CN111553365A - Method and device for selecting questions, electronic equipment and storage medium - Google Patents
Method and device for selecting questions, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN111553365A CN111553365A CN202010366582.0A CN202010366582A CN111553365A CN 111553365 A CN111553365 A CN 111553365A CN 202010366582 A CN202010366582 A CN 202010366582A CN 111553365 A CN111553365 A CN 111553365A
- Authority
- CN
- China
- Prior art keywords
- question
- area
- stem area
- target page
- page image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000004590 computer program Methods 0.000 claims description 13
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 6
- 241000590419 Polygonia interrogationis Species 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 8
- 230000011218 segmentation Effects 0.000 description 7
- 238000010187 selection method Methods 0.000 description 7
- 230000003993 interaction Effects 0.000 description 6
- 238000012937 correction Methods 0.000 description 3
- 239000013598 vector Substances 0.000 description 3
- 239000002775 capsule Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012015 optical character recognition Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- PCTMTFRHKVHKIS-BMFZQQSSSA-N (1s,3r,4e,6e,8e,10e,12e,14e,16e,18s,19r,20r,21s,25r,27r,30r,31r,33s,35r,37s,38r)-3-[(2r,3s,4s,5s,6r)-4-amino-3,5-dihydroxy-6-methyloxan-2-yl]oxy-19,25,27,30,31,33,35,37-octahydroxy-18,20,21-trimethyl-23-oxo-22,39-dioxabicyclo[33.3.1]nonatriaconta-4,6,8,10 Chemical compound C1C=C2C[C@@H](OS(O)(=O)=O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2.O[C@H]1[C@@H](N)[C@H](O)[C@@H](C)O[C@H]1O[C@H]1/C=C/C=C/C=C/C=C/C=C/C=C/C=C/[C@H](C)[C@@H](O)[C@@H](C)[C@H](C)OC(=O)C[C@H](O)C[C@H](O)CC[C@@H](O)[C@H](O)C[C@H](O)C[C@](O)(C[C@H](O)[C@H]2C(O)=O)O[C@H]2C1 PCTMTFRHKVHKIS-BMFZQQSSSA-N 0.000 description 1
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000001072 colon Anatomy 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
- G06V30/153—Segmentation of character regions using recognition of characters or words
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/16—File or folder operations, e.g. details of user interfaces specifically adapted to file systems
- G06F16/164—File meta data generation
- G06F16/166—File name conversion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- 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
- G06V10/225—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 based on a marking or identifier characterising the area
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Human Computer Interaction (AREA)
- Processing Or Creating Images (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the invention relates to the technical field of topic selection, and discloses a method and a device for topic selection, electronic equipment and a storage medium. The method comprises the following steps: when a trigger instruction is received, acquiring a target page image and image coordinates of an operation body in the target page image; determining a question stem area by using the image coordinates and the target page image, and judging the type of the question stem area; when the category is a big question, selecting all the small questions under the big question as a selection area; when the category is a question, acquiring a question type of a question stem area, and determining a selection area according to the question type; selecting the theme pictures in the selection area. By implementing the embodiment of the invention, a question or a question can be selected in a point contact mode, a user does not need to manually cut the picture of the content to be collected, the operation is simple and convenient, the question selection efficiency is improved, the user experience is improved, meanwhile, the question stem and the option are ensured to be simultaneously selected aiming at the single-channel question, and the accuracy of the frame question is improved.
Description
Technical Field
The invention relates to the technical field of topic selection, in particular to a method and a device for topic selection, electronic equipment and a storage medium.
Background
In order to solve the difficult problems of the homework of primary and secondary schools, a plurality of application programs for searching questions or gathering wrong questions for solving the difficult problems of the homework of students appear on the market at present, and all the application programs shoot the question pictures through a camera to be stored as wrong questions or used for searching corresponding answers. The implementation methods thereof are roughly divided into two main categories:
the first method is to select the question desired by the user manually or by selecting a cutting frame according to an operation track, and the cutting frame is required to be operated for multiple times in such a way, so that the size of the cutting frame is proper, and the picture of the question can be accurately obtained.
The second method is that a point is selected by using an operation body on a bearing body, then a picture is taken according to the point and a question picture is obtained by a certain training model based on a preset rule, which is more intelligent than the first method, but the method is easy to cause that a question stem area is selected and an option area is abandoned, or a question stem of a certain big question is selected, an actual question in the big question is not selected, and the actual meaning of the obtained frame selection area is not great.
Disclosure of Invention
Aiming at the defects, the embodiment of the invention discloses a method and a device for selecting questions, electronic equipment and a storage medium, wherein a selection area is determined according to a question number and a question type.
The first aspect of the embodiment of the invention discloses a topic selection method, which is applied to an intelligent terminal and comprises the following steps:
when a trigger instruction is received, acquiring a target page image and an image coordinate of an operation body in the target page image;
determining a question stem area by using the image coordinates and a target page image, and judging the category of the question stem area;
when the category is a big question, selecting all the small questions under the big question as a selection area;
when the category is a question, obtaining a question type of the question stem area, and determining a selection area according to the question type;
and selecting the theme pictures in the selection area.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, when a trigger instruction is received, acquiring an image coordinate of a target page image and an image coordinate of an operation body in the target page image, includes:
receiving an instruction sent by a user and judging whether the instruction is a trigger instruction or not;
when a trigger instruction is received, starting a camera, and shooting the current page of the carrier to obtain the target page image;
and carrying out coordinate transformation on the position coordinate of the operating body on the bearing body to obtain an image coordinate of the position coordinate corresponding to the target page image.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, determining a stem area by using the image coordinates and a target page image, and determining a category of the stem area includes:
performing character recognition on the target page image;
determining the position of the question stem area in the target page image by using the image coordinates and a preset rule;
acquiring a target question number of the question stem area and auxiliary question numbers of N question stems below the question stem area, wherein N is more than 1;
determining naming rules corresponding to the target question numbers and the auxiliary question numbers;
if the number of the first naming rules is larger than that of the second naming rules, the category of the question stem area is a subtopic; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, selecting all the topics under the topic as the selection area includes:
acquiring a boundary question number in the target page image, wherein the boundary question number is the same as the naming rule of the target question number, the boundary question number is positioned below the target question number, and no other question number with the same naming rule as the target question number exists between the boundary question number and the target question number;
and taking the question stem area and the area between the question stem area and the limit question mark as selection areas.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, obtaining a question type of the question stem area, and determining the selection area according to the question type includes:
extracting the question features and key words of the question stem region;
judging question types corresponding to the question stem areas based on the question features and the keywords;
when the question type of the question stem area is a choice question, taking the question stem area and an option area corresponding to the question stem area as a choice area;
and when the question type of the question stem area is a non-selection question, taking the question stem area as a selection area.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the determining, based on the topic feature and the keyword, a topic type corresponding to the topic stem region includes:
selecting a large number of topic samples, obtaining the topic characteristics and keywords of the topic samples, and taking the topic types corresponding to the topic samples as labels;
training an initial question type recognition model by using the question sample to obtain a trained question type recognition model;
and inputting the question features and the keywords of the question stem area into the question type identification model to obtain the question type corresponding to the question stem area.
The second aspect of the embodiments of the present invention discloses a topic selection device, which is applied to an intelligent terminal, and the device includes:
the acquisition unit is used for acquiring a target page image and image coordinates of an operation body in the target page image when a trigger instruction is received;
the judging unit is used for determining a question stem area by utilizing the image coordinates and the target page image and judging the category of the question stem area;
the first determining unit is used for selecting all the small questions under the big questions as selection areas when the categories are the big questions;
the second determining unit is used for acquiring the question types of the question stem areas when the categories are the subjects and determining the selection areas according to the question types;
and the selecting unit is used for selecting the theme pictures in the selection area.
As an optional implementation manner, in a second aspect of the embodiment of the present invention, the determining unit includes:
the character recognition subunit is used for carrying out character recognition on the target page image;
the position determining subunit is used for determining the position of the question stem area in the target page image by using the image coordinates and a preset rule;
the question mark acquisition subunit is used for acquiring a target question mark of the question stem area and auxiliary question marks of N question stems below the question stem area, wherein N is more than 1;
the rule determining subunit is used for determining a naming rule corresponding to the target question number and the auxiliary question number;
the category judgment subunit is configured to, if the number of the first naming rules is greater than the number of the second naming rules, determine that the category of the question stem area is a subtotal; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
The third aspect of the embodiments of the present invention discloses a method for selecting topics, which is applied to an intelligent terminal and a server, and the method includes:
when the intelligent terminal receives a trigger instruction, acquiring a target page image and sending the target page image to a server;
the server determines the image coordinates of the operation body in the target page image, determines a question stem area by using the image coordinates and the target page image, and judges the category of the question stem area;
when the category is a big question, the server selects all the small questions under the big question as a selection area;
when the category is a question, the server acquires the question type of the question stem area and determines a selection area according to the question type;
and the server selects the topic picture in the selected area.
As an optional implementation manner, in the third aspect of the embodiment of the present invention, the determining, by the server, the image coordinate of the operation body in the target page image, determining a stem area by using the image coordinate and the target page image, and determining the category of the stem area includes:
the server obtains image coordinates of the position coordinates corresponding to the target page image through coordinate transformation of the position coordinates of the operating body on the bearing body;
the server carries out character recognition on the target page image;
the server determines the position of the question stem area in the target page image by using the image coordinates and a preset rule;
the server obtains a target question number of the question stem area and auxiliary question numbers of N question stems below the question stem area, wherein N is more than 1;
the server determines a naming rule corresponding to the target question number and the auxiliary question number;
if the number of the first naming rules is larger than that of the second naming rules, the server judges the type of the question stem area as a subtopic; if the number of the first naming rules is smaller than or equal to the number of the second naming rules, the server judges the type of the question stem area to be a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
The fourth aspect of the embodiments of the present invention discloses a topic selection device, which is applied to an intelligent terminal and a server, and the device includes:
the acquisition unit is positioned in the intelligent terminal and used for acquiring a target page image and sending the target page image to the server when a trigger instruction is received;
the judging unit is positioned in the server and used for determining the image coordinates of the operation body in the target page image, determining the question stem area by using the image coordinates and the target page image and judging the category of the question stem area;
the first determining unit is positioned in the server and used for selecting all the small questions under the big questions as a selection area when the category is the big questions;
the second determining unit is positioned in the server and used for acquiring the question types of the question stem areas when the categories are the questions and determining the selection areas according to the question types;
and the selecting unit is positioned in the server and used for selecting the theme pictures in the selected area.
As an optional implementation manner, in a fourth aspect of the embodiment of the present invention, the determining unit includes:
the coordinate conversion subunit is used for converting the position coordinates of the operating body on the bearing body through coordinates to obtain image coordinates of the position coordinates corresponding to the target page image;
the character recognition subunit is used for carrying out character recognition on the target page image;
the position determining subunit is used for determining the position of the question stem area in the target page image by using the image coordinates and a preset rule;
the question mark acquisition subunit is used for acquiring a target question mark of the question stem area and auxiliary question marks of N question stems below the question stem area, wherein N is more than 1;
the rule determining subunit is used for determining a naming rule corresponding to the target question number and the auxiliary question number;
the category judgment subunit is configured to, if the number of the first naming rules is greater than the number of the second naming rules, determine that the category of the question stem area is a subtotal; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
A fifth aspect of an embodiment of the present invention discloses an electronic device, including: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory to execute the method for selecting the theme disclosed in the first aspect of the embodiment of the present invention.
A sixth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute a method for selecting a topic disclosed in the first aspect of the present invention.
A seventh aspect of the embodiments of the present invention discloses a computer program product, which, when running on a computer, enables the computer to execute the method for selecting a topic disclosed in the first aspect of the embodiments of the present invention.
An eighth aspect of the present invention discloses an application publishing platform, where the application publishing platform is configured to publish a computer program product, and when the computer program product runs on a computer, the computer is enabled to execute a method for selecting a topic disclosed in the first aspect of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, when a trigger instruction is received, a target page image and image coordinates of an operation body in the target page image are obtained; determining a question stem area by using the image coordinates and a target page image, and judging the category of the question stem area; when the category is a big question, selecting all the small questions under the big question as a selection area; when the category is a question, obtaining a question type of the question stem area, and determining a selection area according to the question type; and selecting the theme pictures in the selection area. Therefore, by implementing the embodiment of the invention, the selection area can be determined through the question number or the question number and the question type, a plurality of small questions of a big question can be simultaneously selected, the question selection efficiency is improved, the question stem and the option can be simultaneously selected when the single-channel small questions are aimed at, and the accuracy of the frame question is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a topic selection method disclosed in an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another topic selection method disclosed in the embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a topic selection apparatus disclosed in the embodiments of the present invention;
FIG. 4 is a schematic structural diagram of another topic selection apparatus disclosed in the embodiments of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure;
fig. 6 is a schematic structural diagram of a topic selection system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first", "second", "third", "fourth", and the like in the description and the claims of the present invention are used for distinguishing different objects, and are not used for describing a specific order. The terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses a method and a device for selecting titles, electronic equipment and a storage medium, wherein a selection frame can be obtained only by constructing a first straight line and a second straight line according to a start point coordinate and an end point coordinate of a moving track, the operation is very simple and convenient, the completeness of titles can be ensured, the user experience is improved, and the detailed description is carried out in combination with the attached drawings.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart of a topic selection method disclosed in an embodiment of the present invention, where topic selection is all completed in an intelligent terminal. The intelligent terminal comprises but is not limited to a learning machine, a family education machine, a point reading machine, a tablet computer or a mobile phone. The question selection is used for storing the selected questions or searching the questions, the question storage can be applied to wrong question summarization or knowledge point summarization and the like, and the searched questions are answers for searching the questions through the Internet after the questions are selected. As shown in FIG. 1, the title selecting method includes the following steps:
110. and when a trigger instruction is received, acquiring a target page image and an image coordinate of an operation body in the target page image.
The instruction is initiated by the user, and the instruction can be initiated by the user through voice, for example: the "please help me save the question(s)", may be initiated by the user through a touch key or a mechanical key, or may be automatically triggered when the user opens a certain application program, for example, a wrong question collection APP or a question searching APP, and is not limited herein.
The instructions received by the intelligent terminal can be question storage instructions or question searching instructions and the like, the instructions are all based on question selection, and the questions are used for storage or searching after being selected.
Before receiving the instruction, the camera and most devices of the intelligent terminal are in a sleep state, so that the electric quantity can be saved, the intelligent terminal is waken up through the instruction, the intelligent terminal judges whether the instruction is the instruction for triggering the question selection, and if the instruction is the instruction, the camera is started. The camera can be a front camera or a rear camera carried by the intelligent terminal, and can also be an external camera separated from the intelligent terminal and in communication connection with the intelligent terminal.
The supporting body can be a book, an exercise book, a test paper and the like, and the operation body can be a finger, a touch pen, a pencil, a ruler, a small stick and the like. After the intelligent device receives the trigger instruction, the intelligent device can guide the user to place the position of the operation body in a voice interaction mode. For the convenience of positioning of the user, the point contact can be a contact point at a certain position of the question stem of the question (for example, a blank at the lower side of the question stem). In this case, a problem arises that if the question stem selected by the user is a big question stem (e.g., a first choice question), the selection of the question stem is unambiguous, actually represents that the user selects all the small questions of the first big question, and if any user selects the question stem of a certain choice question, the selection of the question with the missing choice is also meaningless for the user.
According to the invention, the intelligent identification can be carried out on the big or small questions selected by the user. Specifically, no matter whether the user selects the big question or the small question, the camera shoots the page of the user selected question integrally to obtain a target page image. Before the corresponding processing is carried out on the target page image, the target page image can be preprocessed firstly so as to ensure the accuracy of character recognition. The preprocessing includes but is not limited to denoising, contrast enhancement, shape correction and the like, the shape correction mainly aims at the problem of the visual angle of a camera to shoot a trapezoidal image or the curling condition of a carrier, the shape correction can be realized by stretching the edge of a target page image and the like, and the finally obtained target page image is rectangular.
After the corrected target page image is obtained, the position coordinates of the operation body on the bearing body are obtained in a coordinate conversion mode to obtain the position coordinates of the operation body on the target page image, and the coordinate conversion can be realized through an affine transformation algorithm. Based on the position coordinates of the operation body on the target page image, the question stem area can be obtained.
120. And determining a question stem area by using the image coordinates and the target page image, and judging the type of the question stem area.
And after obtaining the target page image and the image coordinates, performing character recognition on the target page image.
Character Recognition can be realized by a mature OCR (Optical Character Recognition) technology, and characters include chinese characters, letters, punctuation marks, formulas and the like. And determining the question stem area by using the image coordinates and a preset rule. For example, the preset rule may be that the upper side position of the image coordinate is a question stem region, in this case, the horizontal line where the image coordinate is located is a lower segmentation line, the upper segmentation line is determined by setting a threshold value based on that the interval between the questions is greater than the line interval of the question stem, and the region between the upper and lower segmentation lines is the question stem region, so as to obtain the positions of the question stem region and the question stem region in the target page image.
The question stem area is provided with a question number, the question number is identified, the question number of the question stem area is called a target question number, the question numbers of N (N >1) question stems appearing below the question stem area are called auxiliary question numbers, and the type of the question stem area can be determined to be a big question or a small question through the target question number and the auxiliary question numbers.
The title is realized by a certain title naming rule. The title naming convention can include, but is not limited to, the form of numeric characters (e.g., roman numerals, chinese numerals, etc.) plus punctuation characters (e.g., pause, comma, english period, colon, etc.), the form of numeric characters plus chinese parentheses, etc., such as (1), (2), etc. Generally, Chinese numeric plus punctuation marks are used as categories of major questions, Roman characters plus punctuation marks are used as categories of minor questions, and numeric characters plus Chinese parentheses are used as subcategories of categories of minor questions.
The size questions can be identified regardless of the type of the size questions used. The question mark is generally positioned at the initial position of the head line of the question stem area, the question mark is obtained by character recognition of the initial position of the head line, and the big question generally comprises more than two small questions (if one big question only comprises one small question, the meaning of the big question is not great).
A carrier generally adopts a naming rule of a big question and a small question, and based on the naming rule, the classification of the question stem area can be obtained under the condition that the naming rule of the target question number and the auxiliary question number is determined.
If the number of the first naming rules is larger than that of the second naming rules, the category of the question stem area is a subtopic; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
If the question stem area is a big question which at least comprises two small questions, if two auxiliary question numbers are selected, the number of naming rules of the big question is 1, the number of naming rules of the small questions is 2, and based on the judgment mode, the type of the question stem area can be reversely deduced to be the big question; if three auxiliary question numbers are selected, the question stem area only comprises two small questions, the number of the naming rules of the large questions is 2, the number of the naming rules of the small questions is 2, and based on the judgment mode, the category of the question stem area can be reversely deduced to be the large questions, and so on.
If the question stem area is a small question, if the small question is located at a position, close to the front, of the corresponding big question, the selected auxiliary question numbers are possibly the same as the naming rules of the small question, the number of the naming rules of the small question is certainly larger than that of the naming rules of the big question, based on the judgment mode, the type of the question stem area can be reversely deduced to be the small question.
And determining a selection area according to the type of the obtained question stem area, wherein the selection area is used for all the small questions of the big question and the selection area is used for all the contents of the small questions for the small questions. As shown with particular reference to steps 130 and 140, respectively.
130. And when the category is a big question, selecting all the small questions under the big question as a selection area.
The choice area of a topic is all the topics it contains. The step determines the upper segmentation line of the question stem area, and the upper segmentation line of the question stem area is also used as the upper segmentation line of the selection area. And traversing the question number of the question stem below the question stem area to obtain a boundary question number which is the same as the naming rule of the target question number and is adjacent to the target question number, wherein other question numbers with the same naming rule as the boundary question number and the target question number do not exist between the boundary question number and the target question number.
And selecting the areas between the upper dividing line and the lower dividing line of the selection area by taking the upper blank area of the boundary question number as the lower dividing line of the selection area, thus obtaining the selection area of the big question.
If no boundary question number exists, all contents below the upper segmentation line are used as a selection area of the big question.
140. And when the category is the subject, obtaining the subject type of the subject stem area, and determining a selection area according to the subject type.
Normally, other question types do not generally include options except that the selection question has options, and therefore, in the embodiment of the present invention, after the type corresponding to the question stem region is determined, the selection region is determined for the selection question and other question types respectively.
There are various ways to distinguish the topic types.
Illustratively, topic features and keywords of a topic stem area are obtained. The title feature may be content with underlines or Chinese parentheses or blank areas, and the keywords may be: selecting one, judging, filling in, calculating, simplifying, proving and the like. And then inputting the question features and the keywords into a pre-trained question type recognition model to obtain the type corresponding to the question stem area. The topic type identification model can be realized based on a convolutional neural network, and is obtained by selecting a large number of topic samples, using the topic types of the large number of topic samples as labels, and using the topic characteristics and keywords of the topic samples as input parameters for training.
The method can also be completed by adopting an unsupervised training mode, namely, characters in the question stem area are converted into sentence characteristic vectors through character conversion (such as BERT), and then the sentence characteristic vectors are input into a pre-trained neural network recognition model (such as a capsule network) with a constraint relation to obtain the question patterns of the question stem area, wherein the capsule network recognition model is trained by taking the question patterns of a large number of question samples as labels and taking the sentence characteristic vectors after the question samples are converted as input parameters.
When the type of the question stem area is a subject and the corresponding question type is a selection question, the selection area generally comprises options such as ABCD and the like, the identification technology of the selection area is mature, and the question stem area and the selection area corresponding to the question stem area are used as the selection area together.
When the type of the question stem area is a question and the corresponding question type is a non-choice question, the option area is directly used as the choice area.
In fact, the selection area of the small question can also be realized in a manner similar to the large question, the limit question number of the small question is determined, the limit question number of the small question is the question number of any preset rule adjacent to the lower part of the small question, a lower dividing line is arranged at the blank position above the limit question number, and the selection area of the small question is formed between the lower dividing line and the upper dividing line of the question stem area.
150. And selecting the theme pictures in the selection area.
Selecting a topic picture in the area, namely a target selected by a topic, storing a user of the topic picture as wrong topic collection or knowledge point collection, and searching the characters converted from the topic picture to obtain an answer of the topic picture corresponding to the topic. For the big problem, it is used to search the problem by dividing it into different small images according to the problem number and then searching the problem one by one. Therefore, the preferred scenario for use in the embodiments of the present invention is topic collection.
In order to better guide the user to select a topic, after the user generates a trigger instruction, the user may be guided to locate a contact point in a voice interaction manner through the intelligent terminal, where the voice interaction may be: if a large question is selected, please place a finger in the blank area under the question stem of the large question, and if a small question is selected, please place a finger in the blank area under the question stem of the small question.
By implementing the embodiment of the invention, a question or a question can be selected in a point contact mode, a user does not need to manually cut the picture of the content to be collected, the operation is simple and convenient, the question selection efficiency is improved, the user experience is improved, meanwhile, the question stem and the option are ensured to be simultaneously selected aiming at the single-channel question, and the accuracy of the frame question is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic flowchart of another topic selection method disclosed in the embodiment of the present invention, where topic selection is completed in an interaction between an intelligent terminal and a server. As shown in FIG. 2, the title selecting method includes the following steps:
210. when the intelligent terminal receives a trigger instruction, a target page image is obtained and sent to a server.
220. And the server determines the image coordinates of the operation body in the target page image, determines a question stem area by using the image coordinates and the target page image, and judges the category of the question stem area.
230. And when the category is a big question, the server selects all the small questions under the big question as a selection area.
240. And when the category is the subject, the server acquires the subject type of the subject stem area and determines a selection area according to the subject type.
250. And the server selects the topic picture in the selected area.
The steps 210-250 are similar to the steps 110-150 in the first embodiment. In the second embodiment, the coordinate transformation in step 110 and the content completed in the intelligent terminal in steps 120-150 are delivered to the server to complete, and the preprocessing of the target page image can be completed in the intelligent terminal or/and the server. The problem selection is completed in a server and intelligent terminal interaction mode, the problem selection efficiency can be improved, and the CPU occupancy rate of the intelligent terminal is reduced.
Based on different frame titles of the user, the server also returns different operation results to the user: aiming at the question searching instruction, the server feeds back answers of the search questions to the user; and returning a storage result to the user by the server aiming at the question collection instruction, and enabling the user to check the questions stored in the server database on the intelligent terminal.
By implementing the embodiment of the invention, a question or a question can be selected in a point contact mode, a user does not need to manually cut the picture of the content to be collected, the operation is simple and convenient, the question selection efficiency is improved, the user experience is improved, meanwhile, the question stem and the option are ensured to be simultaneously selected aiming at the single-channel question, and the accuracy of the frame question is improved.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of a topic selecting device disclosed in the embodiment of the present invention, and the topic selecting device is applied to an intelligent terminal. As shown in fig. 3, the title selecting device may include:
the acquiring unit 310 is configured to acquire a target page image and image coordinates of an operating body in the target page image when a trigger instruction is received;
the judging unit 320 is configured to determine a stem area by using the image coordinates and the target page image, and judge a category of the stem area;
a first determining unit 330, configured to select all the small questions under the big question as a selection area when the category is the big question;
a second determining unit 340, configured to, when the category is a question, obtain a question type of the question stem area, and determine a selection area according to the question type;
a selecting unit 350, configured to select the topic picture in the selection area.
As an optional implementation manner, the obtaining unit 310 may include:
the instruction receiving subunit 311 is configured to receive an instruction sent by a user and determine whether the instruction is a trigger instruction;
the image obtaining subunit 312 is configured to, when receiving the trigger instruction, start the camera, and take a picture of the current page of the carrier to obtain the target page image;
and the coordinate conversion subunit 313 is configured to perform coordinate conversion on the position coordinates of the operating body on the carrier to obtain image coordinates of the position coordinates corresponding to the target page image.
As an optional implementation manner, the determining unit 320 may include:
a character recognition subunit 321, configured to perform character recognition on the target page image;
a position determining subunit 322, configured to determine, by using the image coordinates and a preset rule, a position of the stem area in the target page image;
the question mark acquisition subunit 323 is used for acquiring a target question mark of the question stem area and auxiliary question marks of N question stems below the question stem area, wherein N is more than 1;
a rule determining subunit 324, configured to determine a naming rule corresponding to the target question number and the auxiliary question number;
a category judgment subunit 325, configured to, if the number of the first naming rules is greater than the number of the second naming rules, determine that the category of the question stem area is a subtotal; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
As an optional implementation manner, the first determining unit 330 may include:
the boundary question number acquiring subunit 331 is configured to acquire a boundary question number in the target page image, where the boundary question number is the same as a naming rule of the target question number, the boundary question number is located below the target question number, and no other question number having the same naming rule as the target question number exists between the boundary question number and the target question number;
a first area determining subunit 332, configured to use the question stem area and an area between the question stem area and the limit question mark as a selection area.
As an optional implementation manner, the second determining unit 340 may include:
a feature extraction subunit 341, configured to extract topic features and keywords of the topic stem region;
the question type identifying subunit 342 is configured to select a large number of question samples, obtain question features and keywords of the question samples, and use question types corresponding to the question samples as tags; training an initial question type recognition model by using the question sample to obtain a trained question type recognition model; inputting the question features and the keywords of the question stem area into the question type identification model to obtain a question type corresponding to the question stem area;
a second area determining subunit 343, configured to, when the question type of the question stem area is a choice question, use the question stem area and an option area corresponding to the question stem area as a choice area;
a third area determination subunit 344, configured to use the question stem area as a selection area when the question type of the question stem area is a non-selection question.
The question selecting device shown in fig. 3 can select a question or a question in a point contact manner, does not need a user to manually cut a picture of a content to be collected, is simple and convenient to operate, improves the question selecting efficiency, improves the user experience, and simultaneously ensures that a question stem and a choice are simultaneously selected for a single-channel question, thereby improving the accuracy of a frame question.
Example four
Referring to fig. 4, fig. 4 is a schematic structural diagram of another topic selecting device disclosed in the embodiment of the present invention, which is applied to interaction between an intelligent terminal and a server. As shown in fig. 4, the title selecting device may include:
the acquiring unit 410 is located in the intelligent terminal 400 and configured to acquire a target page image when a trigger instruction is received, and send the target page image to a server;
a determining unit 510, located in the server 500, configured to determine an image coordinate of the operation body in the target page image, determine a question stem area by using the image coordinate and the target page image, and determine a category of the question stem area;
a first determining unit 520, located in the server 500, configured to select all the small questions under the big question as a selection area when the category is the big question;
a second determining unit 530, located in the server 500, configured to, when the category is a question, obtain a question type of the question stem area, and determine a selection area according to the question type;
a selecting unit 540, located in the server 500, is configured to select the topic picture in the selection area.
As an optional implementation manner, the obtaining unit 410 may include:
an instruction receiving subunit 411, configured to receive an instruction sent by a user and determine whether the instruction is a trigger instruction;
the image obtaining subunit 412 is configured to start a camera when receiving a trigger instruction, and take a picture of a current page of the carrier to obtain the target page image;
and an image sending subunit 413, configured to send the target page image to a server.
As an optional implementation manner, the determining unit 510 may include:
a coordinate conversion subunit 511, configured to perform coordinate conversion on the position coordinate of the operating body on the carrier to obtain an image coordinate of the position coordinate corresponding to the target page image;
a character recognition subunit 512, configured to perform character recognition on the target page image;
a position determining subunit 513, configured to determine, by using the image coordinates and a preset rule, a position of the stem area in the target page image;
an item number obtaining subunit 514, which obtains a target item number of the item stem area and auxiliary item numbers of N item stems below the item stem area, where N > 1;
a rule determining subunit 515, configured to determine a naming rule corresponding to the target question number and the auxiliary question number;
a category judgment subunit 516, configured to, if the number of the first naming rules is greater than the number of the second naming rules, determine that the category of the question stem area is a subtotal; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
As an optional implementation manner, the first determining unit 520 may include:
the boundary question number acquiring subunit 521 is configured to acquire a boundary question number in the target page image, where the boundary question number is the same as the naming rule of the target question number, the boundary question number is located below the target question number, and no other question number having the same naming rule as the target question number exists between the boundary question number and the target question number;
a first area determining subunit 522, configured to use the question stem area and an area between the question stem area and the limit question mark as selection areas.
As an alternative implementation, the second determining unit 530 may include:
a feature extraction subunit 531, configured to extract topic features and keywords of the topic stem region;
the question type identification subunit 532 is configured to select a large number of question samples, obtain question features and keywords of the question samples, and use question types corresponding to the question samples as tags; training an initial question type recognition model by using the question sample to obtain a trained question type recognition model; inputting the question features and the keywords of the question stem area into the question type identification model to obtain a question type corresponding to the question stem area;
a second area determining subunit 533, configured to, when the question type of the question stem area is a choice question, use the question stem area and an option area corresponding to the question stem area as a choice area;
the third area determination subunit 534 is configured to, when the question type of the question stem area is a non-selection question, take the question stem area as a selection area.
The question selecting device shown in fig. 4 can select a question or a question in a point contact manner, does not need a user to manually cut a picture of a content to be collected, is simple and convenient to operate, improves the question selecting efficiency, improves the user experience, and simultaneously ensures that a question stem and a choice are simultaneously selected for a single-channel question, thereby improving the accuracy of a frame question.
EXAMPLE five
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. The electronic equipment can be a learning machine, a family education machine, a point reading machine, a tablet personal computer or an intelligent terminal such as a mobile phone. As shown in fig. 5, the electronic device 600 may include:
a memory 610 storing executable program code;
a processor 620 coupled to the memory 610;
the processor 620 calls the executable program code stored in the memory 610 to execute part or all of the steps of the method for selecting a topic according to any one of the first to second embodiments.
EXAMPLE six
Referring to fig. 6, fig. 6 is a schematic structural diagram of a topic selection system according to an embodiment of the present invention. As shown in fig. 6, the system 700 includes an intelligent terminal 710, which may be a learning machine, a family education machine, a point-to-read machine, a tablet computer, or a mobile phone, and a server 720. Wherein:
the smart terminal 710 may include: a memory 711 in which executable program codes are stored; a processor 712 coupled with a memory 711; the processor 712 calls the executable program code stored in the memory 711 to execute the steps executed by the intelligent terminal in the topic selection method according to the second embodiment.
The server 720 may include: a memory 721 in which executable program code is stored; a processor 722 coupled with a memory 721; the processor 722 calls the executable program code stored in the memory 721 to execute the steps executed by the server in the topic selection method according to the second embodiment.
The embodiment of the invention discloses a computer-readable storage medium which stores a computer program, wherein the computer program enables a computer to execute part or all of the steps in the method for selecting topics in any one of the first embodiment to the second embodiment.
The embodiment of the invention also discloses a computer program product, wherein when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the method for selecting the topics in any one of the first embodiment to the second embodiment.
The embodiment of the invention also discloses an application publishing platform, wherein the application publishing platform is used for publishing the computer program product, and when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the method for selecting any one of the topics in the first embodiment to the second embodiment.
In various embodiments of the present invention, it should be understood that the sequence numbers of the processes do not mean the execution sequence necessarily in order, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present invention, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the method according to the embodiments of the present invention.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a from which B can be determined. It should also be understood, however, that determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information.
Those skilled in the art will appreciate that some or all of the steps of the methods of the embodiments may be implemented by hardware instructions of a program, which may be stored in a computer-readable storage medium, such as Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (CD-ROM), or other disk Memory, or other Memory, or may be stored in a computer-readable storage medium, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
The method, the device, the electronic device and the storage medium for selecting the topics disclosed by the embodiments of the present invention are described in detail above, a specific example is applied in the present document to explain the principle and the implementation of the present invention, and the description of the above embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (14)
1. A question selecting method is applied to an intelligent terminal and is characterized by comprising the following steps:
when a trigger instruction is received, acquiring a target page image and an image coordinate of an operation body in the target page image;
determining a question stem area by using the image coordinates and a target page image, and judging the category of the question stem area;
when the category is a big question, selecting all the small questions under the big question as a selection area;
when the category is a question, obtaining a question type of the question stem area, and determining a selection area according to the question type;
and selecting the theme pictures in the selection area.
2. The method of claim 1, wherein obtaining a target page image and image coordinates of an operator in the target page image when a trigger instruction is received comprises:
receiving an instruction sent by a user and judging whether the instruction is a trigger instruction or not;
when a trigger instruction is received, starting a camera, and shooting the current page of the carrier to obtain the target page image;
and carrying out coordinate transformation on the position coordinate of the operating body on the bearing body to obtain an image coordinate of the position coordinate corresponding to the target page image.
3. The method of claim 1, wherein determining the stem area by using the image coordinates and the target page image, and determining the type of the stem area comprises:
performing character recognition on the target page image;
determining the position of the question stem area in the target page image by using the image coordinates and a preset rule;
acquiring a target question number of the question stem area and auxiliary question numbers of N question stems below the question stem area, wherein N is more than 1;
determining naming rules corresponding to the target question numbers and the auxiliary question numbers;
if the number of the first naming rules is larger than that of the second naming rules, the category of the question stem area is a subtopic; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
4. The method of claim 3, wherein selecting all the topics under the topic as selection areas comprises:
acquiring a boundary question number in the target page image, wherein the boundary question number is the same as the naming rule of the target question number, the boundary question number is positioned below the target question number, and no other question number with the same naming rule as the target question number exists between the boundary question number and the target question number;
and taking the question stem area and the area between the question stem area and the limit question mark as selection areas.
5. The method of claim 1, wherein obtaining the question stem area question mark and determining the selection area according to the question mark comprises:
extracting the question features and key words of the question stem region;
judging question types corresponding to the question stem areas based on the question features and the keywords;
when the question type of the question stem area is a choice question, taking the question stem area and an option area corresponding to the question stem area as a choice area;
and when the question type of the question stem area is a non-selection question, taking the question stem area as a selection area.
6. The method of claim 5, wherein determining the question type corresponding to the question stem area based on the question features and the keywords comprises:
selecting a large number of topic samples, obtaining the topic characteristics and keywords of the topic samples, and taking the topic types corresponding to the topic samples as labels;
training an initial question type recognition model by using the question sample to obtain a trained question type recognition model;
and inputting the question features and the keywords of the question stem area into the question type identification model to obtain the question type corresponding to the question stem area.
7. The utility model provides a device that topic was selected, is applied to intelligent terminal, its characterized in that, the device includes:
the acquisition unit is used for acquiring a target page image and image coordinates of an operation body in the target page image when a trigger instruction is received;
the judging unit is used for determining a question stem area by utilizing the image coordinates and the target page image and judging the category of the question stem area;
the first determining unit is used for selecting all the small questions under the big questions as selection areas when the categories are the big questions;
the second determining unit is used for acquiring the question types of the question stem areas when the categories are the subjects and determining the selection areas according to the question types;
and the selecting unit is used for selecting the theme pictures in the selection area.
8. The apparatus according to claim 7, wherein the determining unit comprises:
the character recognition subunit is used for carrying out character recognition on the target page image;
the position determining subunit is used for determining the position of the question stem area in the target page image by using the image coordinates and a preset rule;
the question mark acquisition subunit is used for acquiring a target question mark of the question stem area and auxiliary question marks of N question stems below the question stem area, wherein N is more than 1;
the rule determining subunit is used for determining a naming rule corresponding to the target question number and the auxiliary question number;
the category judgment subunit is configured to, if the number of the first naming rules is greater than the number of the second naming rules, determine that the category of the question stem area is a subtotal; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
9. A method for selecting questions is applied to an intelligent terminal and a server, and is characterized by comprising the following steps:
when the intelligent terminal receives a trigger instruction, acquiring a target page image and sending the target page image to a server;
the server determines the image coordinates of the operation body in the target page image, determines a question stem area by using the image coordinates and the target page image, and judges the category of the question stem area;
when the category is a big question, the server selects all the small questions under the big question as a selection area;
when the category is a question, the server acquires the question type of the question stem area and determines a selection area according to the question type;
and the server selects the topic picture in the selected area.
10. The method of claim 9, wherein the server determines image coordinates of the operator in the target page image, determines a stem area using the image coordinates and the target page image, and determines a category of the stem area, comprising:
the server obtains image coordinates of the position coordinates corresponding to the target page image through coordinate transformation of the position coordinates of the operating body on the bearing body;
the server carries out character recognition on the target page image;
the server determines the position of the question stem area in the target page image by using the image coordinates and a preset rule;
the server obtains a target question number of the question stem area and auxiliary question numbers of N question stems below the question stem area, wherein N is more than 1;
the server determines a naming rule corresponding to the target question number and the auxiliary question number;
if the number of the first naming rules is larger than that of the second naming rules, the server judges the type of the question stem area as a subtopic; if the number of the first naming rules is smaller than or equal to the number of the second naming rules, the server judges the type of the question stem area to be a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
11. The utility model provides a device that topic was selected, is applied to in intelligent terminal and the server, its characterized in that: the device comprises:
the acquisition unit is positioned in the intelligent terminal and used for acquiring a target page image and sending the target page image to the server when a trigger instruction is received;
the judging unit is positioned in the server and used for determining the image coordinates of the operation body in the target page image, determining the question stem area by using the image coordinates and the target page image and judging the category of the question stem area;
the first determining unit is positioned in the server and used for selecting all the small questions under the big questions as a selection area when the category is the big questions;
the second determining unit is positioned in the server and used for acquiring the question types of the question stem areas when the categories are the questions and determining the selection areas according to the question types;
and the selecting unit is positioned in the server and used for selecting the theme pictures in the selected area.
12. The apparatus according to claim 11, wherein the determining unit comprises:
the coordinate conversion subunit is used for converting the position coordinates of the operating body on the bearing body through coordinates to obtain image coordinates of the position coordinates corresponding to the target page image;
the character recognition subunit is used for carrying out character recognition on the target page image;
the position determining subunit is used for determining the position of the question stem area in the target page image by using the image coordinates and a preset rule;
the question mark acquisition subunit is used for acquiring a target question mark of the question stem area and auxiliary question marks of N question stems below the question stem area, wherein N is more than 1;
the rule determining subunit is used for determining a naming rule corresponding to the target question number and the auxiliary question number;
the category judgment subunit is configured to, if the number of the first naming rules is greater than the number of the second naming rules, determine that the category of the question stem area is a subtotal; if the number of the first naming rules is less than or equal to the number of the second naming rules, the category of the question stem area is a big question; the first naming rule is a naming rule corresponding to a target question mark, the second naming rule is a naming rule different from the naming rule corresponding to the target question mark in the auxiliary question mark, and the sum of the number of the first naming rules and the number of the second naming rules is N + 1.
13. An electronic device, comprising: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory to perform a method of topic selection as claimed in any one of claims 1 to 6.
14. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute a method of topic selection according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010366582.0A CN111553365B (en) | 2020-04-30 | 2020-04-30 | Question selection method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010366582.0A CN111553365B (en) | 2020-04-30 | 2020-04-30 | Question selection method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111553365A true CN111553365A (en) | 2020-08-18 |
| CN111553365B CN111553365B (en) | 2023-11-24 |
Family
ID=72003355
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010366582.0A Active CN111553365B (en) | 2020-04-30 | 2020-04-30 | Question selection method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111553365B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270295A (en) * | 2020-11-13 | 2021-01-26 | 广东小天才科技有限公司 | Question framing method and device under student homework scene, terminal equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003255816A (en) * | 2002-02-28 | 2003-09-10 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for displaying examination picture, program for executing the method, and recording medium with the execution program recorded thereon |
| US20130195361A1 (en) * | 2012-01-17 | 2013-08-01 | Alibaba Group Holding Limited | Image index generation based on similarities of image features |
| CN108133167A (en) * | 2016-12-01 | 2018-06-08 | 北京新唐思创教育科技有限公司 | A kind of automatic judging method and its device selected with judging topic answer |
| CN108304562A (en) * | 2018-02-08 | 2018-07-20 | 广东小天才科技有限公司 | Question searching method and device and intelligent terminal |
| CN110427412A (en) * | 2019-06-27 | 2019-11-08 | 深圳中兴网信科技有限公司 | Topic read method, device, topic input device and computer storage medium |
| CN110765995A (en) * | 2019-10-11 | 2020-02-07 | 深圳市鹰硕技术有限公司 | Answer sheet generation method, answer sheet identification device and storage medium |
| CN110837833A (en) * | 2019-11-14 | 2020-02-25 | 广东小天才科技有限公司 | Question selection method and device, terminal equipment and readable storage medium |
-
2020
- 2020-04-30 CN CN202010366582.0A patent/CN111553365B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003255816A (en) * | 2002-02-28 | 2003-09-10 | Nippon Telegr & Teleph Corp <Ntt> | Method and device for displaying examination picture, program for executing the method, and recording medium with the execution program recorded thereon |
| US20130195361A1 (en) * | 2012-01-17 | 2013-08-01 | Alibaba Group Holding Limited | Image index generation based on similarities of image features |
| CN108133167A (en) * | 2016-12-01 | 2018-06-08 | 北京新唐思创教育科技有限公司 | A kind of automatic judging method and its device selected with judging topic answer |
| CN108304562A (en) * | 2018-02-08 | 2018-07-20 | 广东小天才科技有限公司 | Question searching method and device and intelligent terminal |
| CN110427412A (en) * | 2019-06-27 | 2019-11-08 | 深圳中兴网信科技有限公司 | Topic read method, device, topic input device and computer storage medium |
| CN110765995A (en) * | 2019-10-11 | 2020-02-07 | 深圳市鹰硕技术有限公司 | Answer sheet generation method, answer sheet identification device and storage medium |
| CN110837833A (en) * | 2019-11-14 | 2020-02-25 | 广东小天才科技有限公司 | Question selection method and device, terminal equipment and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 孟祥丽等: "纸质问卷版面设计与自动识别系统研究", vol. 37, no. 8, pages 32 - 35 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270295A (en) * | 2020-11-13 | 2021-01-26 | 广东小天才科技有限公司 | Question framing method and device under student homework scene, terminal equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111553365B (en) | 2023-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111753767B (en) | Method and device for automatically correcting operation, electronic equipment and storage medium | |
| CN111711757B (en) | Test question shooting method and device capable of preventing finger from being blocked, electronic equipment and storage medium | |
| CN107679070B (en) | Intelligent reading recommendation method and device and electronic equipment | |
| CN111753120B (en) | Question searching method and device, electronic equipment and storage medium | |
| CN102750552B (en) | Handwriting recognition method and system as well as handwriting recognition terminal | |
| CN108121987B (en) | Information processing method and electronic equipment | |
| CN111581367A (en) | Method and system for inputting questions | |
| CN109783613B (en) | Question searching method and system | |
| CN111984589A (en) | Document processing method, document processing device and electronic equipment | |
| CN108256523A (en) | Recognition methods, device and computer readable storage medium based on mobile terminal | |
| CN111680177A (en) | Data searching method, electronic device and computer-readable storage medium | |
| CN115509413A (en) | Display method, display device, dictionary pen, electronic device and storage medium | |
| CN112149680A (en) | Wrong word detection and identification method and device, electronic equipment and storage medium | |
| CN111753168A (en) | Method and device for searching questions, electronic equipment and storage medium | |
| US6567548B2 (en) | Handwriting recognition system and method using compound characters for improved recognition accuracy | |
| CN111553365A (en) | Method and device for selecting questions, electronic equipment and storage medium | |
| CN103093217A (en) | Interactive image and character recognition method and device | |
| CN111079489B (en) | Content identification method and electronic equipment | |
| CN112163513A (en) | Information selection method, system, device, electronic equipment and storage medium | |
| CN111091034A (en) | Multi-finger recognition-based question searching method and family education equipment | |
| CN111582281B (en) | Picture display optimization method and device, electronic equipment and storage medium | |
| CN111711758B (en) | Multi-pointing test question shooting method and device, electronic equipment and storage medium | |
| CN111563497B (en) | Frame question method and device based on moving track, electronic equipment and storage medium | |
| CN112560728B (en) | Target object identification method and device | |
| CN113420983A (en) | Writing evaluation method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |