CN112766247B - Question processing method and device, electronic equipment and computer storage medium - Google Patents

Question processing method and device, electronic equipment and computer storage medium Download PDF

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CN112766247B
CN112766247B CN202110380400.XA CN202110380400A CN112766247B CN 112766247 B CN112766247 B CN 112766247B CN 202110380400 A CN202110380400 A CN 202110380400A CN 112766247 B CN112766247 B CN 112766247B
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target
connecting line
standard
image
connection
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CN112766247A (en
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宁亚光
李毅飒
李兵
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Beijing Century TAL Education Technology Co Ltd
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Beijing Century TAL Education Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • 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
    • G06V30/40—Document-oriented image-based pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection

Abstract

The embodiment of the application provides a topic processing method and device, electronic equipment and a computer storage medium. The method comprises the following steps: acquiring a target image, and carrying out target detection on the target image to obtain the area information of each target connecting line object in the target image; acquiring the area information of each standard connecting object in the standard image matched with the target image and the standard connecting relation among the standard connecting objects; determining the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image; combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; classifying the target connecting line pairs respectively to obtain the class information of the target connecting line pairs; and performing question processing according to the corresponding relation, the standard connecting line relation among the standard connecting line objects and the class information of each target connecting line pair to obtain a processing result. According to the embodiment of the application, the efficiency of processing the connection questions is improved, and the labor cost is reduced.

Description

Question processing method and device, electronic equipment and computer storage medium
Technical Field
The embodiment of the application relates to the technical field of image recognition, in particular to a topic processing method and device, electronic equipment and a computer storage medium.
Background
With the rapid development of online education, a plurality of teaching auxiliary products are produced at the same time. In the teaching process, the product can provide technical support for teachers, the workload of the teachers is reduced, and automatic processing of questions is realized, for example: the correctness of the student answering questions is automatically judged, corrected and the like.
The automatic processing scheme at the present stage is mainly carried out for the questions of filling blank questions, selecting questions and orally calculating questions, wherein the questions are waiting for the answers to be filled with conventional characters. For more special questions such as line questions, automatic processing cannot be realized at present.
Disclosure of Invention
The application aims to provide a topic processing method, a topic processing device, electronic equipment and a computer storage medium, which are used for realizing automatic processing of a connection topic.
According to a first aspect of embodiments of the present application, there is provided a title processing method, including:
acquiring a target image containing a to-be-processed connection problem, and performing target detection on the target image to obtain area information of each target connection object in the target image;
acquiring the area information of each standard connecting line object in a standard image matched with the target image and the standard connecting line relation among the standard connecting line objects;
determining the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image based on the area information of the standard connecting line object and the area information of the target connecting line object;
combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; the plurality of target connecting line pairs are classified respectively to obtain class information of each target connecting line pair, and the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs;
and performing title processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the class information of each target connecting line pair in the target image to obtain a processing result.
According to a second aspect of embodiments of the present application, there is provided a title processing apparatus, the apparatus including:
the target detection module is used for acquiring a target image containing a to-be-processed connecting line question and carrying out target detection on the target image to obtain area information of each target connecting line object in the target image;
the information acquisition module is used for acquiring the area information of each standard connecting object in the standard image matched with the target image and the standard connecting relation among the standard connecting objects;
a corresponding relation determining module, configured to determine, based on the area information of the standard link object and the area information of the target link object, a corresponding relation between each target link object in the target image and each standard link object in the standard image;
the category information obtaining module is used for combining every two target connecting line objects in the target image to obtain a plurality of target connecting line pairs; the plurality of target connecting line pairs are classified respectively to obtain class information of each target connecting line pair, and the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs;
and the processing result obtaining module is used for performing topic processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the category information of each target connecting line pair in the target image to obtain a processing result.
According to a third aspect of embodiments herein, there is provided an electronic apparatus, the apparatus comprising: one or more processors; a computer readable medium configured to store one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the title processing method according to the first aspect.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable medium on which a computer program is stored, the program, when executed by a processor, implementing the title processing method according to the first aspect.
According to the title processing method, the title processing device, the electronic equipment and the computer storage medium, a target image containing a to-be-processed connection title is obtained, target detection is carried out on the target image, and area information of each target connection object in the target image is obtained; acquiring the area information of each standard connecting line object in a standard image matched with the target image and the standard connecting line relation among the standard connecting line objects; determining the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image based on the area information of the standard connecting line object and the area information of the target connecting line object; combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; the plurality of target connecting line pairs are classified respectively to obtain class information of each target connecting line pair, and the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs; and performing title processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the class information of each target connecting line pair in the target image to obtain a processing result.
In the embodiment of the application, based on the standard image matched with the target image, the standard connection relation between each standard connection object in the standard image can be obtained, and then the correct connection relation between each target connection object in the target image can be determined through the corresponding relation between each target connection object and each standard connection object, so that the connection relation between each target connection object in the target image can be processed based on the correct connection relation, and the processing result can be automatically obtained. The process does not need manual participation, and automatic processing of the connection questions is achieved. Compared with a processing method which needs manual participation, the method improves the processing efficiency of the connection questions and reduces the labor cost.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of steps of a topic processing method according to a first embodiment of the present application;
FIG. 2 is a schematic diagram of a target image containing a topic of links to be processed;
FIG. 3 is a schematic illustration of a standard image matched to the target image shown in FIG. 2;
FIG. 4 is a schematic diagram of a pair of target lines in the target image of FIG. 2;
FIG. 5 is a flowchart illustrating steps of a topic processing method according to a second embodiment of the present application;
FIG. 6 is a diagram illustrating a topic processing flow according to an embodiment II of the present application;
FIG. 7 is a schematic diagram illustrating region information of each target link object in the target image shown in FIG. 2;
FIG. 8 is a schematic diagram of sequence numbers of a target link object and a standard link object;
FIG. 9 is a schematic diagram of a minimum bounding rectangle region containing a target link object in a target link pair;
FIG. 10 is a schematic view of a link pair image;
FIG. 11 is a schematic structural diagram of a topic processing apparatus in the third embodiment of the present application;
fig. 12 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application;
fig. 13 is a hardware structure of an electronic device according to a fifth embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example one
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a title processing method according to a first embodiment of the present application.
The title processing method of the embodiment of the application comprises the following steps:
step 101, obtaining a target image containing a to-be-processed connection problem, and performing target detection on the target image to obtain area information of each target connection object in the target image.
The target connecting line object is an object to be connected in the target image. For ease of understanding, the target link object is illustrated by way of example: referring to fig. 2, fig. 2 is a schematic diagram of a target image containing a to-be-processed connecting line question, where the target image contains 6 target connecting line objects in total, each elliptical region corresponds to one target connecting line object, and region information of the target connecting line objects may be information representing positions of the elliptical regions in the target image.
In the embodiment of the present application, target detection may be performed on a target image in an appropriate manner to obtain area information of each target connection object in the target image, for example: the target detection can be carried out on the target image by adopting a general target detection algorithm, and the target detection of the target image can also be realized by a neural network model. In the embodiment of the present application, the specific manner adopted in the target detection is not limited.
Step 102, obtaining the area information of each standard connecting object in the standard image matched with the target image and the standard connecting relation among the standard connecting objects.
In this step, the standard image includes a standard connection question, and the standard connection question and the connection question to be processed included in the target image are the same connection question. Referring to fig. 3, fig. 3 is a schematic diagram of a standard image matched with the target image shown in fig. 2, and it can be seen that: the topic included in fig. 2 is the same topic as the topic included in fig. 3.
Specifically, the standard image may be an image that is predetermined to match the target image; after the target image is acquired, an image matched with the target image is searched from a preset topic library containing a plurality of images according to text content contained in the target image.
The standard connecting objects are objects to be connected in the standard image, and the standard connecting relation among the standard connecting objects is used for representing the correct connecting relation among the standard connecting objects. Referring to fig. 3, the standard image totally includes 6 standard link objects, each elliptical area corresponds to one standard link object, and the area information of the standard link objects may be information representing positions of the elliptical areas in the standard image.
And 103, determining the corresponding relation between each target connecting object in the target image and each standard connecting object in the standard image based on the area information of the standard connecting object and the area information of the target connecting object.
Since the number of the standard connection objects in the standard image is multiple, and similarly, the number of the target connection objects in the target image is also multiple, in order to ensure that the processing result can be obtained by correctly performing the title processing on the target image based on the standard connection relation between the standard connection objects, it is necessary to determine the corresponding relation between each target connection object and each standard connection object after obtaining the area information of each standard connection object in the standard image and the area information of each target connection object in the target image.
In the embodiment of the present application, the correspondence relationship may be determined in an appropriate manner. For example: respectively performing text recognition based on the two kinds of area information to obtain text information contained in the target connecting line object and text information contained in the standard connecting line object; then, performing text matching so as to determine the corresponding relation between each target connecting object and each standard connecting object; or according to the area information of the standard link object and the area information of the target link object, respectively sequencing the standard link object and the target link object in the same sequencing mode to obtain the serial number of each standard link object and the serial number of each target link object, and then determining the standard link object and the target link object with the same serial number as the link objects with the corresponding relationship. Here, what way to determine the correspondence between the target connection object and the standard connection object is specifically adopted is not limited.
Taking fig. 2 and 3 as an example: in this step, the following may be determined according to the position of each target connection object in fig. 2 and the position of each standard connection object in fig. 3: the target link object in the first row and the first column in fig. 2 corresponds to the standard link object in the first row and the first column in fig. 3; the target link object in the first row and the second column in fig. 2 corresponds to the standard link object in the first row and the second column in fig. 3; by analogy, the corresponding relationship between each target connection object in fig. 2 and each standard connection object in fig. 3 is finally obtained.
104, combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; and classifying the plurality of target connecting line pairs respectively to obtain class information of each target connecting line pair, wherein the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs.
The present step is also explained with reference to fig. 4 by taking the target image shown in fig. 2 as an example: referring to fig. 4, fig. 4 is a schematic diagram of a target connection line pair in the target image shown in fig. 2, and it should be noted that, for the target image shown in fig. 2, 6 target connection line objects included therein are combined two by two to obtain C6 2=15 target pairs of wires, of which only one (first row) is shown in fig. 4.
The general class division algorithm can be adopted to carry out class division on each target connecting line pair; the class division of each target connecting line pair can also be realized through a neural network model, and when the class division is carried out by adopting the neural network model, the neural network model with the existing structure can be adopted, such as: the existing Resnet residual error network and the like can also carry out structural design and parameter training of the network model again according to actual needs, so that a neural network model for carrying out classification on each target connecting line pair is obtained. In the embodiment of the present application, the specific manner adopted when performing category division is not limited.
The class information of the target wire pair is used for indicating: in the target image, a to-be-processed connection relation exists between the target connection line pairs or the to-be-processed connection relation does not exist. For example, after the target connection pair shown in fig. 4 is classified into categories, category information indicating that there is no connection relationship between the target connection pairs may be obtained.
And 105, performing title processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the class information of each target connecting line pair in the target image to obtain a processing result.
The topic processing in the embodiment of the present application may be: and (4) judging the title, namely: judging whether the connection relation between all target connection objects in the question is correct or not; the following steps can be also included: subject correction, namely: judging whether the connection relation between the target connection objects in the title is correct, and correcting or annotating the target connection objects with the wrong connection relation, wherein the specific content of the title processing is not limited.
The processing can be performed on each target connection line, specifically: determining whether a connection relation exists between target connection objects in the target connection pair according to the category information of the target connection pair; determining a standard connecting line object corresponding to the target connecting line object in the target connecting line pair according to the corresponding relation between the target connecting line object and the standard connecting line object; determining whether a connection relation exists between the standard connection objects corresponding to the target connection object according to the standard connection relation among the standard connection objects in the standard image; if the connection relation exists between the target connection objects and the connection relation also exists between the standard connection objects corresponding to the target connection objects, the connection relation between the target connection objects in the target connection pair is correct, otherwise, if the connection relation exists between the target connection objects and the connection relation does not exist between the standard connection objects corresponding to the target connection objects, or if the connection relation does not exist between the target connection objects and the connection relation exists between the standard connection objects corresponding to the target connection objects, the connection relation between the target connection objects in the target connection pair is wrong.
Taking the target connection line pair composed of the target connection object located in the first row and the first column and the target connection object located in the third row and the second column in fig. 2 as an example: step 102, determining that a connection relation exists between the standard connection object located in the first row and the first column in fig. 3 and the standard connection object located in the third row and the second column; in step 103, the following correspondence is determined: the target link object in the first row and the first column in fig. 2 corresponds to the standard link object in the first row and the first column in fig. 3; the target link object in the third row and the second column in fig. 2 corresponds to the standard link object in the third row and the second column in fig. 3; step 104 determines that a connection relation exists between the two target connection objects of the target connection pair. That is, through the above steps 102-104, the following results are obtained: for the target connection line pair composed of the target connection object located in the first row and the first column in fig. 2 and the target connection object located in the second row and the third column in fig. 2, the connection relationship exists between the target connection objects, and the connection relationship also exists between the standard connection objects corresponding to the target connection objects, so that the connection relationship between the target connection objects in the target connection line pair is correct.
In the embodiment of the application, based on the standard image matched with the target image, the standard connection relation between each standard connection object in the standard image can be obtained, and then the correct connection relation between each target connection object in the target image can be determined through the corresponding relation between each target connection object and each standard connection object, so that the connection relation between each target connection object in the target image can be processed based on the correct connection relation, and the processing result can be automatically obtained. The process does not need manual participation, and automatic processing of the connection questions is achieved. Compared with a processing method which needs manual participation, the method improves the processing efficiency of the connection questions and reduces the labor cost.
The topic processing method of the embodiments of the present application can be performed by any suitable electronic device with data processing capability, including but not limited to: servers, PCs, even high performance mobile terminals, etc.
Example two
Referring to fig. 5, fig. 5 is a flowchart illustrating steps of a title processing method according to a second embodiment of the present application.
In the title processing method of the embodiment of the application, a standard image matched with a target image is obtained from a preset title library. For convenience of explanation, the preset topic library will be briefly described as follows: the preset topic library may comprise: the method comprises the steps of obtaining a plurality of standard images, pre-marking area information of each standard connecting object in each standard image, and obtaining a standard connecting relation among the standard connecting objects in each standard image.
In addition, in the embodiment of the present application, the plurality of target connection lines are classified based on a classification model trained in advance.
The title processing method of the embodiment of the application comprises the following steps:
step 501, obtaining a target image containing a to-be-processed connection problem, and performing target detection on the target image to obtain area information of each target connection object in the target image.
The target connecting line object is an object to be connected in the target image.
In the embodiment of the present application, target detection may be performed on a target image in an appropriate manner to obtain area information of each target connection object in the target image, for example: the target detection can be carried out on the target image by adopting a general target detection algorithm, and the target detection of the target image can also be realized by a neural network model. In the embodiment of the present application, the specific manner adopted in the target detection is not limited.
Step 502, performing text recognition on the target image to obtain a text recognition result.
The method includes the steps of performing text recognition on a target image by using a general text recognition method to obtain a text recognition result, for example, performing text recognition by using a pre-trained text recognition neural network model, and the like.
Step 503, searching an image matched with the target image in a preset question library as a standard image based on the text recognition result; when the preset question library does not have an image matched with the target image, ending the question processing flow; when an image matching the target image exists in the preset topic library, step 504 is executed.
Specifically, text recognition may be performed on each standard image included in the question bank in advance to obtain text information corresponding to each standard image, and the text information is stored in the preset question bank. Then, the text recognition results obtained in step 502 are respectively compared with the text information corresponding to each standard image, so as to search the standard image matched with the target image in the preset topic library.
Step 504, obtaining the area information of each standard connection object in the pre-labeled standard image and the standard connection relation between each standard connection object.
The standard connecting objects are objects to be connected in the standard image, and the standard connecting relation among the standard connecting objects is used for representing the correct connecting relation among the standard connecting objects.
Step 505, determining a corresponding relationship between each target link object in the target image and each standard link object in the standard image based on the area information of the standard link object and the area information of the target link object.
Since the number of the standard connection objects in the standard image is multiple, and similarly, the number of the target connection objects in the target image is also multiple, in order to ensure that the processing result can be obtained by correctly performing the title processing on the target image based on the standard connection relation between the standard connection objects, it is necessary to determine the corresponding relation between each target connection object and each standard connection object after obtaining the area information of each standard connection object in the standard image and the area information of each target connection object in the target image.
Optionally, in some embodiments, determining, based on the area information of the standard link object and the area information of the target link object, a correspondence between each target link object in the target image and each standard link object in the standard image includes:
numbering each standard connecting line object by adopting a preset sequencing mode based on the region information of each standard connecting line object in the standard image to obtain the serial number of each standard connecting line object;
numbering each target connecting line object by adopting the same preset sequencing mode as each standard connecting line object based on the region information of each target connecting line object in the target image to obtain the serial number of each target connecting line object;
and determining the corresponding relation between each target connecting line object and each standard connecting line object according to the serial number of each standard connecting line object and the serial number of each target connecting line pair.
In the embodiment of the present application, what sort method is specifically adopted is not limited, and may be set according to actual conditions. For example: the standard wiring objects can be numbered in sequence from top to bottom, wherein for the standard wiring objects on the same horizontal line, the number of the standard wiring object on the left side is smaller than that of the standard wiring object on the right side; then, the target connection objects are numbered in sequence according to the same sorting mode as the above. The standard connecting line objects can be numbered in sequence from left to right, wherein for the standard connecting line objects on the same plumb line, the number of the standard connecting line object positioned above is smaller than that of the standard connecting line object positioned below; then, the target link objects are numbered in sequence according to the same sorting mode as the above.
Taking fig. 2 and fig. 3 as an example, assuming that the target link objects in fig. 2 are numbered sequentially from top to bottom, where, for the target link objects located on the same horizontal line, the number of the target link object located on the left side is smaller than the number of the target link object located on the right side, then: the number of the target connecting line object positioned in the first row and the first column is 1, the number of the target connecting line object positioned in the first row and the second column is 2, the number of the target connecting line object positioned in the second row and the first column is 3, and so on, the number of the target connecting line object positioned in the third row and the second column is 6; correspondingly, with respect to fig. 3, still in the above sorting manner, it can be obtained that: in fig. 3, the standard link object in the first row and the first column is numbered 1, the standard link object in the first row and the second column is numbered 2, the standard link object in the second row and the first column is numbered 3, and so on, the standard link object in the third row and the second column is numbered 6.
Furthermore, the corresponding relationship between the target connection object and the standard connection object can be determined according to the numbers, that is, the target connection object and the standard connection object with the same numbers can be determined as connection objects with the corresponding relationship: the target connection object with the number of 1 corresponds to the standard connection object with the number of 1; the target connection object with the number of 2 corresponds to the standard connection object with the number of 2; the target connection object with the number of 3 corresponds to the standard connection object with the number of 3; and by analogy, the target connection object with the number of 6 corresponds to the standard connection object with the number of 6.
Compared with the mode of performing text matching based on the text information contained in the target link object and the text information contained in the standard link object and further determining the corresponding relationship between the target link object and each standard link object, the mode only needs to number the standard link objects according to the area information of each standard link object and can determine the corresponding relationship according to the numbers without performing a complicated text matching process, so that the processing process is simpler and the efficiency is higher.
Step 506, combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs.
The present step is also explained with reference to fig. 4 by taking the target image shown in fig. 2 as an example: referring to fig. 4, fig. 4 is a schematic diagram of a target connection line pair in the target image shown in fig. 2, and it should be noted that, for the target image shown in fig. 2, 6 target connection line objects included therein are combined two by two to obtain C6 2=15 target pairs of wires, of which only one (first row) is shown in fig. 4.
And 507, cutting the target image according to the area information of the target connecting objects contained in each target connecting line pair to respectively obtain connecting line pair images containing each target connecting line pair.
Optionally, in some embodiments, the cutting the target image according to the area information of the target connection object included in each target connection pair to obtain connection pair images including each target connection pair respectively includes:
determining a minimum circumscribed rectangular area containing the target connecting line object in each target connecting line pair in the target image according to the area information of the target connecting line object contained in each target connecting line pair;
and cutting the target image according to the minimum circumscribed rectangular area to obtain a connecting line pair image comprising each target connecting line pair.
The target image is cut according to the minimum circumscribed rectangular area containing the target connecting line object in each target connecting line pair, the connecting line pair image obtained by cutting can be ensured to contain the target connecting line object in the target connecting line pair, and meanwhile, other irrelevant information contained in the connecting line pair image can be reduced as much as possible, so that the data volume needing to be processed during subsequent class information detection can be reduced, the class information detection efficiency is improved, and the whole topic processing efficiency is improved.
And step 508, inputting the images of the connecting line pairs into a classification model trained in advance respectively to detect the class information, so as to obtain the class information of each connecting line pair.
In the embodiment of the application, any classification model capable of detecting the category information of the connection line pair image to obtain the category information of the target connection line pair can be adopted, and the specific form of the classification model is not limited herein.
Step 509, according to the corresponding relationship between each target connection object in the target image and each standard connection object in the standard image, the standard connection relationship between each standard connection object in the standard image, and the category information of each target connection pair in the target image, perform topic processing to obtain a processing result.
The topic processing in the embodiment of the present application may be: and (4) judging the title, namely: judging whether the connection relation between all target connection objects in the question is correct or not; the following steps can be also included: subject correction, namely: judging whether the connection relation between the target connection objects in the title is correct, and correcting or annotating the target connection objects with the wrong connection relation, wherein the specific content of the title processing is not limited.
The processing can be performed on each target connection line, specifically: determining whether a connection relation exists between target connection objects in the target connection pair according to the category information of the target connection pair; determining a standard connecting line object corresponding to the target connecting line object in the target connecting line pair according to the corresponding relation between the target connecting line object and the standard connecting line object; determining whether a connection relation exists between the standard connection objects corresponding to the target connection object according to the standard connection relation among the standard connection objects in the standard image; if the connection relation exists between the target connection objects and the connection relation also exists between the standard connection objects corresponding to the target connection objects, the connection relation between the target connection objects in the target connection pair is correct, otherwise, if the connection relation exists between the target connection objects and the connection relation does not exist between the standard connection objects corresponding to the target connection objects, or if the connection relation does not exist between the target connection objects and the connection relation exists between the standard connection objects corresponding to the target connection objects, the connection relation between the target connection objects in the target connection pair is wrong.
In the embodiment shown in fig. 5, based on the standard image matched with the target image, the standard connection relationship between the standard connection objects in the standard image may be obtained, and then, through the corresponding relationship between each target connection object and each standard connection object, the correct connection relationship between each target connection object in the target image may be determined, and further, the connection relationship between each target connection object in the target image may be processed based on the correct connection relationship, so as to automatically obtain the processing result. The process does not need manual participation, and automatic processing of the connection questions is achieved. Compared with a processing method which needs manual participation, the method improves the processing efficiency of the connection questions and reduces the labor cost.
In addition, according to the embodiment of the application, after the target image is acquired, the standard image matched with the target image is searched from the preset topic library containing a plurality of images according to the text content contained in the target image, and compared with the standard image which is manually determined in advance and is matched with the target image, the standard image matched with the target image can be automatically acquired, and manual participation is not needed in the acquisition process, so that the labor cost in the topic processing process can be reduced. Meanwhile, the question bank can contain a large number of different standard images, so that the embodiment of the application can be used for processing various different connection questions, and the application range is wider.
The topic processing method of the embodiments of the present application can be performed by any suitable electronic device with data processing capability, including but not limited to: servers, PCs, even high performance mobile terminals, etc.
Referring to fig. 6, fig. 6 is a schematic view of a topic processing flow provided in the second embodiment of the present application, and the following briefly describes, with reference to fig. 6, the topic processing flow provided in the second embodiment of the present application, which mainly includes:
first, a target image is acquired. The target image is an image including a topic of the connection to be processed, and for example, the target image shown in fig. 2 may be acquired.
And secondly, performing text recognition on the target image to obtain a text recognition result.
And thirdly, searching an image matched with the target image in a preset topic library as a standard image according to the text recognition result. If a standard image matched with the target image is searched, executing the fourth step; if the standard image matching the target image is not searched, the flow of the title processing is ended.
Fourthly, performing target detection on the target image by adopting a general target detection network to obtain the area information of each target connecting object in the target image, for example: the area information of each target link object can be represented in a set manner as follows: bp = { bpiThe/i belongs to {1,2,3, …, n } }, wherein n represents the number of target connecting line objects in the target image; bpiRepresenting an object imageWhen the detected target connection object region is a rectangle, the region information may be represented by coordinates of four vertices of the rectangle, for example, as shown in fig. 7, each rectangular frame in fig. 7 represents the region information of each target connection object in the target image obtained after target detection is performed on the target image shown in fig. 2, that is: and the position of the detection frame corresponding to each target connecting line object.
And fifthly, acquiring annotation information aiming at the standard image, wherein the annotation information comprises two parts: the area information of each standard connecting line object in the standard image; and standard connection relation among the standard connection objects. For example: the area information of each standard connecting line object in the standard image can be represented in a set manner as follows: b isg={bgiI belongs to {1,2,3, …, n } }, wherein n represents the number of standard wiring objects in the standard image (the same as the number of target wiring objects); bgiAnd representing the area information of the ith standard connecting line object in the standard image, wherein the area information can be represented by the coordinates of four vertexes of the rectangle. The standard connection relation between the standard connection objects can be expressed as: { (b)gi,bgj) I ∈ {1,2,3, …, n } }, where bgiAnd bgjRespectively representing two standard connecting line objects with standard connecting line relation.
And sixthly, numbering the standard connecting line object and the target connecting line object respectively in the same sequencing mode to obtain the serial numbers of the standard connecting line object and the target connecting line object respectively. For example: the sorting mode can be as follows: numbering each standard connecting line object from 1 in sequence from top to bottom, wherein the number of the standard connecting line object positioned on the left side is smaller than that of the standard connecting line object positioned on the right side for the standard connecting line objects positioned on the same horizontal line; then, the target connection objects are numbered in sequence according to the same sorting mode as the above. Referring to fig. 8, fig. 8 shows the sequence numbers of the target link object and the standard link object obtained after numbering the target link object in the target image and the standard link object in the standard image shown in fig. 2 according to the above sorting manner. In fig. 8, the left image is a standard image, and the right image is a target image.
Seventhly, combining all target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; for any plurality of target connecting line pairs, determining a minimum circumscribed rectangular area containing a target connecting line object in the target connecting line pair in a target image; and cutting the target image according to the minimum circumscribed rectangular area to obtain a connecting line pair image comprising each target connecting line pair. For example: the dashed line box shown in fig. 9 is the minimum circumscribed rectangular area containing the target link object in the target link pair, and what needs to be described is: only a portion of the minimum bounding rectangle region, not all of it, is shown in fig. 9; correspondingly, fig. 10 is a connection line pair image including a target connection line pair obtained by cutting the target image according to the minimum circumscribed rectangular region located at the lowermost position shown in fig. 9.
And eighthly, detecting the category information of each connecting line pair image by adopting a general classification network model to obtain the category information of each target connecting line pair. That is, it is determined whether or not a link relationship exists between the target link objects in the link pair images.
And ninthly, comparing and matching the category information of each target connecting line pair with the standard connecting line relation between each standard connecting line object to obtain a processing result. Specifically, the method comprises the following steps: for each target link: determining whether a connection relation exists between target connection objects in the target connection pair according to the category information of the target connection pair; determining a standard connecting line object corresponding to the target connecting line object in the target connecting line pair according to the corresponding relation between the target connecting line object and the standard connecting line object; determining whether a connection relation exists between the standard connection objects corresponding to the target connection object according to the standard connection relation among the standard connection objects in the standard image; if the connection relation exists between the target connection objects and the connection relation also exists between the standard connection objects corresponding to the target connection objects, the connection relation between the target connection objects in the target connection pair is correct, otherwise, if the connection relation exists between the target connection objects and the connection relation does not exist between the standard connection objects corresponding to the target connection objects, or if the connection relation does not exist between the target connection objects and the connection relation exists between the standard connection objects corresponding to the target connection objects, the connection relation between the target connection objects in the target connection pair is wrong.
The topic processing flow shown in fig. 6 can automatically perform topic processing, and thus a processing result can be obtained. The process does not need manual participation, and automatic processing of the connection questions is achieved. Compared with a processing method which needs manual participation, the method improves the processing efficiency of the connection questions and reduces the labor cost.
Meanwhile, according to the title processing flow, after the target image is obtained, the standard image matched with the target image is searched from the preset title library containing a plurality of images according to the text content contained in the target image, and compared with the standard image which is manually determined in advance and is matched with the target image, the standard image matched with the target image can be automatically obtained, and manual participation is not needed in the obtaining process, so that the labor cost in the title processing process can be reduced. Meanwhile, the question bank can contain a large number of different standard images, so that the embodiment of the application can be used for processing various different connection questions, and the application range is wider.
In addition, when the target image is cut to obtain the connecting line pair image, the target image is cut according to the minimum external rectangular area containing the target connecting line object in each target connecting line pair, so that the connecting line pair image obtained by cutting contains the target connecting line object in the target connecting line pair, and meanwhile, other irrelevant information contained in the connecting line pair image is reduced as much as possible, so that the data amount required to be processed during subsequent class information detection can be reduced, the class information detection efficiency is improved, and the whole topic processing efficiency is improved.
EXAMPLE III
Referring to fig. 11, fig. 11 is a schematic structural diagram of a topic processing apparatus in the third embodiment of the present application. The title processing apparatus provided by the embodiment of the application includes:
the target detection module 1101 is configured to acquire a target image including a to-be-processed connection object, and perform target detection on the target image to obtain area information of each target connection object in the target image;
an information obtaining module 1102, configured to obtain area information of each standard link object in a standard image matched with the target image, and a standard link relationship between each standard link object;
a correspondence determining module 1103, configured to determine, based on the area information of the standard link object and the area information of the target link object, a correspondence between each target link object in the target image and each standard link object in the standard image;
a category information obtaining module 1104, configured to combine every two target connection objects in the target image to obtain a plurality of target connection pairs; classifying the plurality of target connecting line pairs respectively to obtain class information of each target connecting line pair, wherein the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs;
a processing result obtaining module 1105, configured to perform topic processing according to the correspondence between each target connection object in the target image and each standard connection object in the standard image, the standard connection relationship between each standard connection object in the standard image, and the category information of each target connection pair in the target image, so as to obtain a processing result.
Optionally, in an embodiment of the present application, when performing the step of performing category division on the plurality of target connection pairs respectively to obtain category information of each target connection pair, the category information obtaining module 1104 is specifically configured to:
cutting the target image according to the area information of the target connecting objects contained in each target connecting line pair to respectively obtain connecting line pair images containing each target connecting line pair;
and respectively detecting the category information of the connecting line pair images to obtain the category information of each target connecting line pair.
Optionally, in an embodiment of the present application, when executing the step of performing category information detection on the connection line pair images respectively to obtain category information of each target connection line pair, the category information obtaining module 1104 is specifically configured to:
and respectively inputting the images of the connecting line pairs into a classification model which is trained in advance to carry out class information detection, so as to obtain class information of each connecting line pair.
Optionally, in an embodiment of the present application, when executing the step of cutting the target image according to the area information of the target connection object included in each target connection line pair, and respectively obtaining connection line pair images including each target connection line pair, the category information obtaining module 1104 is specifically configured to:
determining a minimum circumscribed rectangular area containing the target connecting line object in each target connecting line pair in the target image according to the area information of the target connecting line object contained in each target connecting line pair;
and cutting the target image according to the minimum circumscribed rectangular area to obtain a connecting line pair image comprising each target connecting line pair.
Optionally, in an embodiment of the present application, the correspondence determining module 1103 is specifically configured to:
numbering each standard connecting line object by adopting a preset sequencing mode based on the region information of each standard connecting line object in the standard image to obtain the serial number of each standard connecting line object;
numbering each target connecting line object by adopting the same preset sequencing mode as each standard connecting line object based on the region information of each target connecting line object in the target image to obtain the serial number of each target connecting line object;
and determining the corresponding relation between each target connecting line object and each standard connecting line object according to the serial number of each standard connecting line object and the serial number of each target connecting line object.
Optionally, in an embodiment of the present application, the information obtaining module 1102 is specifically configured to:
performing text recognition on the target image to obtain a text recognition result;
searching an image matched with the target image in a preset question library based on a text recognition result to be used as a standard image;
and acquiring the area information of each standard connecting object in the pre-labeled standard image and the standard connecting relation among the standard connecting objects.
The topic processing device in the embodiment of the application is used for implementing the corresponding topic processing method in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again. In addition, the functional implementation of each module in the topic identification device in the embodiment of the present application can refer to the description of the corresponding part in the foregoing method embodiment, and is not repeated here.
Example four
Fig. 12 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application; the electronic device may include:
one or more processors 1201;
a computer-readable medium 1202, which may be configured to store one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the title processing method as in the first or second embodiment.
EXAMPLE five
Fig. 13 is a hardware structure of an electronic device according to a fifth embodiment of the present application; as shown in fig. 13, the hardware structure of the electronic device may include: a processor 1301, a communication interface 1302, a computer readable medium 1303 and a communication bus 1304;
wherein, the processor 1301, the communication interface 1302 and the computer readable medium 1303 complete the communication with each other through the communication bus 1304;
alternatively, the communication interface 1302 may be an interface of a communication module, such as an interface of a GSM module;
the processor 1301 may be specifically configured to: acquiring a target image containing a to-be-processed connection problem, and performing target detection on the target image to obtain area information of each target connection object in the target image; acquiring the area information of each standard connecting object in the standard image matched with the target image and the standard connecting relation among the standard connecting objects; determining the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image based on the area information of the standard connecting line object and the area information of the target connecting line object; combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; classifying the plurality of target connecting line pairs respectively to obtain class information of each target connecting line pair, wherein the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs; and performing question processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the class information of each target connecting line pair in the target image to obtain a processing result.
Processor 1301 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The computer-readable medium 1303 may be, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
In particular, according to an embodiment of the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code configured to perform 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 section, and/or installed from a removable medium. The computer program, when executed by a Central Processing Unit (CPU), performs the above-described functions defined in the method of the present application. It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable medium can 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 storage media (RAM), a read-only storage media (ROM), an erasable programmable read-only storage media (EPROM or flash memory), an optical fiber, a portable compact disc read-only storage media (CD-ROM), an optical storage media piece, a magnetic storage media piece, or any suitable combination of the foregoing. In the present application, 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 this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, 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: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code configured to carry out operations for the present application 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 operate over any of a variety of networks: including a Local Area Network (LAN) or a Wide Area Network (WAN) -to the user's computer, or alternatively, to an external computer (e.g., 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 application. 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 configured to implement the specified logical function(s). In the above embodiments, specific precedence relationships are provided, but these precedence relationships are only exemplary, and in particular implementations, the steps may be fewer, more, or the execution order may be modified. That is, 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 modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprises a target detection module, an information acquisition module, a corresponding relation determination module, a category information acquisition module and a processing result acquisition module. For example, the target detection module may also be described as a module that acquires a target image including a to-be-processed connection object, and performs target detection on the target image to obtain area information of each target connection object in the target image.
As another aspect, the present application also provides a computer-readable medium on which a computer program is stored, the program, when executed by a processor, implementing the title processing method as described in the first or second embodiment.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring a target image containing a to-be-processed connection problem, and performing target detection on the target image to obtain area information of each target connection object in the target image; acquiring the area information of each standard connecting object in the standard image matched with the target image and the standard connecting relation among the standard connecting objects; determining the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image based on the area information of the standard connecting line object and the area information of the target connecting line object; combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; classifying the plurality of target connecting line pairs respectively to obtain class information of each target connecting line pair, wherein the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs; and performing question processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the class information of each target connecting line pair in the target image to obtain a processing result.
The expressions "first", "second", "said first" or "said second" used in various embodiments of the present disclosure may modify various components regardless of order and/or importance, but these expressions do not limit the respective components. The above description is only configured for the purpose of distinguishing elements from other elements. For example, the first user equipment and the second user equipment represent different user equipment, although both are user equipment. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure.
When an element (e.g., a first element) is referred to as being "operably or communicatively coupled" or "connected" (operably or communicatively) to "another element (e.g., a second element) or" connected "to another element (e.g., a second element), it is understood that the element is directly connected to the other element or the element is indirectly connected to the other element via yet another element (e.g., a third element). In contrast, it is understood that when an element (e.g., a first element) is referred to as being "directly connected" or "directly coupled" to another element (a second element), no element (e.g., a third element) is interposed therebetween.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims (9)

1. A method for processing a topic, the method comprising:
acquiring a target image containing a to-be-processed connection problem, and performing target detection on the target image to obtain area information of each target connection object in the target image;
acquiring the area information of each standard connecting line object in a standard image matched with the target image and the standard connecting line relation among the standard connecting line objects;
numbering each standard connecting line object by adopting a preset sequencing mode based on the area information of each standard connecting line object in the standard image to obtain the serial number of each standard connecting line object; numbering each target connecting line object by adopting the same preset sequencing mode as each standard connecting line object based on the region information of each target connecting line object in the target image to obtain the serial number of each target connecting line object; determining the corresponding relation between each target connecting line object and each standard connecting line object according to the serial number of each standard connecting line object and the serial number of each target connecting line object;
combining the target connecting line objects in the target image pairwise to obtain a plurality of target connecting line pairs; the plurality of target connecting line pairs are classified respectively to obtain class information of each target connecting line pair, and the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs;
and performing title processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the class information of each target connecting line pair in the target image to obtain a processing result.
2. The method according to claim 1, wherein the classifying the plurality of target connection pairs into categories to obtain category information of each target connection pair comprises:
cutting the target image according to the area information of the target connecting line object contained in each target connecting line pair to respectively obtain connecting line pair images containing each target connecting line pair;
and respectively carrying out category information detection on the connecting line pair images to obtain category information of each target connecting line pair.
3. The method of claim 2, wherein the performing class information detection on the link pair images respectively to obtain class information of each target link pair comprises:
and respectively inputting the connecting line pair images into a classification model which is trained in advance to carry out class information detection, so as to obtain class information of each target connecting line pair.
4. The method of claim 2, wherein the cropping the target image according to the area information of the target connection object included in each target connection pair to obtain connection pair images including each target connection pair respectively comprises:
determining a minimum circumscribed rectangular area containing the target connecting line object in each target connecting line pair in the target image according to the area information of the target connecting line object contained in each target connecting line pair;
and cutting the target image according to the minimum circumscribed rectangular area to obtain a connecting line pair image comprising each target connecting line pair.
5. The method according to claim 1, wherein the acquiring of the area information of each standard link object in the standard image matched with the target image and the standard link relationship between the standard link objects comprises:
performing text recognition on the target image to obtain a text recognition result;
searching an image matched with the target image in a preset question library based on the text recognition result to be used as a standard image;
and acquiring the pre-marked area information of each standard connecting object in the standard image and the standard connecting relation among the standard connecting objects.
6. The method of claim 5, further comprising:
and when the preset topic library does not have the image matched with the target image, ending the topic processing flow.
7. A topic processing apparatus, comprising:
the target detection module is used for acquiring a target image containing a to-be-processed connecting line question and carrying out target detection on the target image to obtain area information of each target connecting line object in the target image;
the information acquisition module is used for acquiring the area information of each standard connecting object in the standard image matched with the target image and the standard connecting relation among the standard connecting objects;
the corresponding relation determining module is used for numbering the standard connecting line objects in a preset sequencing mode based on the area information of the standard connecting line objects in the standard image to obtain the serial numbers of the standard connecting line objects; numbering each target connecting line object by adopting the same preset sequencing mode as each standard connecting line object based on the region information of each target connecting line object in the target image to obtain the serial number of each target connecting line object; determining the corresponding relation between each target connecting line object and each standard connecting line object according to the serial number of each standard connecting line object and the serial number of each target connecting line object;
the category information obtaining module is used for combining every two target connecting line objects in the target image to obtain a plurality of target connecting line pairs; the plurality of target connecting line pairs are classified respectively to obtain class information of each target connecting line pair, and the class information is used for indicating that a connecting line relation to be processed exists or a connecting line relation to be processed does not exist between the target connecting line pairs;
and the processing result obtaining module is used for performing topic processing according to the corresponding relation between each target connecting line object in the target image and each standard connecting line object in the standard image, the standard connecting line relation between each standard connecting line object in the standard image and the category information of each target connecting line pair in the target image to obtain a processing result.
8. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the title processing method according to any one of claims 1-6.
9. A computer storage medium, having stored thereon a computer program which, when executed by a processor, implements the title processing method according to any one of claims 1-6.
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