix and the stored value for generating the graphical file in the cloud service platform includes: in the data collection module of the cloud service platform, querying the The handwriting matrix and stored values of the graphic file.
  • judging whether there are historical characters corresponding to the handwriting matrix and stored values includes:
  • the direct use of the historical characters as the characters of the graphic to be recognized in the graphic file includes:
  • the method before the acquisition of the graphical file requiring character recognition, the method further includes:
  • the handwriting data includes the paper surface information when the smart pen writes, the handwriting matrix and the stored value, the handwriting matrix is generated by the smart pen client based on the handwriting of the smart pen, and the stored value is provided by the cloud service.
  • the platform is generated based on the user's historical handwriting data;
  • a graphical file of the handwriting data on the current page is formed in sequence according to the handwriting matrix and the graphic style corresponding to the stored value on the current page.
  • the handwriting data that needs to be graphed can be obtained, and the handwriting data includes paper surface information, a handwriting matrix and a stored value when the smart pen writes, and the handwriting matrix is generated by the smart pen client based on the handwriting of the smart pen , the stored value is generated by the cloud service platform based on the user's historical handwriting data.
  • the handwriting data of the smart pen can be uploaded to the cloud service platform, and the handwriting data can be processed through the cloud service platform. Convert handwriting data into graphic files, and display the real shape of handwriting through graphic files.
  • the paper surface information, the handwriting matrix and the stored value formed by the smart pen during writing can be obtained from the handwriting data.
  • the paper surface information is used to describe the paper surface on which the smart pen writes handwriting. For example, if the user has written 10 pages of content through the smart pen, at this time, each page of 1-10 can be used to find the content written by the user.
  • the handwriting matrix is a feature matrix extracted from the elements that characterize the handwriting features, such as position coordinates, acceleration values, and pressure values contained in the handwriting after the user has finished writing the handwriting. It is used to identify the specific information and features of the user's handwriting. Ability to restore user's handwriting.
  • the stored value is the eigenvalue of the user's historical handwriting matrix that has been stored in the cloud service platform.
  • the historical handwriting matrix Store the value to replace the newly generated handwriting matrix, thereby saving data processing and reducing system resources.
  • the handwriting matrix and the stored value corresponding to the current paper information can be searched in the graphic module of the cloud service platform.
  • the cloud service platform may include a graphical module. Through the graphical module, the handwriting matrix and the stored value corresponding to the current paper information stored in the database can be queried, so that the user can be restored based on the queried handwriting matrix and the stored value. 's handwriting.
  • the handwriting matrix and the stored value may be sorted based on the generation time corresponding to the handwriting matrix and the stored value to form a graphical sorting result.
  • the handwriting matrix and the handwriting corresponding to the stored value may be sorted in ascending or descending order, so that the handwriting on the current page can be sorted according to the actual generation order or reverse order of the handwriting.
  • a graphical file of the handwriting data on the current page may be formed on the current page in sequence according to the graphic style corresponding to the handwriting matrix and the stored value.
  • the pressure value or position coordinate corresponding to each handwriting matrix or the stored value can be further obtained, and determined by the pressure value
  • the thickness feature of handwriting determines the position coordinates of handwriting on the current page through the position coordinates, and finally forms a graphical handwriting file.
  • the graphic operation of handwriting can be quickly performed, and the efficiency of character recognition of the smart pen is improved.
  • the obtaining the handwriting data that needs to be graphed includes: querying the newly generated handwriting data in the cloud service platform; identifying the newly generated handwriting data as the Handwriting data that needs to be graphed.
  • searching for the handwriting matrix and the stored value corresponding to the current paper information includes: based on the acquired identifier of the smart pen, A query operation is performed in the database of the cloud service platform; based on the query result, a handwriting matrix and a stored value corresponding to the current paper information are obtained.
  • the sorting of the handwriting matrix and the stored values based on the generation time corresponding to the handwriting matrix and the stored value includes: sorting the handwriting matrix Arrange in ascending order with the generation time of the stored value; and determine the arrangement order of the handwriting matrix and the stored value based on the result of the ascending order.
  • the handwriting data is formed on the current page in sequence according to the graphic style corresponding to the handwriting matrix and the stored value based on the graphical sorting result.
  • the graphic file of the current page includes: searching the handwriting matrix or the handwriting position coordinates and pressure values corresponding to the stored values in chronological order; generating the handwriting matrix or the handwriting matrix based on the handwriting position coordinates and the pressure value The graphic handwriting corresponding to the stored value.
  • the method before acquiring the handwriting data that needs to be graphed, the method further includes: dividing the acquired handwriting data based on the pressure value and the acceleration value to form a plurality of handwriting data part.
  • the method further includes: dividing the handwriting data segments into The corresponding time sequence, pressure value sequence, position coordinate sequence and acceleration value sequence are encapsulated to form a handwriting matrix corresponding to the handwriting data segment; the eigenvalues corresponding to the handwriting matrix are sent to the data in the cloud service platform acquisition module, so that the data acquisition module can query whether there is a stored value similar to the feature value in the handwriting data that has been stored in the cloud service platform; When the value is set, the storage matrix corresponding to the stored value is directly called as the characteristic matrix corresponding to the characteristic value.
  • the writing track of the smart pen can be generated by means of a dot matrix.
  • the writing track can include various data of the smart pen, such as the generation time of the handwriting, the pressure value of the pen tip during writing, and the writing process of the writing pen. Position coordinates on paper, acceleration value when writing, etc. By sampling and arranging these data according to time training, time series, pressure value series, position coordinate series and acceleration value series can be formed. Time series, pressure value series, position coordinate series and acceleration value series can be used to describe and restore User's handwriting.
  • the handwriting data may be divided based on the pressure value and the acceleration value to form a plurality of handwriting data segments.
  • the handwriting of the smart pen is directly uploaded to the server for data processing, the data processing speed will be slow due to the large amount of data. Therefore, the handwriting data of the smart pen needs to be processed.
  • the first pressure value threshold and the second acceleration threshold may be set first. Based on the first pressure value threshold, the pressure value sequence is divided to form multiple pressure value sequences. For example, the pressure value sequence part greater than the first pressure value threshold may be divided to form one or more pressure value sequences, One or more handwriting strokes to represent the actual writing of the user.
  • the acceleration value sequence corresponding to each pressure value sequence may be further searched, and based on the second acceleration value threshold, a clipping operation is performed on the acceleration value sequence to form multiple acceleration value sequences.
  • the handwriting data in the paused state of the user can be filtered, thereby further simplifying the segmented handwriting data.
  • the handwriting data is divided based on the time series corresponding to the acceleration value series.
  • the time series, pressure value series, position coordinate series and acceleration value series corresponding to the handwriting data segment may be packaged to form a handwriting matrix corresponding to the handwriting data segment.
  • the time series, the pressure value series, the position coordinate series, and the acceleration value series can be regarded as row vectors or column vectors respectively, thereby forming one or more handwriting matrices corresponding to the handwriting data segments.
  • the eigenvalues corresponding to the handwriting matrix can be sent to the data collection module in the cloud service platform, so that the data collection module can query whether there is a handwriting data stored in the cloud service platform with Stored values with similar eigenvalues; when a stored value similar to the eigenvalue already exists in the cloud service platform, directly call the storage matrix corresponding to the stored value as the eigenvalue corresponding to the eigenvalue, when When there is no stored value similar to the feature value in the cloud service platform, the smart pen client that generates the feature data is notified to upload the handwriting matrix to the data acquisition module.
  • the stored value is the writing eigenvalue formed based on the user's previous writing handwriting. By comparing whether there is similarity between the eigenvalue and the stored value, it can be determined whether to call the storage matrix that has been stored in the cloud service platform, and use the value in the storage matrix to directly It replaces the data in the handwriting matrix, thereby further reducing the amount of data transmission and calculation, and improving the efficiency of handwriting processing.
  • the acquiring the handwriting data of the smart pen includes: monitoring whether pressure data is generated by the smart pen; if there is, collecting the handwriting data generated by the smart pen operate.
  • dividing the handwriting data based on the pressure value and the acceleration value includes: dividing the pressure value sequence based on a first pressure value threshold to form a plurality of Sequence of pressure values. Based on the first pressure value threshold, the pressure value sequence is divided to form multiple pressure value sequences. For example, the pressure value sequence part greater than the first pressure value threshold may be divided to form one or more pressure value sequences, One or more handwriting strokes to represent the actual writing of the user.
  • the acceleration value sequence corresponding to each pressure value sequence is searched; based on the second acceleration value threshold, a clipping operation is performed on the acceleration value sequence to form multiple acceleration value sequences. Based on the second acceleration value threshold, a clipping operation is performed on the acceleration value sequence to form a plurality of acceleration value sequences.
  • the handwriting data in the paused state of the user can be filtered, thereby further simplifying the segmented handwriting data.
  • the handwriting data is divided based on the time series corresponding to the acceleration value series.
  • the encapsulating the time sequence, pressure value sequence, position coordinate sequence, and acceleration value sequence corresponding to the handwriting data segment includes:
  • the handwriting matrix corresponding to the handwriting data segment is formed in time sequence.
  • the method before the eigenvalue corresponding to the handwriting matrix is sent to the data acquisition module in the cloud service platform, the method further includes:
  • the eigenvalues of the divided handwriting data are calculated respectively to form the eigenvalue sequence of the handwriting data.
  • the method further includes:
  • graphic processing is performed on the handwriting data obtained by the data acquisition module to obtain the handwriting image data of the smart pen.
  • the method further includes: performing character recognition on the handwriting image data by using a character recognition module in a cloud service platform to obtain character data corresponding to the handwriting image data; Through the content analysis module in the cloud service platform, a content analysis service is performed on the character data to form writing content data corresponding to the handwriting data.
  • the smart pen can collect the user's writing data in the form of pressure, acceleration value, etc. under the user's use, thereby forming the writing data. These writing data, as the user's handwriting data, are transmitted to the cloud wirelessly or wiredly. Service Platform.
  • the cloud service platform is a platform that communicates with the smart pen terminal through wired or wireless means.
  • Multiple data processing modules can be set up in the cloud service platform. These processing modules can process and analyze the writing data generated by the smart pen, so that users can The recognition and identification of handwriting has become more accurate and efficient.
  • a data collection module is provided in the cloud service platform, and through the data collection module, the handwriting data written by the user can be collected and stored.
  • the data acquisition module can be set to have extremely high flexibility and scalability, and can adjust the resource allocation in time according to the needs of data acquisition to ensure the rapid response of the system and avoid data congestion caused by the rapid expansion of business volume.
  • the data acquisition module is provided with a data storage service unit, which is used to adopt the distributed data storage service in the big data architecture, support the high concurrent data storage service, and provide support for distributed computing.
  • the user's handwriting collected by the data acquisition module is usually stored in the form of time, position coordinates, pressure value, acceleration value, etc. For this reason, the collected handwriting data needs to be imaged and restored to the user's real handwriting.
  • various data such as time, configuration, movement, and pressure of the original handwriting data can be structured and processed.
  • the original handwriting data can be calculated as image and video data, and finally bitmap,
  • Various output formats such as vector graphics and dynamic videos are output, and the handwriting of fixed-line users is displayed in the form of images.
  • the character recognition module set in the cloud service platform can be used to recognize the graphical characters, so as to obtain the character data corresponding to the handwriting image.
  • the character recognition function of handwriting can be set in the character recognition module, and the data written by the user can be quickly converted into standard characters that can be recognized by the computer.
  • the recognition characters of Chinese characters, letters, symbols and formulas can be set.
  • a semantic understanding function based on natural language processing technology can be added to the character recognition module in the process of handwriting recognition, and the probability of character content can be calculated according to the text content of the context to improve the accuracy of character recognition.
  • the content parsing module set on the cloud service platform can be used to perform natural language processing, machine learning, deep learning and other artificial intelligence technologies to parse the content, including entity recognition, relation extraction, and semantics of character content. Services such as comprehension, abstract extraction, keyword extraction, and knowledge graph construction.
  • the user's handwriting can be processed in the cloud, thereby improving the processing efficiency and accuracy of the smart pen's handwriting.
  • the method further includes: based on the content data, performing feature analysis on the writing behavior of the user to form a User-corresponding writing feature font library.
  • the user's writing behavior can be extracted and analyzed, including the writing characteristics of a single character, specific painting and writing characteristics, overall writing habits, writing speed and other writing characteristics, and a unique character feature library for a specific user can be generated to realize user handwriting identification.
  • each character written by the user with the smart pen can identify the writer, and can be used in scenarios such as document signature authenticity verification, exam imitation and cheating, etc.
  • the method further includes: comparing the target feature of the handwriting image data with preset target handwriting data and analysis, and the analysis result of the handwriting image data is determined based on the result of the comparison and analysis.
  • the system can receive the preset writing/painting target characters/graphics, collect the content written by the user, and calculate the similarity between the target and the writing result by using the method of graphic hash value comparison, cosine similarity comparison, mutual information comparison, etc. Judging the similarity between the user's writing content and the target can be applied to scenarios such as calligraphy learning and painting learning.
  • the method further includes:
  • the content data is compared with preset target data to form a content comparison result.
  • the content data can be the answer data written by the user in the process of taking the test
  • the target data is the answer data corresponding to the test content.
  • the similarity value between the content data and the target data can be determined, so as to further determine the correct rate of the handwriting data answered by the user.
  • it can be further determined whether the content written by the user is correct based on the written data of the user.
  • the method further includes: sending the handwriting image data and the content data to the client at the same time, So that the client can display the handwriting image data or the content data.
  • the method further includes: identifying the content data, and judging whether there is a table in the content data content data; if so, display the table content data in a tabular form.
  • the data that needs to be displayed in the form of a table can be identified, and the part of the content can be displayed in the form of a table, thereby improving the processing function of the smart pen data.
  • the method further includes: performing semantic analysis on the content data to determine whether there is a content data corresponding to the content data Recommendation data for the response.
  • the recommendation data can be data corresponding to the content data.
  • the content data is the case data of the user written by the doctor by handwriting, etc., then by analyzing the case data, the prescription data corresponding to the case data can be recommended (recommended data). ), so that it is convenient for doctors to select some recommended data according to actual needs. If it exists, the recommendation data corresponding to the content data is generated. Through this embodiment, the writing efficiency of the writing content data can be further improved.
  • an embodiment of the present application further discloses a smart pen character recognition device 50, including:
  • the obtaining module 501 is used for obtaining a graphical file that needs to perform character recognition, and the graphical file is generated by the handwriting of the smart pen;
  • a search module 502 configured to search the handwriting matrix and the stored value for generating the graphical file in the cloud service platform
  • the judgment module 503 is used for judging whether there is a historical character corresponding to the handwriting matrix and the stored value in a preset character recognition database;
  • the execution module 504 is configured to directly use the historical characters as the characters of the graphic to be recognized in the graphic file when there are historical characters corresponding to the handwriting matrix and the stored value.
  • an embodiment of the present disclosure further provides an electronic device 60, the electronic device includes:
  • the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the smart pen character recognition method in the foregoing method embodiments.
  • Embodiments of the present disclosure also provide a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, make The computer executes the smart pen character recognition method in the foregoing method embodiments.
  • FIG. 6 it shows a schematic structural diagram of an electronic device 60 suitable for implementing an embodiment of the present disclosure.
  • the electronic devices in the embodiments of the present disclosure may include, but are not limited to, such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (eg, mobile terminals such as in-vehicle navigation terminals), etc., and stationary terminals such as digital TVs, desktop computers, and the like.
  • the electronic device shown in FIG. 6 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.
  • electronic device 60 may include processing means (eg, central processing unit, graphics processor, etc.) 601 that may be loaded into random access according to a program stored in read only memory (ROM) 602 or from storage means 608 Various appropriate actions and processes are executed by the programs in the memory (RAM) 603 . In the RAM 603, various programs and data necessary for the operation of the electronic device 60 are also stored.
  • the processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (I/O) interface 605 is also connected to bus 604 .
  • I/O interface 605 input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speakers, An output device 607 of a vibrator or the like; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609 .
  • Communication means 609 may allow electronic device 60 to communicate wirelessly or by wire with other devices to exchange data. While the figures show the electronic device 60 having various means, it should be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
  • embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart.
  • the computer program may be downloaded and installed from the network via the communication device 609 , or from the storage device 608 , or from the ROM 602 .
  • the processing apparatus 601 the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed.
  • the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two.
  • the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .
  • Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, electrical wire, optical fiber cable, RF (radio frequency), etc., or any suitable combination of the foregoing.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or may exist alone without being assembled into the electronic device.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: acquires at least two Internet Protocol addresses; A node evaluation request for an Internet Protocol address, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained The Internet Protocol address indicates an edge node in the content distribution network.
  • the above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; From the at least two Internet Protocol addresses, the Internet Protocol address is selected; the selected Internet Protocol address is returned; wherein, the received Internet Protocol address indicates an edge node in the content distribution network.
  • Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional Procedural programming language - such as the "C" language or similar programming language.
  • 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.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through Internet connection).
  • LAN local area network
  • WAN wide area network
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions.
  • the functions noted in the blocks 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.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments of the present disclosure may be implemented in a software manner, and may also be implemented in a hardware manner.
  • the name of the unit does not constitute a limitation of the unit itself under certain circumstances, for example, the first obtaining unit may also be described as "a unit that obtains at least two Internet Protocol addresses".
  • the smart pen character recognition solution in the embodiment of the present disclosure includes acquiring a graphic file that needs to be recognized for character, and the graphic file is generated by the handwriting of the smart pen; searching for a handwriting matrix for generating the graphic file in a cloud service platform and stored value; in the preset character recognition database, determine whether there is a historical character corresponding to the handwriting matrix and the stored value; if so, directly use the historical character as the character of the graphic to be recognized in the graphic file.
  • the efficiency of character recognition of the smart pen is improved.

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Abstract

本公开实施例中提供了一种智能笔字符识别方法、装置及电子设备，属于数据处理技术领域，该方法包括：获取需要进行字符识别的图形化文件，所述图形化文件由智能笔的笔迹生成；在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；若是，则直接使用所述历史字符作为图形化文件中待识别图形的字符。通过本公开的处理方案，能够提高智能笔字符的识别效率。

Description

智能笔字符识别方法、装置及电子设备 技术领域
本公开涉及数据处理技术领域，尤其涉及一种智能笔字符识别方法、装置及电子设备。
背景技术
点阵数码智能笔是一种通过在普通纸张上印刷一层不可见的点阵图案，数码笔前端的高速摄像头随时捕捉笔尖的运动轨迹，同时压力传感器将压力数据传回数据处理器，最终将信息通过蓝牙或者USB线向外传输的新型书写工具。
与普通的纸张和笔不同，这些信息包括纸张类型、来源、页码、位置、笔迹坐标、运动轨迹、笔尖压力、笔画顺序、运笔时间、运笔速度等信息，笔迹记录过程与书写过程同步。当书写时，点阵数码笔将纸张上书写的文字或者图片以位图的形式存储在电脑中，形成文档，如需要还可以同步通过投影显示。
如何基于云平台对智能笔的字符进行识别处理，提高智能笔字符识别的处理效率，成为需要解决的问题。
发明内容
有鉴于此，本公开实施例提供一种智能笔字符识别方法、装置及电子设备，以至少部分解决现有技术中存在的问题。
第一方面，本公开实施例提供了一种智能笔字符识别方法，包括：
获取需要进行字符识别的图形化文件，所述图形化文件由智能笔的笔迹生成；
在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；
在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；
若是，则直接使用所述历史字符作为图形化文件中待识别图形的字符。
根据本公开实施例的一种具体实现方式，所述在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符之后，所述方法还包括：
当预设的字符识别数据库中不存在与所述笔迹矩阵及存储值对应的历史字符时，直接对所述图形化文件上的字符进行识别；
将识别到的字符及其对应的笔迹矩阵或存储值保存到所述字符识别数据库中。
根据本公开实施例的一种具体实现方式，所述获取需要进行字符识别的图形化文件，包括：
在所述云端服务平台中查找新生成的图形化文件；
将所述新生成的图形化文件作为需要进行字符识别的图形化文件。
根据本公开实施例的一种具体实现方式，所述在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值，包括：
在所述云端服务平台的数据采集模块中，查询所述图形化文件的笔迹矩阵及存储值。
根据本公开实施例的一种具体实现方式，所述在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符，包括：
将所述笔迹矩阵和所述存储值输入到所述字符识别数据库中执行查询操作；
基于查询操作的结果，判断是否存在与所述笔迹矩阵及存储值对应的历史字符。
根据本公开实施例的一种具体实现方式，所述直接使用所述历史字符作为图形化文件中待识别图形的字符，包括：
获取所述待识别图形在图形化文件中的位置坐标；
将所述历史字符设置在待识别图形在图形化文件中的位置坐标处，以得 到字符识别结果。
根据本公开实施例的一种具体实现方式，所述获取需要进行字符识别的图形化文件之前，所述方法还包括：
获取需要图形化的笔迹数据，所述笔迹数据包括智能笔书写时的纸面信息、笔迹矩阵以及存储值，所述笔迹矩阵由智能笔客户端基于智能笔笔迹产生，所述存储值由云端服务平台基于用户的历史笔迹数据生成；
在云端服务平台的图形化模块中，查找与当前纸面信息所对应的笔迹矩阵及存储值；
基于所述笔迹矩阵与所述存储值所对应的生成时间，对所述笔迹矩阵及所述存储值进行排序，形成图形化排序结果；
基于所述图形化排序结果，在当前页面上依序按照所述笔迹矩阵与所述存储值所对应的图形样式，形成所述笔迹数据在当前页面的图形化文件。
第二方面，本公开实施例提供了一种智能笔字符识别装置，包括：
获取模块，用于获取需要进行字符识别的图形化文件，所述图形化文件由智能笔的笔迹生成；
查找模块，用于在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；
判断模块，用于在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；
执行模块，用于存在与所述笔迹矩阵及存储值对应的历史字符时，则直接使用所述历史字符作为图形化文件中待识别图形的字符。
第三方面，本公开实施例还提供了一种电子设备，该电子设备包括：
至少一个处理器；以及，
与该至少一个处理器通信连接的存储器；其中，
该存储器存储有可被该至少一个处理器执行的指令，该指令被该至少一个处理器执行，以使该至少一个处理器能够执行前述第一方面或第一方面的 任一实现方式中的智能笔字符识别方法。
第四方面，本公开实施例还提供了一种非暂态计算机可读存储介质，该非暂态计算机可读存储介质存储计算机指令，该计算机指令用于使该计算机执行前述第一方面或第一方面的任一实现方式中的智能笔字符识别方法。
第五方面，本公开实施例还提供了一种计算机程序产品，该计算机程序产品包括存储在非暂态计算机可读存储介质上的计算程序，该计算机程序包括程序指令，当该程序指令被计算机执行时，使该计算机执行前述第一方面或第一方面的任一实现方式中的智能笔字符识别方法。
本公开实施例中的智能笔字符识别方案，包括获取需要进行字符识别的图形化文件，所述图形化文件由智能笔的笔迹生成；在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；若是，则直接使用所述历史字符作为图形化文件中待识别图形的字符。通过本公开的处理方案，提高了智能笔字符识别的效率。
附图说明
为了更清楚地说明本公开实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为本公开实施例提供的一种智能笔字符识别方法的流程图；
图2为本公开实施例提供的另一种智能笔字符识别方法的流程图；
图3为本公开实施例提供的另一种智能笔字符识别方法的流程图；
图4为本公开实施例提供的另一种智能笔字符识别方法的流程图；
图5为本公开实施例提供的一种智能笔字符识别装置的结构示意图；
图6为本公开实施例提供的电子设备示意图。
具体实施方式
下面结合附图对本公开实施例进行详细描述。
以下通过特定的具体实例说明本公开的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本公开的其他优点与功效。显然，所描述的实施例仅仅是本公开一部分实施例，而不是全部的实施例。本公开还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本公开的精神下进行各种修饰或改变。需说明的是，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。基于本公开中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本公开保护的范围。
需要说明的是，下文描述在所附权利要求书的范围内的实施例的各种方面。应显而易见，本文中所描述的方面可体现于广泛多种形式中，且本文中所描述的任何特定结构及/或功能仅为说明性的。基于本公开，所属领域的技术人员应了解，本文中所描述的一个方面可与任何其它方面独立地实施，且可以各种方式组合这些方面中的两者或两者以上。举例来说，可使用本文中所阐述的任何数目个方面来实施设备及/或实践方法。另外，可使用除了本文中所阐述的方面中的一或多者之外的其它结构及/或功能性实施此设备及/或实践此方法。
还需要说明的是，以下实施例中所提供的图示仅以示意方式说明本公开的基本构想，图式中仅显示与本公开中有关的组件而非按照实际实施时的组件数目、形状及尺寸绘制，其实际实施时各组件的型态、数量及比例可为一种随意的改变，且其组件布局型态也可能更为复杂。
另外，在以下描述中，提供具体细节是为了便于透彻理解实例。然而，所属领域的技术人员将理解，可在没有这些特定细节的情况下实践所述方面。
本公开实施例提供一种智能笔字符识别方法。本实施例提供的智能笔字符识别方法可以由一计算装置来执行，该计算装置可以实现为软件，或者实现为软件和硬件的组合，该计算装置可以集成设置在服务器、客户端等中。
参见图1，本公开实施例中的智能笔字符识别方法，可以包括如下步骤：
S101，获取需要进行字符识别的图形化文件，所述图形化文件由智能笔 的笔迹生成。
图形化文件通过智能笔书写的轨迹转化而成，用以通过图形的方式来展示智能笔的书写轨迹，该图形化文件可以是各种类型的图形文件。
在进行字符识别之前，可以在云端服务平台中直接查找新生成的图形化文件，通过获取需要实时进行字符识别的图形化文件。
S102，在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值。
笔迹矩阵是用户在书写完笔迹之后，对笔迹中包含的位置坐标、加速度值、压力值等表征笔迹特征的元素提炼出来的特征矩阵，用来标识用户笔迹的具体的信息及特征，通过笔迹矩阵能够还原用户的笔迹。
存储值是云端服务平台中已经存储的用户的历史笔迹矩阵的特征值，图形化文件中包含的字符与笔迹矩阵或存储值具有一一对应的关系，为此，可以直接基于该对应关系来查找生成所述图形化文件的笔迹矩阵及存储值。
S103，在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符。
字符识别数据库中保存有之前已经识别过的字符，同时保存已经识别的字符与笔迹矩阵或存储值之间的一一对应关系，为此，可以直接在字符识别数据库中，直接查询是否存在与所述笔迹矩阵及存储值对应的历史字符。
S104，若是，则直接使用所述历史字符作为图形化文件中待识别图形的字符。
通过这种方式，能够基于历史识别记录来直接对待识别图形的字符进行识别，不用对每个图形化文件中的每个图形都进行字符识别，从而极大的提高了字符识别的效率。
参见图2，根据本公开实施例的一种具体实现方式，所述在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符之后，所述方法还包括：
S201，当预设的字符识别数据库中不存在与所述笔迹矩阵及存储值对应的历史字符时，直接对所述图形化文件上的字符进行识别；
S202，将识别到的字符及其对应的笔迹矩阵或存储值保存到所述字符识别数据库中。
根据本公开实施例的一种具体实现方式，所述获取需要进行字符识别的图形化文件，包括：在所述云端服务平台中查找新生成的图形化文件；
将所述新生成的图形化文件作为需要进行字符识别的图形化文件。
根据本公开实施例的一种具体实现方式，所述在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值，包括：在所述云端服务平台的数据采集模块中，查询所述图形化文件的笔迹矩阵及存储值。
参见图3，根据本公开实施例的一种具体实现方式，所述在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符，包括：
S301，将所述笔迹矩阵和所述存储值输入到所述字符识别数据库中执行查询操作；
S302，基于查询操作的结果，判断是否存在与所述笔迹矩阵及存储值对应的历史字符。
参见图4，根据本公开实施例的一种具体实现方式，所述直接使用所述历史字符作为图形化文件中待识别图形的字符，包括：
S401，获取所述待识别图形在图形化文件中的位置坐标；
S402，将所述历史字符设置在待识别图形在图形化文件中的位置坐标处，以得到字符识别结果。
根据本公开实施例的一种具体实现方式，所述获取需要进行字符识别的图形化文件之前，所述方法还包括：
获取需要图形化的笔迹数据，所述笔迹数据包括智能笔书写时的纸面信息、笔迹矩阵以及存储值，所述笔迹矩阵由智能笔客户端基于智能笔笔迹产生，所述存储值由云端服务平台基于用户的历史笔迹数据生成；
在云端服务平台的图形化模块中，查找与当前纸面信息所对应的笔迹矩 阵及存储值；
基于所述笔迹矩阵与所述存储值所对应的生成时间，对所述笔迹矩阵及所述存储值进行排序，形成图形化排序结果；
基于所述图形化排序结果，在当前页面上依序按照所述笔迹矩阵与所述存储值所对应的图形样式，形成所述笔迹数据在当前页面的图形化文件。
作为一种可选方式，可以获取需要图形化的笔迹数据，所述笔迹数据包括智能笔书写时的纸面信息、笔迹矩阵以及存储值，所述笔迹矩阵由智能笔客户端基于智能笔笔迹产生，所述存储值由云端服务平台基于用户的历史笔迹数据生成。
笔迹数据在智能笔端生成之后，为了提高智能笔笔迹的识别效率，可以将智能笔的笔迹数据上传到云端服务平台，通过云端服务平台对笔迹数据进行处理，作为笔迹数据的一种方式，便是将笔迹数据转换为图形文件，通过图形文件来展示笔迹的真实的形状。
为此可以在笔迹数据中获取智能笔在书写时形成的纸面信息、笔迹矩阵以及存储值。
纸面信息用于描述智能笔在哪个纸面上进行了笔迹书写，例如，用户通过智能笔书写了10页的内容，此时可以通过1-10每个页面来查找用户书写的内容。
笔迹矩阵是用户在书写完笔迹之后，对笔迹中包含的位置坐标、加速度值、压力值等表征笔迹特征的元素提炼出来的特征矩阵，用来标识用户笔迹的具体的信息及特征，通过笔迹矩阵能够还原用户的笔迹。
存储值是云端服务平台中已经存储的用户的历史笔迹矩阵的特征值，当智能笔书写时生成的笔迹矩阵已经存在云端服务平台中存储的历史笔迹矩阵中时，此时便将历史笔迹矩阵的存储值来替代新生成的笔迹矩阵，从而节省数据的处理过程，降低***资源。
作为一种可选方式，可以在云端服务平台的图形化模块中，查找与当前纸面信息所对应的笔迹矩阵及存储值。
云端服务平台中可以包括图形化模块，通过该图形化模块，能够查询数据库中存储的当前纸面信息所对应的笔迹矩阵及存储值，从而能够基于查询到的笔迹矩阵和存储值来还原用户之前的笔迹。
作为一种可选方式，可以基于所述笔迹矩阵与所述存储值所对应的生成时间，对所述笔迹矩阵及所述存储值进行排序，形成图形化排序结果。
具体的，可以采用升序或降序的方式，对所述笔迹矩阵及所述存储值所对应的笔迹进行排序，从而能够按照笔迹实际的生成顺序或倒序的方式，对当前页面的笔迹进行排序。
作为一种可选方式，可以基于所述图形化排序结果，在当前页面上依序按照所述笔迹矩阵与所述存储值所对应的图形样式，形成所述笔迹数据在当前页面的图形化文件。
在当前页面上依序按照所述笔迹矩阵与所述存储值所对应的图形样式进行排序之后，可以进一步获取每个笔迹矩阵或所述存储值所对应的压力值或位置坐标，通过压力值确定笔迹的粗细特征，通过位置坐标确定笔迹在当前页面的位置坐标，最终形成图形化的笔迹文件。
通过上述实施例的内容，能够快速的对笔迹执行图形化操作，提高了智能笔字符识别的效率。
根据本公开实施例的一种具体实现方式，所述获取需要图形化的笔迹数据，包括：在所述云端服务平台中查询新生成的笔迹数据；将所述新生成的笔迹数据认定为所述需要进行图形化的笔迹数据。
根据本公开实施例的一种具体实现方式，所述在云端服务平台的图形化模块中，查找与当前纸面信息所对应的笔迹矩阵及存储值，包括：基于获取到的智能笔的标识，在所述云端服务平台的数据库中执行查询操作；基于查询的结果，得到与当前纸面信息所对应的笔迹矩阵及存储值。
根据本公开实施例的一种具体实现方式，所述基于所述笔迹矩阵与所述 存储值所对应的生成时间，对所述笔迹矩阵及所述存储值进行排序，包括：对所述笔迹矩阵与所述存储值的生成时间进行升序排列；基于升序排列的结果，确定所述笔迹矩阵及所述存储值的排列顺序。
根据本公开实施例的一种具体实现方式，所述基于所述图形化排序结果，在当前页面上依序按照所述笔迹矩阵与所述存储值所对应的图形样式，形成所述笔迹数据在当前页面的图形化文件，包括：按照时间顺序查找所述笔迹矩阵或所述存储值对应的笔迹位置坐标及压力值；基于所述笔迹位置坐标和所述压力值，生成所述笔迹矩阵或所述存储值对应的图形化笔迹。
根据本公开实施例的一种具体实现方式，所述获取需要图形化的笔迹数据之前，所述方法还包括：基于压力值和加速度值，对获取到的笔迹数据进行划分，形成多个笔迹数据段。
根据本公开实施例的一种具体实现方式，所述基于压力值和加速度值，对获取到的笔迹数据进行划分，形成多个笔迹数据段之后，所述方法还包括：将所述笔迹数据段所对应的时间序列、压力值序列、位置坐标序列及加速度值序列进行封装，形成与所述笔迹数据段对应的笔迹矩阵；将所述笔迹矩阵所对应的特征值发送给云服务平台中的数据采集模块，以便于所述数据采集模块查询云服务平台中已经存储的笔迹数据中是否存在与所述特征值相似的存储值；当所述云服务平台中已经存在与所述特征值相似的存储值时，直接调用所述存储值对应的存储矩阵作为所述特征值对应的特征矩阵。
智能笔在书写的过程中，通过点阵的方式能够生成智能笔的书写轨迹，书写轨迹可以包括智能笔的多种数据，比如，笔迹的生成时间，书写时笔尖的压力值、书写笔在书写纸上的位置坐标、书写时的加速度值等。通过将这些数据按照时间的训练进行采样排列，便可以形成时间序列、压力值序列、位置坐标序列及加速度值序列，时间序列、压力值序列、位置坐标序列及加速度值序列可以用来描述和还原用户的书写笔迹。
作为一种可选方式，可以基于压力值和加速度值，对所述笔迹数据进行划分，形成多个笔迹数据段。
智能笔的书写笔迹如果直接上传到服务器端进行数据处理，会由于数据 量过大，导致数据的处理速度较慢，为此，需要对智能笔的笔迹数据进行处理。
作为一种方式，可以首先设置第一压力值阈值和第二加速度阈值。基于第一压力值阈值，对所述压力值序列进行划分，形成多个压力值序列，例如，可以将大于第一压力值阈值的压力值序列部分划分出来，形成一个或多个压力值序列，用以表示用户真实书写的一个或多个笔迹笔画。
在确定完压力值序列之后，还可以进一步的查找每个压力值序列所对应的加速度值序列，基于第二加速度值阈值，对所述加速度值序列进行裁剪操作，形成多个加速度值序列。通过第二加速度阈值，可以过滤到用户处于停顿状态的笔迹数据，从而进一步的简化分段后的笔迹数据。最后，基于所述加速度值序列对应的时间序列，对所述笔迹数据进行划分。
作为一种可选方式，可以将所述笔迹数据段所对应的时间序列、压力值序列、位置坐标序列及加速度值序列进行封装，形成与所述笔迹数据段对应的笔迹矩阵。
可以将时间序列、压力值序列、位置坐标序列及加速度值序分别作为行向量或者列向量，进而形成一个或多个与笔迹数据段对应的笔迹矩阵。
作为一种可选方式，可以将所述笔迹矩阵所对应的特征值发送给云服务平台中的数据采集模块，以便于所述数据采集模块查询云服务平台中已经存储的笔迹数据中是否存在与所述特征值相似的存储值；当所述云服务平台中已经存在与所述特征值相似的存储值时，直接调用所述存储值对应的存储矩阵作为所述特征值对应的特征矩阵，当所述云服务平台中不存在与所述特征值相似的存储值时，则通知生成所述特征数据的智能笔客户端上传所述笔迹矩阵至所述数据采集模块。
存储值是基于用户之前的书写笔迹形成的书写特征值，通过比较特征值与存储值之间是否存在相似，可以决定是否调用云服务平台中已经存储的存储矩阵，用存储矩阵中的数值来直接代替笔迹矩阵中的数据，从而进一步的减少了数据的传输和计算量，提高了笔迹处理的效率。
通过上传特征值的方式，可以进一步的减少数据的计算量，简化数据的 计算过程。
根据本公开实施例的一种具体实现方式，所述获取智能笔的笔迹数据，包括：监控所述智能笔的是否存在压力数据产生；若存在，则对所述智能笔产生的笔迹数据进行采集操作。
根据本公开实施例的一种具体实现方式，所述于压力值和加速度值，对所述笔迹数据进行划分，包括：基于第一压力值阈值，对所述压力值序列进行划分，形成多个压力值序列。基于第一压力值阈值，对所述压力值序列进行划分，形成多个压力值序列，例如，可以将大于第一压力值阈值的压力值序列部分划分出来，形成一个或多个压力值序列，用以表示用户真实书写的一个或多个笔迹笔画。
查找每个压力值序列所对应的加速度值序列；基于第二加速度值阈值，对所述加速度值序列进行裁剪操作，形成多个加速度值序列。基于第二加速度值阈值，对所述加速度值序列进行裁剪操作，形成多个加速度值序列。通过第二加速度阈值，可以过滤到用户处于停顿状态的笔迹数据，从而进一步的简化分段后的笔迹数据。基于所述加速度值序列对应的时间序列，对所述笔迹数据进行划分。通过上述实施方式，能够通过设置阈值的方式，进一步的减少数据的计算量。
根据本公开实施例的一种具体实现方式，所述将所述笔迹数据段所对应的时间序列、压力值序列、位置坐标序列及加速度值序列进行封装，包括：
将时间序列、压力值序列、位置坐标序列及加速度值序列作为矩阵的行向量，以时间顺序对形成所述笔迹数据段对应的笔迹矩阵。
根据本公开实施例的一种具体实现方式，所述将所述笔迹矩阵所对应的特征值发送给云服务平台中的数据采集模块之前，所述方法还包括：
分别计算划分后的笔迹数据的特征值，形成所述笔迹数据的特征值序列。
根据本公开实施例的一种具体实现方式，所述方法还包括：
利用所述云服务平台中的图形化模块，对所述数据采集模块获得的笔迹数据进行图形化处理，得到智能笔的笔迹图像数据。
根据本公开实施例的一种具体实现方式，所述方法还包括：针对所述笔迹图像数据，利用云服务平台中的字符识别模块进行字符识别，得到与所述笔迹图像数据对应的字符数据；通过云服务平台中的内容解析模块，对所述字符数据进行内容解析服务，形成与所述笔迹数据对应的书写内容数据。
智能笔作为一个电子设备终端，可以在用户的使用下以压力、加速度值等方式采集用户的书写数据，从而形成书写数据，这些书写数据作为用户的笔迹数据，通过无线或有线的方式传递给云服务平台。
云服务平台是通过有线或无线方式与智能笔终端通信连接的平台，在云服务平台中可以设置多个数据处理模块，通过这些处理模块对智能笔产生的书写数据进行处理和分析，从而使得用户书写笔迹的识别和鉴定变得更加的准确和高效。
作为一种方式，在云服务平台中设置有数据采集模块，通过该数据采集模块，能够对用户书写的笔迹数据进行采集和存储。
数据采集模块可以设置为具有极高的灵活性和可扩展性，可依据数据采集需要及时调整资源配置，保证***快速响应，避免因业务量快速扩张引起的数据阻塞。
数据采集模块设置有数据存储服务单元，用于采用大数据架构中的分布式数据存储服务，支持高并发的数据存储服务，且对分布式计算提供支持。
数据采集模块采集到的用户的书写笔迹通常以时间、位置坐标、压力值、加速度值等方式进行存储，为此，需要对采集到的笔迹数据进行图像化处理，还原成用户真实的书写笔迹。
为此，可以将采集到原笔迹数据的时间、配置、移动以及压力等各种数据进行结构化处理，通过图形化计算模块，可将原笔迹数据计算为图像和视频数据，最后以位图、矢量图和动态视频等多种输出格式进行输出，以图像的形式来固话用户的书写笔迹。
得到书写对应的笔迹图像数据之后，可以利用云服务平台中设置的字符识别模块对图形化的字符进行识别，从而得到笔迹图像对应的字符数据。
可以在字符识别模块中设置手写笔迹的字符识别功能，将用户书写的数据快速转化为电脑能识别的标准字符，例如，可以设置汉字、字母、符号和公式等内容的识别字符。
作为一种可选方式，可以在笔迹识别的过程中采用基于字符识别模块中加入了基于自然语言处理技术的语义理解功能，可根据上下文的文本内容计算字符内容的概率，提高字符识别的准确度。
对用户书写笔迹识别为标准字符后，可以利用云服务平台设置的内容解析模块执行自然语言处理、机器学习、深度学习等人工智能技术对内容进行解析，包括字符内容的实体识别、关系抽取、语义理解、摘要提取、关键词提取以及知识图谱构建等服务。
通过对内容进行解析，可以综合用户书写笔迹的全部上下文内容对用户的书写内容进行总体判断和分析，进一步提高了书写内容数据的准确性。
通过上述实施例的内容和方案，能够在云端对用户的书写笔迹进行处理，从而提高了智能笔书写笔迹的处理效率和准确度。
根据本公开实施例的一种具体实现方式，所述形成与所述笔迹数据对应的书写内容数据之后，所述方法还包括：基于所述内容数据，对用户的书写行为进行特征分析，形成与用户对应的书写特征字体库。例如，可以对用户的书写行为包括单个字符的书写特性、特定等画书写特征、整体书写习惯、书写速度等书写特征进行提取和分析，可生成特定用户的独有字符特征库，实现用户笔迹鉴定，用户使用智能笔书写的每一个字符都可识别书写人，可应用文件签名真伪认定、考试仿作弊等场景。
根据本公开实施例的一种具体实现方式，所述形成与所述笔迹数据对应的书写内容数据之后，所述方法还包括：将所述笔迹图像数据与预设的目标笔迹数据进行目标特征对比和分析，并基于对比和分析的结果确定所述笔迹图像数据的分析结果。例如，可以***接收预置书写/绘画的目标字符/图形，采集用户书写的内容，利用图形哈希值对比、余弦相似度对比、互信息对比等方法计算目标与书写结果的相似性，用于判断用户书写内容与目标的相似性，可应用于书法学习、绘画学习等场景。
根据本公开实施例的一种具体实现方式，所述形成与所述笔迹数据对应的书写内容数据之后，所述方法还包括：
首先，将所述内容数据与预设的目标数据进行比对，形成内容比对结果。
作为一种应用场景，内容数据可以是用户进行考试等过程中书写的解答数据，而目标数据则是考试内容对应的答案数据，通过将内容数据和目标数据进行比对，可以形成比对结果。
其次，基于所述内容比对结果，确定所述内容数据与目标数据之间的相似度值。
通过上述步骤形成的比对结果，能够确定内容数据与目标数据之间的相似度值，从而进一步的确定用户解答的笔迹数据的正确率。通过该实施例的内容，能够进一步的基于用户的书写数据判断用户书写的内容是否正确。
根据本公开实施例的一种具体实现方式，所述形成与所述笔迹数据对应的书写内容数据之后，所述方法还包括：将所述笔迹图像数据和所述内容数据同时发送给客户端，以便于所述客户端显示所述笔迹图像数据或所述内容数据。
根据本公开实施例的一种具体实现方式，所述形成与所述笔迹数据对应的书写内容数据之后，所述方法还包括：对所述内容数据进行识别，判断所述内容数据中是否存在表格内容数据；若是，则以表格形式显示所述表格内容数据。
通过这种方式，能够将需要通过表格方式显示的数据识别出来，并通过表格的方式对该部分内容进行显示，从而提高了智能笔数据的处理功能。
根据本公开实施例的一种具体实现方式，所述形成与所述笔迹数据对应的书写内容数据之后，所述方法还包括：对所述内容数据进行语义分析，判断是否存在与所述内容数据响应的推荐数据。推荐数据可以是与内容数据对应的数据，作为一个例子，内容数据是医生通过手写等方式书写的用户的病例数据，则通过分析该病例数据，可以推荐与该病例数据对应的处方数据(推荐数据)，从而方便医生根据实际的需要选择部分推荐数据。若存在，则生成 与所述内容数据所对应的推荐数据。通过该实施方式，能够进一步的提高书写内容数据的书写效率。
与上面的实施例相对应，参见图5，本申请实施例还公开了一种智能笔字符识别装置50，包括：
获取模块501，用于获取需要进行字符识别的图形化文件，所述图形化文件由智能笔的笔迹生成；
查找模块502，用于在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；
判断模块503，用于在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；
执行模块504，用于存在与所述笔迹矩阵及存储值对应的历史字符时，则直接使用所述历史字符作为图形化文件中待识别图形的字符。
本实施例未详细描述的部分，参照上述方法实施例中记载的内容，在此不再赘述。
参见图6，本公开实施例还提供了一种电子设备60，该电子设备包括：
至少一个处理器；以及，
与该至少一个处理器通信连接的存储器；其中，
该存储器存储有可被该至少一个处理器执行的指令，该指令被该至少一个处理器执行，以使该至少一个处理器能够执行前述方法实施例中的智能笔字符识别方法。
本公开实施例还提供了一种计算机程序产品，该计算机程序产品包括存储在非暂态计算机可读存储介质上的计算程序，该计算机程序包括程序指令，当该程序指令被计算机执行时，使该计算机执行前述方法实施例中的的智能笔字符识别方法。
下面参考图6，其示出了适于用来实现本公开实施例的电子设备60的结构示意图。本公开实施例中的电子设备可以包括但不限于诸如移动电话、笔 记本电脑、数字广播接收器、PDA(个人数字助理)、PAD(平板电脑)、PMP(便携式多媒体播放器)、车载终端(例如车载导航终端)等等的移动终端以及诸如数字TV、台式计算机等等的固定终端。图6示出的电子设备仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图6所示，电子设备60可以包括处理装置(例如中央处理器、图形处理器等)601，其可以根据存储在只读存储器(ROM)602中的程序或者从存储装置608加载到随机访问存储器(RAM)603中的程序而执行各种适当的动作和处理。在RAM 603中，还存储有电子设备60操作所需的各种程序和数据。处理装置601、ROM 602以及RAM 603通过总线604彼此相连。输入/输出(I/O)接口605也连接至总线604。
通常，以下装置可以连接至I/O接口605：包括例如触摸屏、触摸板、键盘、鼠标、图像传感器、麦克风、加速度计、陀螺仪等的输入装置606；包括例如液晶显示器(LCD)、扬声器、振动器等的输出装置607；包括例如磁带、硬盘等的存储装置608；以及通信装置609。通信装置609可以允许电子设备60与其他设备进行无线或有线通信以交换数据。虽然图中示出了具有各种装置的电子设备60，但是应理解的是，并不要求实施或具备所有示出的装置。可以替代地实施或具备更多或更少的装置。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置609从网络上被下载和安装，或者从存储装置608被安装，或者从ROM602被安装。在该计算机程序被处理装置601执行时，执行本公开实施例的方法中限定的上述功能。
需要说明的是，本公开上述的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的***、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、 硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行***、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读信号介质可以发送、传播或者传输用于由指令执行***、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、RF(射频)等等，或者上述的任意合适的组合。
上述计算机可读介质可以是上述电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。
上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被该电子设备执行时，使得该电子设备：获取至少两个网际协议地址；向节点评价设备发送包括所述至少两个网际协议地址的节点评价请求，其中，所述节点评价设备从所述至少两个网际协议地址中，选取网际协议地址并返回；接收所述节点评价设备返回的网际协议地址；其中，所获取的网际协议地址指示内容分发网络中的边缘节点。
或者，上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被该电子设备执行时，使得该电子设备：接收包括至少两个网际协议地址的节点评价请求；从所述至少两个网际协议地址中，选取网际协议地址；返回选取出的网际协议地址；其中，接收到的网际协议地址指示内容分发网络中的边缘节点。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开的操作的计算机程序代码，上述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或 类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本公开各种实施例的***、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的***来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，单元的名称在某种情况下并不构成对该单元本身的限定，例如，第一获取单元还可以被描述为“获取至少两个网际协议地址的单元”。
应当理解，本公开的各部分可以用硬件、软件、固件或它们的组合来实现。
以上所述，仅为本公开的具体实施方式，但本公开的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本公开揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本公开的保护范围之内。因此，本公开的保护范围应以权利要求的保护范围为准。
工业实用性
本公开实施例中的智能笔字符识别方案，包括获取需要进行字符识别的 图形化文件，所述图形化文件由智能笔的笔迹生成；在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；若是，则直接使用所述历史字符作为图形化文件中待识别图形的字符。通过本公开的处理方案，提高了智能笔字符识别的效率。

Claims (10)

  1. 一种智能笔字符识别方法，其特征在于，包括：
    获取需要进行字符识别的图形化文件，所述图形化文件由智能笔的笔迹生成；
    在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；
    在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；
    若是，则直接使用所述历史字符作为图形化文件中待识别图形的字符。
  2. 根据权利要求1所述的方法，其特征在于，所述在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符之后，所述方法还包括：
    当预设的字符识别数据库中不存在与所述笔迹矩阵及存储值对应的历史字符时，直接对所述图形化文件上的字符进行识别；
    将识别到的字符及其对应的笔迹矩阵或存储值保存到所述字符识别数据库中。
  3. 根据权利要求1所述的方法，其特征在于，所述获取需要进行字符识别的图形化文件，包括：
    在所述云端服务平台中查找新生成的图形化文件；
    将所述新生成的图形化文件作为需要进行字符识别的图形化文件。
  4. 根据权利要求1所述的方法，其特征在于，所述在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值，包括：
    在所述云端服务平台的数据采集模块中，查询所述图形化文件的笔迹矩阵及存储值。
  5. 根据权利要求1所述的方法，其特征在于，所述在预设的字符识别数 据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符，包括：
    将所述笔迹矩阵和所述存储值输入到所述字符识别数据库中执行查询操作；
    基于查询操作的结果，判断是否存在与所述笔迹矩阵及存储值对应的历史字符。
  6. 根据权利要求1所述的方法，其特征在于，所述直接使用所述历史字符作为图形化文件中待识别图形的字符，包括：
    获取所述待识别图形在图形化文件中的位置坐标；
    将所述历史字符设置在待识别图形在图形化文件中的位置坐标处，以得到字符识别结果。
  7. 根据权利要求1所述的方法，其特征在于，所述获取需要进行字符识别的图形化文件之前，所述方法还包括：
    获取需要图形化的笔迹数据，所述笔迹数据包括智能笔书写时的纸面信息、笔迹矩阵以及存储值，所述笔迹矩阵由智能笔客户端基于智能笔笔迹产生，所述存储值由云端服务平台基于用户的历史笔迹数据生成；
    在云端服务平台的图形化模块中，查找与当前纸面信息所对应的笔迹矩阵及存储值；
    基于所述笔迹矩阵与所述存储值所对应的生成时间，对所述笔迹矩阵及所述存储值进行排序，形成图形化排序结果；
    基于所述图形化排序结果，在当前页面上依序按照所述笔迹矩阵与所述存储值所对应的图形样式，形成所述笔迹数据在当前页面的图形化文件。
  8. 一种智能笔字符识别装置，其特征在于，包括：
    获取模块，用于获取需要进行字符识别的图形化文件，所述图形化文件由智能笔的笔迹生成；
    查找模块，用于在云端服务平台中查找生成所述图形化文件的笔迹矩阵及存储值；
    判断模块，用于在预设的字符识别数据库中，判断是否存在与所述笔迹矩阵及存储值对应的历史字符；
    执行模块，用于存在与所述笔迹矩阵及存储值对应的历史字符时，则直接使用所述历史字符作为图形化文件中待识别图形的字符。
  9. 一种电子设备，其特征在于，所述电子设备包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行前述利要求1-7中任一项所述的智能笔字符识别方法。
  10. 一种非暂态计算机可读存储介质，该非暂态计算机可读存储介质存储计算机指令，该计算机指令用于使该计算机执行前述权利要求1-7中任一项所述的方法。
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