CN109189895A - A kind of topic for verbal exercise corrects method and device - Google Patents
A kind of topic for verbal exercise corrects method and device Download PDFInfo
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- CN109189895A CN109189895A CN201811125659.4A CN201811125659A CN109189895A CN 109189895 A CN109189895 A CN 109189895A CN 201811125659 A CN201811125659 A CN 201811125659A CN 109189895 A CN109189895 A CN 109189895A
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- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
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Abstract
The present invention provides a kind of topics for verbal exercise to correct method and device, the feature vector of topic to be searched is obtained according to the word content of the stem of each topic to be searched first, then the target paper to match with paper to be searched is searched from exam pool using the feature vector of each topic to be searched, it is the topic to be searched of verbal exercise for topic types, feature vector inside target paper based on topic carries out quadratic search, the standard of lookup is that most short editing distance is minimum, if the topic types for the target topic being matched to are also verbal exercise, then confirm entitled mental arithmetic topic to be changed to be searched, it recycles preset mental arithmetic engine to calculate verbal exercise mesh to be changed and exports calculated result as verbal exercise purpose answer to be changed.The accuracy that verbal exercise is corrected can be improved using scheme provided by the invention.
Description
Technical field
The present invention relates to field of artificial intelligence more particularly to a kind of topic for verbal exercise correct method, apparatus,
Electronic equipment and computer readable storage medium.
Background technique
With the continuous propulsion of computer technology and IT application in education sector, computer technology is gradually applied to daily religion
It educates in teaching Activities, such as has obtained corresponding application under teaching assessment scene.Domestic existing basic education, student
The main investigation form of study condition is still various types of examinations or test, and in this situation, teacher bears very big batch
Change the operating pressure of paper.
Currently, intelligent terminal class product is there are many solving to correct students' papers to inscribe APP with searching for paper, it will include paper to be changed
Image input search topic APP, to search topic APP according to the presentation content of paper from being searched in the image with paper in exam pool
The corresponding topic of each topic.
Existing topic searching method can generate the feature vector of topic, root according to the word content of the stem of topic
It is scanned for from exam pool according to this feature vector.When generating feature vector, different text (token) is based on produced by word frequency
Weighted, occur more frequently showing that the text (token) is more inessential (as " " word is being inscribed in the word content of stem
There are many frequency of occurrence in dry, then assert " " word is inessential), then the weight of the text (token) is arranged lower.
However, the word content of the stem of verbal exercise is mostly number and calculates symbol, and digital for verbal exercise
It is relatively high with the word frequency of calculating symbol, i.e., lack the text with the high weight of discrimination in the word content of the stem of verbal exercise
Word (token), it is smaller that this will lead to the discrimination between feature vector corresponding to different verbal exercises, once identification is drawn
It holds up and small identification mistake occurs, will lead to verbal exercise and be matched to another different verbal exercise, and then lead to topic of doing a sum orally
Correct error.As it can be seen that the topic for being directed to verbal exercise is corrected and is easy to appear mistake, accuracy is not high.
Summary of the invention
The purpose of the present invention is to provide a kind of topics for verbal exercise to correct method, apparatus, electronic equipment and calculating
Machine readable storage medium storing program for executing carries out correcting easy error in a manner of solving existing topic and correct for verbal exercise, and accuracy is not high
The problem of.
In order to solve the above technical problems, the present invention provides a kind of topics for verbal exercise to correct method, the method
Include:
Step S11: detecting the image of paper to be searched, detects each to be searched on the paper to be searched
Destination region is inscribed, determines the topic types of each topic to be searched, and identifies stem in each topic destination region to be searched
Word content；
Step S12: according to the word content of the stem of each topic to be searched, obtain the feature of the topic to be searched to
Amount, and scanned in exam pool according to the feature vector of the topic to be searched, search the immediate topic of topic to be searched；
Step S13: summarize all topics to be searched found closest to the paper where topic, default item will be met
The paper of part is determined as and the matched target paper of paper to be searched；
Step S14: in the paper to be searched comprising topic types be verbal exercise topic to be searched in the case where, needle
It is the topic to be searched of verbal exercise to each topic types, it will be in the feature vector of the topic to be searched and the target paper
The feature vector of each topic carries out most short editing distance matching, determines in the target paper and matches with the topic to be searched
Target topic, if the topic types of the target topic be verbal exercise, it is determined that the entitled verbal exercise to be changed to be searched
Mesh；
Step S15: be directed to each mental arithmetic topic to be changed, using preset mental arithmetic engine to the mental arithmetic topic to be changed into
Row calculates, and exports the calculated result of the mental arithmetic engine as the verbal exercise purpose answer to be changed, completes to described to be searched
Verbal exercise purpose to be changed is corrected on paper.
It optionally, is verbal exercise in the topic types of the target topic, and the target topic exists in step S14
In the identical situation in position in the paper to be searched, determining should for position in the target paper and the topic to be searched
Entitled mental arithmetic topic to be changed to be searched.
Optionally, in the case where the target paper for meeting preset condition is not present in step S13, in the paper to be searched
In comprising topic types be verbal exercise topic to be searched when, by topic types be verbal exercise topic to be searched be determined as it is pending
Arithmetic problem mesh of withdrawing the previous remark counts the mental arithmetic topic to be changed using preset mental arithmetic engine for each mental arithmetic topic to be changed
It calculates and exports the verbal exercise purpose calculated result to be changed as the verbal exercise purpose answer to be changed, complete to described to be searched
Verbal exercise purpose to be changed is corrected on paper.
Optionally, step S15 further include: the calculated result for examining the mental arithmetic engine and the mental arithmetic topic to be changed are in institute
State whether corresponding Key for Reference on target paper is consistent, the calculated result that the mental arithmetic engine is exported if consistent, which is used as, is somebody's turn to do
Verbal exercise purpose answer to be changed.
Optionally, when the calculated result of the mental arithmetic engine and the ginseng of the mental arithmetic topic to be changed on the target paper
Examine answer it is inconsistent when, export for indicating the inconsistent prompt information of the verbal exercise purpose Key for Reference to be changed, with prompt
The paper person of correcting pays attention to the mental arithmetic topic to be changed.
Optionally, the preset mental arithmetic engine includes the first identification model trained in advance, first identification model
It is model neural network based；
The mental arithmetic topic to be changed is calculated using preset mental arithmetic engine in step S15, comprising:
By it is described in advance training the first identification model identify this it is to be changed mental arithmetic topic in number, letter, text
Word, character and calculating type, the calculating type include: four fundamental rules hybrid operation, estimation, band remainder division, score calculating, list
Position conversion, vertical calculating, de- formula calculate；
It according to the number identified, letter, text, character and calculates type and is calculated, obtain this and pending withdraw the previous remark
Arithmetic problem purpose calculated result.
Optionally, the step S12 further comprises:
Step S121, by the word content input of the stem of each topic to be searched stem vectorization model trained in advance
In, the feature vector of the stem of each topic to be searched is obtained, the feature vector as each topic to be searched, wherein described
Stem vectorization model is model neural network based；
Step S122 is scanned in exam pool for each topic to be searched, searches the feature with the topic to be searched
The corresponding topic of the feature vector to match in exam pool is determined as with the topic to be searched most by the feature vector that vector matches
Close topic.
Optionally, the stem vectorization model is obtained by following steps training:
It concentrates each topic sample to be labeled processing the first topic sample training, marks out and inscribed in each topic sample
Dry word content；
Two-dimensional feature vector extraction is carried out using word content of the neural network model to stem in each topic sample, from
And training obtains the stem vectorization model.
Optionally, index information table is established to the feature vector of each topic on paper in exam pool in advance；
Step S122 further comprises:
For each topic to be searched, the feature vector phase with the topic to be searched is searched in the index information table
The feature vector matched；
By the feature vector to match, corresponding topic is determined as with the topic to be searched most in the index information table
Close topic.
Optionally, before establishing the index information table, the feature vector of different length is grouped according to length；
It is described to be directed to each topic to be searched, the feature vector with the topic to be searched is searched in the index information table
The feature vector to match, comprising:
It is identical as the feature vector length of the topic to be searched in the index information table for each topic to be searched
Or in similar grouping, the feature vector to match with the feature vector of the topic to be searched is searched.
Optionally, the paper for meeting preset condition is determined as trying with the matched target of paper to be searched by step S13
Volume, comprising:
The maximum and paper greater than the first preset threshold of the frequency of occurrences is determined as and the matched mesh of paper to be searched
Mark paper.
Optionally, step S11 detects the image of paper to be searched, detects each on the paper to be searched
Topic destination region to be searched, comprising:
The image of the paper to be searched is detected using preparatory trained detection model, is detected described wait search
Each topic destination region to be searched on rope paper, wherein the detection model is model neural network based.
Optionally, step S11 identifies the word content of stem in each topic destination region to be searched, comprising:
The word content of stem in each topic destination region to be searched is identified using preparatory trained second identification model,
Wherein, second identification model is model neural network based.
In order to achieve the above objectives, the present invention also provides a kind of topics for verbal exercise to correct device, described device packet
It includes:
Identification module is detected, detects, is detected on the paper to be searched for the image to paper to be searched
Each topic destination region to be searched, determines the topic types of each topic to be searched, and identifies the area of each topic to be searched
The word content of stem in domain；
Topic searching module obtains the topic to be searched for the word content according to the stem of each topic to be searched
Feature vector, and scanned in exam pool according to the feature vector of the topic to be searched, search the topic to be searched and most connect
Close topic；
Paper determining module, for summarize all topics to be searched found closest to the paper where topic, will
The paper for meeting preset condition is determined as and the matched target paper of paper to be searched；
Verbal exercise determining module, for being the topic to be searched of verbal exercise comprising topic types in the paper to be searched
In the case where, for each topic types be verbal exercise topic to be searched, by the feature vector of the topic to be searched with it is described
The feature vector of each topic in target paper carries out most short editing distance matching, determine in the target paper with this wait search
The target topic that rope topic matches, if the topic types of the target topic are verbal exercise, it is determined that this is to be searched entitled
Mental arithmetic topic to be changed；
Verbal exercise corrects module, pending to this using preset mental arithmetic engine for being directed to each mental arithmetic topic to be changed
Arithmetic problem mesh of withdrawing the previous remark is calculated, and is exported the calculated result of the mental arithmetic engine as the verbal exercise purpose answer to be changed, is completed
Verbal exercise purpose to be changed on the paper to be searched is corrected.
Optionally, the verbal exercise determining module is also used in the topic types of the target topic be verbal exercise, and institute
It is identical with position of the topic to be searched in the paper to be searched in the position in the target paper to state target topic
In the case of, determine the entitled mental arithmetic topic to be changed to be searched.
Optionally, the paper determining module is also used to there is no the target paper for meeting preset condition,
It is verbal exercise wait search by topic types when including to be searched topic of the topic types for verbal exercise in the paper to be searched
Rope topic is determined as mental arithmetic topic to be changed, pending to this using preset mental arithmetic engine for each mental arithmetic topic to be changed
Arithmetic problem mesh of withdrawing the previous remark is calculated and exports the verbal exercise purpose calculated result to be changed as the verbal exercise purpose answer to be changed,
Verbal exercise purpose to be changed on the paper to be searched is corrected in completion.
Optionally, the verbal exercise corrects module, is also used to examine the calculated result of the mental arithmetic engine to be changed with this
Whether topic corresponding Key for Reference on the target paper of doing a sum orally is consistent, and the meter of the mental arithmetic engine is exported if consistent
Result is calculated as the verbal exercise purpose answer to be changed.
Optionally, the verbal exercise corrects module, is also used to pending withdraw the previous remark when the calculated result of the mental arithmetic engine with this
For arithmetic problem mesh when the Key for Reference on the target paper is inconsistent, output is for indicating that verbal exercise purpose reference to be changed is answered
The inconsistent prompt information of case, to prompt the paper person of correcting to pay attention to the mental arithmetic topic to be changed.
Optionally, the preset mental arithmetic engine includes the first identification model trained in advance, first identification model
It is model neural network based；
The verbal exercise corrects module, identifies that this is pending specifically for the first identification model by the training in advance
Withdraw the previous remark and the number in arithmetic problem mesh, letter, text, character and calculate type, the calculating type include: four fundamental rules hybrid operation,
Estimation, band remainder division, score calculating, unit conversion, vertical calculating, de- formula calculate；According to identified number, letter,
Text, character and calculating type are calculated, and the verbal exercise purpose calculated result to be changed is obtained.
Optionally, the topic searching module, comprising:
Feature vector obtaining unit, the topic trained in advance for the word content input by the stem of each topic to be searched
In dry vectorization model, obtain the feature vector of the stem of each topic to be searched, as each topic to be searched feature to
Amount, wherein the stem vectorization model is model neural network based；
Topic searching unit is scanned in exam pool, is searched and the topic to be searched for being directed to each topic to be searched
The corresponding topic of the feature vector to match in exam pool is determined as and is somebody's turn to do wait search by the feature vector that purpose feature vector matches
The immediate topic of rope topic.
Optionally, the stem vectorization model is obtained by following steps training:
It concentrates each topic sample to be labeled processing the first topic sample training, marks out and inscribed in each topic sample
Dry word content；
Two-dimensional feature vector extraction is carried out using word content of the neural network model to stem in each topic sample, from
And training obtains the stem vectorization model.
Optionally, described device further include:
Preprocessing module establishes index information table for the feature vector in advance to each topic on paper in exam pool；
The topic searching unit, be specifically used for be directed to each topic to be searched, in the index information table search with
The feature vector that the feature vector of the topic to be searched matches；The feature vector to match is right in the index information table
The topic answered is determined as and the immediate topic of topic to be searched.
Optionally, the preprocessing module is also used to before establishing the index information table, by the feature of different length
Vector is grouped according to length；
The topic searching unit is specifically used for being directed to each topic to be searched, in the index information table with should be to
In the same or similar grouping of the feature vector length of search topic, what the feature vector of lookup and the topic to be searched matched
Feature vector.
Optionally, the paper determining module, specifically for by frequency of occurrences maximum and greater than the examination of the first preset threshold
Volume is determined as and the matched target paper of paper to be searched.
Optionally, the detection identification module is specifically used for using preparatory trained detection model to described to be searched
The image of paper is detected, and detects each topic destination region to be searched on the paper to be searched, wherein the detection
Model is model neural network based.
Optionally, the detection identification module is specifically used for identifying using trained second identification model in advance each
The word content of stem in topic destination region to be searched, wherein second identification model is model neural network based.
In order to achieve the above objectives, the present invention also provides a kind of electronic equipment, including processor, communication interface, memory
And communication bus, wherein processor, communication interface, memory complete mutual communication by communication bus；
Memory, for storing computer program；
Processor when for executing the program stored on memory, realizes as above any topic for being directed to verbal exercise
Mesh corrects the method and step of method.
In order to achieve the above objectives, the present invention also provides a kind of computer readable storage mediums, which is characterized in that the meter
It is stored with computer program in calculation machine readable storage medium storing program for executing, as above any institute is realized when the computer program is executed by processor
State the method and step that method is corrected for the topic of verbal exercise.
Compared with prior art, the present invention is directed to paper to be searched, first according to the text of the stem of each topic to be searched
Word content obtains the feature vector of topic to be searched, then using each topic to be searched feature vector is searched from exam pool and
The target paper that paper to be searched matches, and for topic types it is the topic to be searched of verbal exercise, inside target paper
Feature vector based on topic carries out quadratic search, and the standard of lookup is that most short editing distance is minimum, if the target topic being matched to
Purpose topic types are also verbal exercise, then confirm entitled mental arithmetic topic to be changed to be searched, recycle preset mental arithmetic engine
Verbal exercise mesh to be changed is calculated and exports calculated result as verbal exercise purpose answer to be changed.As it can be seen that for pending
It withdraws the previous remark arithmetic problem mesh, since discrimination is smaller to each other for the feature vector that is obtained according to the word content of stem, causes from exam pool
Key for Reference and the unmatched possibility of mental arithmetic topic to be changed in the target paper of lookup is also larger, therefore quadratic search is true
Fixed mental arithmetic topic to be changed is simultaneously calculated by engine of doing a sum orally, and verbal exercise purpose can be improved and correct accuracy.
Detailed description of the invention
Fig. 1 is the flow diagram that the topic for verbal exercise that one embodiment of the invention provides corrects method；
Fig. 2 is the structural schematic diagram that the topic for verbal exercise that one embodiment of the invention topic supplies corrects device；
Fig. 3 is the structural schematic diagram for the electronic equipment that one embodiment of the invention provides.
Specific embodiment
Below in conjunction with the drawings and specific embodiments to a kind of topic for verbal exercise proposed by the present invention correct method and
Device is described in further detail.According to claims and following explanation, advantages and features of the invention will be become apparent from.
For solve problem of the prior art, the embodiment of the invention provides a kind of topic for verbal exercise correct method,
Device, electronic equipment and computer readable storage medium.
It should be noted that the topic method of correcting for verbal exercise of the embodiment of the present invention can be applied to implementation of the present invention
The topic for verbal exercise of example corrects device, and should correct device for the topic of verbal exercise can be configured on electronic equipment.
Wherein, which can be personal computer, mobile terminal etc., which can be the tool such as mobile phone, tablet computer
There is the hardware device of various operating systems.
Fig. 1 is the flow diagram that a kind of topic for verbal exercise that one embodiment of the invention provides corrects method.Please
With reference to Fig. 1, a kind of topic method of correcting for verbal exercise be may include steps of:
Step S11: detecting the image of paper to be searched, detects each topic to be searched on paper to be searched
Region, determine the topic types of each topic to be searched, and identify it is each it is to be searched topic destination region in stem text
Content.
The image of paper to be searched can be the image comprising paper to be searched.Specifically, can use detection model pair
The image of paper to be searched is detected, and detects each topic destination region to be searched on paper to be searched, the detection mould
Type is model neural network based.Wherein, detection model for example can be based on depth convolutional neural networks
What the sample that (Convolutional Neural Networks, CNN) concentrates paper sample training was trained.Benefit
Two-dimensional feature vector is extracted from the image of paper to be searched with trained detection model, in each net of two-dimensional feature vector
Lattice generate anchor point of different shapes, each topic to be searched that be will test out using callout box (Groundtruth Boxes)
Region is labeled, and the anchor point of callout box and generation can also be returned to (regression) processing, so that callout box is more pasted
The physical location of nearly topic.Per pass topic to be searched can be carried out to be cut into single image after having identified title field, or not
Practical cutting, and each title field to be searched is distinguished in processing and is handled for single area image, it can be according to topic
Mesh location information is ranked up.
After detecting each topic destination region to be searched, it can use Classification and Identification model and determine each topic to be searched
Topic types, the Classification and Identification model are models neural network based.Wherein, Classification and Identification model, which for example can be, is based on
What the sample that depth convolutional neural networks concentrate paper sample training was trained, the topic in each sample marks
There are topic types.Topic types can be divided into operation questions, verbal exercise, gap-filling questions, multiple-choice question, using topic etc..
At the same time it can also identify the word content of stem in topic destination region to be searched, institute using the second identification model
Stating the second identification model is model neural network based.Each component part in topic to be searched is marked out first, is formed
Part may include stem, answer and/or picture, and then be identified in topic in the text of stem by the second identification model
Hold.Wherein, the second identification model can be based on the foundation of empty convolution sum attention model, specifically, using empty convolution
Feature extraction, then the characteristic solution that will be extracted by attention model are carried out to stem, answer and/or the corresponding callout box of picture
Code is at character.
Step S12: according to the word content of the stem of each topic to be searched, obtain the feature of the topic to be searched to
Amount, and scanned in exam pool according to the feature vector of the topic to be searched, search the immediate topic of topic to be searched.
Specifically, the step S12 can further include:
Step S121, by the word content input of the stem of each topic to be searched stem vectorization model trained in advance
In, the feature vector of the stem of each topic to be searched is obtained, the feature vector as each topic to be searched, wherein described
Stem vectorization model is model neural network based.
For example, such as in topic to be searched the word content of stem is that " 4. Xiao Ming, which walk, just arrives whole one for 3 minutes
Half, is his family apart from school's how much rice? (6 points) ", by word content input stem vectorization model-trained in advance
In sent2vec model, the feature vector of the stem is obtained, feature vector can be expressed as [x0, x1, x2 ... .xn].
Wherein, the stem vectorization model can be model neural network based, such as CNN model, the stem to
Quantitative model can be obtained by following steps training: concentrate each topic sample to be labeled place the first topic sample training
Reason, marks out the word content of stem in each topic sample；Using neural network model to stem in each topic sample
Word content carries out two-dimensional feature vector extraction, so that training obtains the stem vectorization model.Wherein, it specifically trained
Journey belongs to the prior art, and this will not be repeated here.
Step S122 is scanned in exam pool for each topic to be searched, searches the feature with the topic to be searched
The corresponding topic of the feature vector to match in exam pool is determined as with the topic to be searched most by the feature vector that vector matches
Close topic.
Wherein it is possible to search the feature vector with the topic to be searched in exam pool in such a way that vector approximation is searched for
The feature vector to match, searched specially in exam pool with the feature vector of the topic to be searched apart from nearest feature to
Amount.It is understood that the side that the similarity measurement (Similarity Measurement) between different vectors generallys use
Method is exactly " distance (Distance) " between calculating vector, is commonly had apart from calculation: Euclidean distance, manhatton distance,
Included angle cosine (Cosine) etc..The calculation used in the present embodiment is included angle cosine.
It preferably, is the lookup convenient for feature vector, feature that can also in advance to each topic on paper in exam pool
Vector establishes index information table.Can store in index information table the feature vector of each topic in exam pool, topic it is specific in
The ID etc. of paper where appearance and topic.
Correspondingly, step S122 can further include: being directed to each topic to be searched, looked into the index information table
Look for the feature vector to match with the feature vector of the topic to be searched；By the feature vector to match in the index information table
In corresponding topic be determined as and the immediate topic of topic to be searched.
It is understood that after finding the feature vector to match in index information table, in the index information table
In find immediate topic, can obtain at this time closest to topic particular content (stem, answer including topic and/or
Picture) and the id information closest to paper where topic.
Preferably, before establishing the index information table, can also by the feature vector of different length according to length into
Row grouping, in this way, when searching the feature vector to match with the feature vector of the topic to be searched in the index information table,
Point same or similar with the length of the feature vector of the topic to be searched can be navigated in the index information table first
Group, and then in the index information table in grouping identical with the feature vector length of the topic to be searched, search with should be to
The feature vector that the feature vector of search topic matches.Wherein, the identical feature vector of length can be divided into one when grouping
Feature vector of the length within the scope of some can also be divided into one group by group, and which is not limited by the present invention.As it can be seen that by different
The feature vector of length is grouped according to length, can make the later period search for topic when according to the length of feature vector in respective packets
It is inside inquired, improves topic search speed.It is understood that the length of feature vector is not both the text number because of stem
Caused by amount is different.
Step S13: summarize all topics to be searched found closest to the paper where topic, default item will be met
The paper of part is determined as and the matched target paper of paper to be searched.
Wherein, by the paper for meeting preset condition be determined as with the matched target paper of paper to be searched, specifically may be used
With are as follows: it is determined as by frequency of occurrences maximum and greater than the paper of the first preset threshold and the matched target of paper to be searched is tried
Volume.Reality is in processing, since per pass topic has corresponding paper id information and the position letter in current paper in exam pool
Breath, therefore can be according to judging to belong to closest to topic which paper, Jin Erke closest to the paper ID where topic
To determine that the frequency of occurrences is maximum and be greater than the paper ID of the first preset threshold, tried so that paper ID is determined as matched target
Volume.Wherein, the frequency of occurrences of a certain paper can calculate with the following methods: closest to topic the paper topic to be searched
The ratio of quantity and topic sum to be searched in paper to be searched, alternatively, the topic number that the paper and paper to be searched match
The ratio of amount and topic sum to be searched in paper to be searched.It is understood that if the appearance of the maximum paper of the frequency of occurrences
Frequency is less than first preset threshold, indicates the topic number to match in the maximum paper of the frequency of occurrences and paper to be searched
Amount is very little, at this time it is considered that being not present and the matched target paper of paper to be searched in exam pool.
Further, in the case where the target paper for meeting preset condition is not present in step S13, in the examination to be searched
When in volume including the topic to be searched that topic types are verbal exercise, the topic to be searched that topic types are verbal exercise can be determined
For mental arithmetic topic to be changed, for each mental arithmetic topic to be changed, using preset mental arithmetic engine to the mental arithmetic topic to be changed
It is calculated and exports the verbal exercise purpose calculated result to be changed as the verbal exercise purpose answer to be changed, completed to described
Verbal exercise purpose to be changed is corrected on paper to be searched.
Step S14: in paper to be searched comprising topic types be verbal exercise topic to be searched in the case where, for every
One topic types are the topic to be searched of verbal exercise, by each topic in the feature vector of the topic to be searched and target paper
Feature vector carry out the matching of most short editing distance, determine the target topic to match in target paper with the topic to be searched,
If the topic types of target topic are verbal exercise, it is determined that the entitled mental arithmetic topic to be changed to be searched.
Specifically, being the topic to be searched of verbal exercise for topic types, carrying out the most short matched process of editing distance can
To be referred to as the process of quadratic search, the verbal exercise in paper to be searched can be further confirmed that by quadratic search.Secondary
When lookup, for each topic types be verbal exercise topic to be searched, can by target paper with the topic to be searched
Most short editing distance minimum and the search result less than the topic of the second preset threshold as the topic to be searched, that is, target
The target topic to match in paper with the topic to be searched.It, can be with if the topic types of target topic are also verbal exercise
Confirm that the topic to be searched is verbal exercise really, so that it is determined that the entitled mental arithmetic topic to be changed to be searched.Wherein, to feature
Vector carries out the calculation method that the most short matched algorithm of editing distance belongs to this field routine, and this will not be repeated here.
For example, such as verbal exercise A: " 385 × 8-265=() " and verbal exercise B: " 375 × 8-265=() ", this two
A topic is very approximate using stem vectorization feature vector obtained, therefore, if a certain entitled in paper to be searched
" 385 × 8-265=() ", be easy to for the verbal exercise B in exam pool to be determined as in step s 12 the topic closest to topic,
It is at this time inaccurate to this search result.In order to improve accuracy, carried out in target paper for the topic secondary
It searches, the standard of lookup is that the most short editing distance of text is minimum, since most short editing distance does not calculate weight, can be easy to
The topic corresponding target topic i.e. verbal exercise A in target paper are found, since the topic types of verbal exercise A are labeled as
Verbal exercise, so that it is determined that the topic is verbal exercise really.
It further, can also be verbal exercise, and the mesh in the topic types of the target topic in step S14
The title mesh situation identical as position of the topic to be searched in the paper to be searched in the position in the target paper
Under, determine the entitled mental arithmetic topic to be changed to be searched.It is understood that inscribing destination locations to topic to be searched and target
Confirmed, that is, position and target topic of the topic of verbal exercise in topic to be searched will be identified as in paper to be searched
Position in target paper is compared, and the two position is identical to indicate that target topic is strictly correctly searching for the topic to be searched
Rope as a result, in this way can to avoid identification when due to vector difference, mistakenly the topic to be searched is identified as in target paper separately
Approximate topic together.For example, verbal exercise to be changed region locating in paper to be searched, with target topic in target
Locating region is consistent in paper, then it represents that the position of the two is identical.
Step S15: be directed to each mental arithmetic topic to be changed, using preset mental arithmetic engine to the mental arithmetic topic to be changed into
Row calculates, the calculated result of output mental arithmetic engine as the verbal exercise purpose answer to be changed, complete on paper to be searched to
Verbal exercise purpose is corrected to correct.
Wherein, the preset mental arithmetic engine may include the first identification model trained in advance, the first identification mould
Type is model neural network based, identical as the second identification model, and the first identification model can be based on empty convolution sum note
Power model foundation of anticipating, specifically, carrying out feature extraction to verbal exercise mesh to be changed using empty convolution, then pass through attention mould
The feature extracted is decoded into character by type.
The mental arithmetic topic to be changed is calculated using preset mental arithmetic engine in step S15, may include: firstly,
By it is described in advance training the first identification model identify this it is to be changed mental arithmetic topic in number, letter, text, character with
And type is calculated, the calculating type may include: four fundamental rules hybrid operation, estimation, band remainder division, score calculates, unit is changed
It calculates, vertical calculating, de- formula calculating；Then, it is counted according to the number, letter, text, character and the calculating type that are identified
It calculates, obtains the verbal exercise purpose calculated result to be changed.For example, such as the entitled " 385 × 8-265=of mental arithmetic to be changed
() ", mental arithmetic engine by the first identification model can identify " 3 ", " 8 ", " 5 ", "×", " 8 ", "-", " 2 ", " 6 ", " 5 ",
"=", " (", " ", ") ", calculating type is four fundamental rules hybrid operation, and then is calculated automatically from calculated result again.
Further, to guarantee that it is accurate that verbal exercise corrects result, step S15 can also include: to examine the mental arithmetic engine
Calculated result whether the corresponding Key for Reference on the target paper consistent with the mental arithmetic topic to be changed, if consistent
The calculated result of the mental arithmetic engine is exported as the verbal exercise purpose answer to be changed.
Further, when the calculated result of the mental arithmetic engine and the mental arithmetic topic to be changed are on the target paper
When Key for Reference is inconsistent, export for indicating the inconsistent prompt information of the verbal exercise purpose Key for Reference to be changed, to mention
Show that the paper person of correcting pays attention to the mental arithmetic topic to be changed.
For example, if the calculated result of mental arithmetic engine and the mental arithmetic topic to be changed are corresponding on the target paper
Key for Reference is consistent, and the calculated result of mental arithmetic engine is shown in the verbal exercise destination region to be changed, if inconsistent, waits at this
Correct display reminding information in verbal exercise destination region, prompt information can be with are as follows: " answer is to be determined, please corrects manually " printed words.
In conclusion compared with prior art, the present invention is directed to paper to be searched, first according to each topic to be searched
The word content of stem obtains the feature vector of topic to be searched, then utilizes the feature vector of each topic to be searched from exam pool
The target paper that middle lookup matches with paper to be searched, and for topic types it is the topic to be searched of verbal exercise, in target
Feature vector inside paper based on topic carries out quadratic search, and the standard of lookup is that most short editing distance is minimum, if being matched to
The topic types of target topic be also verbal exercise, then confirm entitled mental arithmetic topic to be changed to be searched, recycle preset
Mental arithmetic engine calculates verbal exercise mesh to be changed and exports calculated result as verbal exercise purpose answer to be changed.As it can be seen that
For mental arithmetic topic to be changed, since discrimination is smaller to each other for the feature vector that is obtained according to the word content of stem, cause
From in the target paper searched in exam pool Key for Reference and the unmatched possibility of mental arithmetic topic to be changed it is also larger, therefore two
Secondary lookup determines mental arithmetic topic to be changed and is calculated by engine of doing a sum orally, and verbal exercise purpose can be improved and correct accuracy.
Embodiment of the method is corrected corresponding to the above-mentioned topic for verbal exercise, the present invention provides a kind of for verbal exercise
Topic corrects device, referring to fig. 2, the apparatus may include:
Identification module 21 is detected, can be used for detecting the image of paper to be searched, detect the examination to be searched
Each topic destination region to be searched on volume, determines the topic types of each topic to be searched, and identify each topic to be searched
The word content of stem in destination region；
Topic searching module 22 can be used for the word content of the stem according to each topic to be searched, and obtaining should be wait search
Rope inscribes purpose feature vector, and is scanned in exam pool according to the feature vector of the topic to be searched, searches the topic to be searched
The immediate topic of mesh；
Paper determining module 23, can be used for summarizing all topics to be searched found closest to the examination where topic
Volume, the paper for meeting preset condition is determined as and the matched target paper of paper to be searched；
Verbal exercise determining module 24 can be used for being verbal exercise wait search comprising topic types in the paper to be searched
It is the topic to be searched of verbal exercise for each topic types, by the feature vector of the topic to be searched in the case where rope topic
Most short editing distance is carried out with the feature vector of each topic in the target paper match, determine in the target paper and
The target topic that the topic to be searched matches, if the topic types of the target topic are verbal exercise, it is determined that this is to be searched
Entitled mental arithmetic topic to be changed；
Verbal exercise corrects module 25, can be used for utilizing preset mental arithmetic engine pair for each mental arithmetic topic to be changed
The mental arithmetic topic to be changed is calculated, and the calculated result for exporting the mental arithmetic engine is answered as the verbal exercise purpose to be changed
Verbal exercise purpose to be changed on the paper to be searched is corrected in case, completion.
Optionally, the verbal exercise determining module 25 can be also used in the topic types of the target topic being mental arithmetic
Topic, and the target topic is in the position and position of the topic to be searched in the paper to be searched in the target paper
In identical situation, the entitled mental arithmetic topic to be changed to be searched is determined.
Optionally, the paper determining module 23 can be also used for that the target paper for meeting preset condition is being not present
In the case of, in the paper to be searched comprising topic types be verbal exercise topic to be searched when, by topic types be mental arithmetic
The topic to be searched of topic is determined as mental arithmetic topic to be changed, for each mental arithmetic topic to be changed, utilizes preset mental arithmetic engine
The mental arithmetic topic to be changed is calculated and exports the verbal exercise purpose calculated result to be changed as the verbal exercise to be changed
Verbal exercise purpose to be changed on the paper to be searched is corrected in purpose answer, completion.
Optionally, the verbal exercise corrects module 25, can be also used for examining the calculated result of the mental arithmetic engine and being somebody's turn to do
Whether mental arithmetic topic to be changed corresponding Key for Reference on the target paper is consistent, and the mental arithmetic is exported if consistent and is drawn
The calculated result held up is as the verbal exercise purpose answer to be changed.
Optionally, the verbal exercise corrects module 25, can be also used for calculated result when the mental arithmetic engine with should be to
Mental arithmetic topic is corrected when the Key for Reference on the target paper is inconsistent, is exported for indicating the verbal exercise purpose to be changed
The inconsistent prompt information of Key for Reference, to prompt the paper person of correcting to pay attention to the mental arithmetic topic to be changed.
Optionally, the preset mental arithmetic engine may include the first identification model trained in advance, first identification
Model is model neural network based；
The verbal exercise corrects module 25, specifically can be used for identifying by the first identification model of the training in advance
Number, letter, text, character and calculating type in the mental arithmetic topic to be changed, the calculating type include: four fundamental rules mixing
Operation, estimation, band remainder division, score calculating, unit conversion, vertical calculating, de- formula calculate；According to the number identified,
Letter, text, character and calculating type are calculated, and the verbal exercise purpose calculated result to be changed is obtained.
Optionally, the topic searching module 22 may include:
Feature vector obtaining unit can be used for the word content input training in advance of the stem of each topic to be searched
Stem vectorization model in, obtain the feature vector of the stem of each topic to be searched, the spy as each topic to be searched
Levy vector, wherein the stem vectorization model is model neural network based；
Topic searching unit can be used for scanning in exam pool for each topic to be searched, searches and is somebody's turn to do wait search
The corresponding topic of the feature vector to match in exam pool is determined as and is somebody's turn to do by the feature vector that rope topic purpose feature vector matches
The immediate topic of topic to be searched.
Optionally, the stem vectorization model can be obtained by following steps training:
It concentrates each topic sample to be labeled processing the first topic sample training, marks out and inscribed in each topic sample
Dry word content；
Two-dimensional feature vector extraction is carried out using word content of the neural network model to stem in each topic sample, from
And training obtains the stem vectorization model.
Optionally, described device can also include:
Preprocessing module can be used for establishing index information to the feature vector of each topic on paper in exam pool in advance
Table；
The topic searching unit specifically can be used for looking into the index information table for each topic to be searched
Look for the feature vector to match with the feature vector of the topic to be searched；By the feature vector to match in the index information table
In corresponding topic be determined as and the immediate topic of topic to be searched.
Optionally, the preprocessing module can be also used for before establishing the index information table, by different length
Feature vector is grouped according to length；
The topic searching unit specifically can be used for for each topic to be searched, in the index information table with
In the same or similar grouping of the feature vector length of the topic to be searched, the feature vector phase with the topic to be searched is searched
The feature vector matched.
Optionally, the paper determining module 23 specifically can be used for the frequency of occurrences is maximum and be greater than the first default threshold
The paper of value is determined as and the matched target paper of paper to be searched.
Optionally, the detection identification module 21 specifically can be used for using preparatory trained detection model to described
The image of paper to be searched is detected, and detects each topic destination region to be searched on the paper to be searched, wherein institute
Stating detection model is model neural network based.
Optionally, the detection identification module 21 specifically can be used for knowing using trained second identification model in advance
The word content of stem in not each topic destination region to be searched, wherein second identification model is neural network based
Model.
The embodiment of the invention also provides a kind of electronic equipment, as shown in figure 3, include processor 301, communication interface 302,
Memory 303 and communication bus 304, wherein processor 301, communication interface 302, memory 303 are complete by communication bus 304
At mutual communication,
Memory 303, for storing computer program；
Processor 301 when for executing the program stored on memory 303, realizes following steps:
Step S11: detecting the image of paper to be searched, detects each to be searched on the paper to be searched
Destination region is inscribed, determines the topic types of each topic to be searched, and identifies stem in each topic destination region to be searched
Word content；
Step S12: according to the word content of the stem of each topic to be searched, obtain the feature of the topic to be searched to
Amount, and scanned in exam pool according to the feature vector of the topic to be searched, search the immediate topic of topic to be searched；
Step S13: summarize all topics to be searched found closest to the paper where topic, default item will be met
The paper of part is determined as and the matched target paper of paper to be searched；
Step S14: in the paper to be searched comprising topic types be verbal exercise topic to be searched in the case where, needle
It is the topic to be searched of verbal exercise to each topic types, it will be in the feature vector of the topic to be searched and the target paper
The feature vector of each topic carries out most short editing distance matching, determines in the target paper and matches with the topic to be searched
Target topic, if the topic types of the target topic be verbal exercise, it is determined that the entitled verbal exercise to be changed to be searched
Mesh；
Step S15: be directed to each mental arithmetic topic to be changed, using preset mental arithmetic engine to the mental arithmetic topic to be changed into
Row calculates, and exports the calculated result of the mental arithmetic engine as the verbal exercise purpose answer to be changed, completes to described to be searched
Verbal exercise purpose to be changed is corrected on paper.
Specific implementation and relevant explanation content about each step of this method may refer to above-mentioned method shown in FIG. 1
Embodiment, this will not be repeated here.
In addition, the topic for verbal exercise that processor 301 executes the program stored on memory 303 and realizes is corrected
Other implementations of method, it is identical as implementation mentioned by preceding method embodiment part, it also repeats no more here.
The communication bus that above-mentioned electronic equipment is mentioned can be Peripheral Component Interconnect standard (Peripheral Component
Interconnect, PCI) bus or expanding the industrial standard structure (Extended Industry Standard
Architecture, EISA) bus etc..The communication bus can be divided into address bus, data/address bus, control bus etc..For just
It is only indicated with a thick line in expression, figure, it is not intended that an only bus or a type of bus.
Communication interface is for the communication between above-mentioned electronic equipment and other equipment.
Memory may include random access memory (Random Access Memory, RAM), also may include non-easy
The property lost memory (Non-Volatile Memory, NVM), for example, at least a magnetic disk storage.Optionally, memory may be used also
To be storage device that at least one is located remotely from aforementioned processor.
Above-mentioned processor can be general processor, including central processing unit (Central Processing Unit,
CPU), network processing unit (Network Processor, NP) etc.；It can also be digital signal processor (Digital Signal
Processing, DSP), it is specific integrated circuit (Application Specific Integrated Circuit, ASIC), existing
It is field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete
Door or transistor logic, discrete hardware components.
The embodiment of the invention also provides a kind of computer readable storage medium, stored in the computer readable storage medium
There is computer program, which realizes that the above-mentioned topic for verbal exercise corrects method step when being executed by processor
Suddenly.
Described it should be noted that each embodiment in this specification is all made of relevant mode, each embodiment it
Between same and similar part may refer to each other, each embodiment focuses on the differences from other embodiments.
For device, electronic equipment, computer readable storage medium embodiment, implement since it is substantially similar to method
Example, so being described relatively simple, the relevent part can refer to the partial explaination of embodiments of method.
Foregoing description is only the description to present pre-ferred embodiments, not to any restriction of the scope of the invention, this hair
Any change, the modification that the those of ordinary skill in bright field does according to the disclosure above content, belong to the protection of claims
Range.
Claims (20)
1. a kind of topic for verbal exercise corrects method, which is characterized in that the described method includes:
Step S11: detecting the image of paper to be searched, detects each topic to be searched on the paper to be searched
Region, determine the topic types of each topic to be searched, and identify it is each it is to be searched topic destination region in stem text
Content；
Step S12: according to the word content of the stem of each topic to be searched, obtaining the feature vector of the topic to be searched, and
It is scanned in exam pool according to the feature vector of the topic to be searched, searches the immediate topic of topic to be searched；
Step S13: summarize all topics to be searched found closest to the paper where topic, preset condition will be met
Paper is determined as and the matched target paper of paper to be searched；
Step S14: in the paper to be searched comprising topic types be verbal exercise topic to be searched in the case where, for every
One topic types be verbal exercise topic to be searched, by the feature vector of the topic to be searched with it is each in the target paper
The feature vector of topic carries out most short editing distance matching, determines the mesh to match in the target paper with the topic to be searched
Title mesh, if the topic types of the target topic are verbal exercise, it is determined that the entitled mental arithmetic topic to be changed to be searched；
Step S15: it is directed to each mental arithmetic topic to be changed, the mental arithmetic topic to be changed is counted using preset mental arithmetic engine
It calculates, exports the calculated result of the mental arithmetic engine as the verbal exercise purpose answer to be changed, complete to the paper to be searched
Upper verbal exercise purpose to be changed is corrected.
2. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that in step S14, described
The topic types of target topic are verbal exercise, and position of the target topic in the target paper and the topic to be searched
In the identical situation in position in the paper to be searched, the entitled mental arithmetic topic to be changed to be searched is determined.
3. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that in step S13, there is no full
In the case where the target paper of sufficient preset condition, in the paper to be searched comprising topic types be verbal exercise topic to be searched
When mesh, the topic to be searched that topic types are verbal exercise is determined as mental arithmetic topic to be changed, for each verbal exercise to be changed
Mesh calculates the mental arithmetic topic to be changed using preset mental arithmetic engine and exports verbal exercise purpose calculating knot to be changed
Fruit corrects verbal exercise purpose to be changed on the paper to be searched as the verbal exercise purpose answer to be changed, completion.
4. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that step S15 further include: examine
Calculated result and mental arithmetic topic to be changed of the mental arithmetic engine on the target paper corresponding Key for Reference whether one
It causes, the calculated result of the mental arithmetic engine is exported if consistent as the verbal exercise purpose answer to be changed.
5. correcting method for the topic of verbal exercise as claimed in claim 4, which is characterized in that when the meter of the mental arithmetic engine
Result and the mental arithmetic topic to be changed are calculated when the Key for Reference on the target paper is inconsistent, is exported for indicating that this is pending
It withdraws the previous remark the inconsistent prompt information of arithmetic problem purpose Key for Reference, to prompt the paper person of correcting to pay attention to the mental arithmetic topic to be changed.
6. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that the preset mental arithmetic engine
The first identification model including training in advance, first identification model is model neural network based；
The mental arithmetic topic to be changed is calculated using preset mental arithmetic engine in step S15, comprising:
By it is described in advance training the first identification model identify this it is to be changed mental arithmetic topic in number, letter, text, word
Symbol and calculating type, the calculating type includes: four fundamental rules hybrid operation, estimation, band remainder division, score calculates, unit is changed
It calculates, vertical calculating, de- formula calculating；
It is calculated according to the number, letter, text, character and the calculating type that are identified, obtains the verbal exercise to be changed
Purpose calculated result.
7. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that the step S12 is further
Include:
Step S121 inputs the word content of the stem of each topic to be searched in stem vectorization model trained in advance,
The feature vector of the stem of each topic to be searched is obtained, the feature vector as each topic to be searched, wherein the stem
Vectorization model is model neural network based；
Step S122 is scanned in exam pool for each topic to be searched, searches the feature vector with the topic to be searched
The corresponding topic of the feature vector to match in exam pool is determined as closest with the topic to be searched by the feature vector to match
Topic.
8. correcting method for the topic of verbal exercise as claimed in claim 7, which is characterized in that the stem vectorization model
It is obtained by following steps training:
It concentrates each topic sample to be labeled processing the first topic sample training, marks out stem in each topic sample
Word content；
Two-dimensional feature vector extraction is carried out using word content of the neural network model to stem in each topic sample, to instruct
Get the stem vectorization model.
9. correcting method for the topic of verbal exercise as claimed in claim 7, which is characterized in that in advance on paper in exam pool
The feature vector of each topic establish index information table；
Step S122 further comprises:
For each topic to be searched, the feature vector of lookup and the topic to be searched matches in the index information table
Feature vector；
By the feature vector to match in the index information table corresponding topic be determined as it is closest with the topic to be searched
Topic.
10. correcting method for the topic of verbal exercise as claimed in claim 9, which is characterized in that establishing the index letter
Before ceasing table, the feature vector of different length is grouped according to length；
It is described to be directed to each topic to be searched, the feature vector phase with the topic to be searched is searched in the index information table
The feature vector matched, comprising:
For each topic to be searched, or the phase identical as the feature vector length of the topic to be searched in the index information table
In close grouping, the feature vector to match with the feature vector of the topic to be searched is searched.
11. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that step S13 will meet default
The paper of condition is determined as and the matched target paper of paper to be searched, comprising:
It is determined as by frequency of occurrences maximum and greater than the paper of the first preset threshold and the matched target of paper to be searched is tried
Volume.
12. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that step S11 is to examination to be searched
The image of volume is detected, and detects each topic destination region to be searched on the paper to be searched, comprising:
The image of the paper to be searched is detected using preparatory trained detection model, detects the examination to be searched
Each topic destination region to be searched on volume, wherein the detection model is model neural network based.
13. correcting method for the topic of verbal exercise as described in claim 1, which is characterized in that step S11 identifies each
The word content of stem in topic destination region to be searched, comprising:
The word content of stem in each topic destination region to be searched is identified using preparatory trained second identification model,
In, second identification model is model neural network based.
14. a kind of topic for verbal exercise corrects device, which is characterized in that described device includes:
Identification module is detected, detects, detects each on the paper to be searched for the image to paper to be searched
Topic destination region to be searched, determines the topic types of each topic to be searched, and identifies in each topic destination region to be searched
The word content of stem；
Topic searching module obtains the spy of the topic to be searched for the word content according to the stem of each topic to be searched
Vector is levied, and is scanned in exam pool according to the feature vector of the topic to be searched, it is immediate to search the topic to be searched
Topic；
Paper determining module, for summarize all topics to be searched found closest to the paper where topic, will meet
The paper of preset condition is determined as and the matched target paper of paper to be searched；
Verbal exercise determining module, for including the feelings for the topic to be searched that topic types are verbal exercise in the paper to be searched
It is the topic to be searched of verbal exercise for each topic types, by the feature vector of the topic to be searched and the target under condition
The feature vector of each topic in paper carries out most short editing distance matching, determine in the target paper with the topic to be searched
The target topic that mesh matches, if the topic types of the target topic are verbal exercise, it is determined that this is to be searched entitled pending
It withdraws the previous remark arithmetic problem mesh；
Verbal exercise corrects module, for being directed to each mental arithmetic topic to be changed, pending is withdrawn the previous remark using preset mental arithmetic engine to this
Arithmetic problem mesh is calculated, and is exported the calculated result of the mental arithmetic engine as the verbal exercise purpose answer to be changed, is completed to institute
Verbal exercise purpose to be changed on paper to be searched is stated to correct.
15. correcting device for the topic of verbal exercise as claimed in claim 14, which is characterized in that the verbal exercise determines mould
Block is also used in the topic types of the target topic be verbal exercise, and position of the target topic in the target paper
It sets with the topic to be searched in the identical situation in position in the paper to be searched, determines that this is to be searched entitled to be changed
Mental arithmetic topic.
16. correcting device for the topic of verbal exercise as claimed in claim 14, which is characterized in that the paper determines mould
Block is also used to there is no the target paper for meeting preset condition, includes topic class in the paper to be searched
When type is the topic to be searched of verbal exercise, the topic to be searched that topic types are verbal exercise is determined as mental arithmetic topic to be changed,
For each mental arithmetic topic to be changed, the mental arithmetic topic to be changed is calculated using preset mental arithmetic engine and export this to
Verbal exercise purpose calculated result is corrected as the verbal exercise purpose answer to be changed, is completed to be changed on the paper to be searched
Verbal exercise purpose is corrected.
17. correcting device for the topic of verbal exercise as claimed in claim 14, which is characterized in that the verbal exercise corrects mould
Block, be also used to examine the calculated result of the mental arithmetic engine with the mental arithmetic topic to be changed the corresponding ginseng on the target paper
Examine whether answer is consistent, the calculated result that the mental arithmetic engine is exported if consistent is answered as the verbal exercise purpose to be changed
Case；
The verbal exercise corrects module, be also used to when it is described mental arithmetic engine calculated result with the mental arithmetic topic to be changed described
When Key for Reference on target paper is inconsistent, verbal exercise purpose Key for Reference to be changed is inconsistent for indicating this mentions for output
Show information, to prompt the paper person of correcting to pay attention to the mental arithmetic topic to be changed.
18. correcting device for the topic of verbal exercise as claimed in claim 14, which is characterized in that the preset mental arithmetic is drawn
The first identification model including training in advance is held up, first identification model is model neural network based；
The verbal exercise corrects module, identifies that this pending is withdrawn the previous remark specifically for the first identification model by the training in advance
Number, letter, text, character in arithmetic problem mesh and calculate type, the calculating type include: four fundamental rules hybrid operation, estimation,
Band remainder division, score calculating, unit conversion, vertical calculating, de- formula calculate；According to identified number, letter, text,
Character and calculating type are calculated, and the verbal exercise purpose calculated result to be changed is obtained.
19. a kind of electronic equipment, which is characterized in that including processor, communication interface, memory and communication bus, wherein processing
Device, communication interface, memory complete mutual communication by communication bus；
Memory, for storing computer program；
Processor when for executing the program stored on memory, realizes any method and step of claim 1-13.
20. a kind of computer readable storage medium, which is characterized in that be stored with computer in the computer readable storage medium
Program realizes claim 1-13 described in any item method and steps when the computer program is executed by processor.
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| CN201811125659.4A CN109189895B (en) | 2018-09-26 | 2018-09-26 | Question correcting method and device for oral calculation questions |
| PCT/CN2019/105321 WO2020063347A1 (en) | 2018-09-26 | 2019-09-11 | Answer marking method for mental calculation questions, device, electronic apparatus, and storage medium |
| US16/756,468 US11721229B2 (en) | 2018-09-26 | 2019-09-11 | Question correction method, device, electronic equipment and storage medium for oral calculation questions |
| EP19865656.3A EP3859558A4 (en) | 2018-09-26 | 2019-09-11 | Answer marking method for mental calculation questions, device, electronic apparatus, and storage medium |
| JP2021517407A JP7077483B2 (en) | 2018-09-26 | 2019-09-11 | Problem correction methods, devices, electronic devices and storage media for mental arithmetic problems |
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| CN201811125659.4A CN109189895B (en) | 2018-09-26 | 2018-09-26 | Question correcting method and device for oral calculation questions |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110929582A (en) * | 2019-10-25 | 2020-03-27 | 广州视源电子科技股份有限公司 | Automatic correction method and device for oral calculation questions, storage medium and electronic equipment |
| WO2020063347A1 (en) * | 2018-09-26 | 2020-04-02 | 杭州大拿科技股份有限公司 | Answer marking method for mental calculation questions, device, electronic apparatus, and storage medium |
| WO2020177531A1 (en) * | 2019-03-04 | 2020-09-10 | 杭州大拿科技股份有限公司 | Question assistance method and system |
| CN111666799A (en) * | 2019-03-08 | 2020-09-15 | 小船出海教育科技（北京）有限公司 | Method and terminal for checking oral calculation questions |
| CN112307858A (en) * | 2019-08-30 | 2021-02-02 | 北京字节跳动网络技术有限公司 | Image recognition and processing method, device, equipment and storage medium |
| CN112396009A (en) * | 2020-11-24 | 2021-02-23 | 广东国粒教育技术有限公司 | Calculation question correcting method and device based on full convolution neural network model |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164994A (en) * | 2013-03-15 | 2013-06-19 | 南京信息工程大学 | Operation exercise correction and feedback method |
| CN103646244A (en) * | 2013-12-16 | 2014-03-19 | 北京天诚盛业科技有限公司 | Methods and devices for face characteristic extraction and authentication |
| CN105955962A (en) * | 2016-05-10 | 2016-09-21 | 北京新唐思创教育科技有限公司 | Method and device for calculating similarity of topics |
| CN106060172A (en) * | 2016-07-21 | 2016-10-26 | 北京华云天科技有限公司 | Method for judging answer to test question and server |
| CN106251725A (en) * | 2016-07-21 | 2016-12-21 | 北京华云天科技有限公司 | Examination question corrects method and server |
| CN107818168A (en) * | 2017-11-10 | 2018-03-20 | 广东小天才科技有限公司 | Topic searching method, device and equipment |
| CN108537014A (en) * | 2018-04-04 | 2018-09-14 | 深圳大学 | A kind of method for authenticating user identity and system based on mobile device |
-
2018
- 2018-09-26 CN CN201811125659.4A patent/CN109189895B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164994A (en) * | 2013-03-15 | 2013-06-19 | 南京信息工程大学 | Operation exercise correction and feedback method |
| CN103646244A (en) * | 2013-12-16 | 2014-03-19 | 北京天诚盛业科技有限公司 | Methods and devices for face characteristic extraction and authentication |
| CN105955962A (en) * | 2016-05-10 | 2016-09-21 | 北京新唐思创教育科技有限公司 | Method and device for calculating similarity of topics |
| CN106060172A (en) * | 2016-07-21 | 2016-10-26 | 北京华云天科技有限公司 | Method for judging answer to test question and server |
| CN106251725A (en) * | 2016-07-21 | 2016-12-21 | 北京华云天科技有限公司 | Examination question corrects method and server |
| CN107818168A (en) * | 2017-11-10 | 2018-03-20 | 广东小天才科技有限公司 | Topic searching method, device and equipment |
| CN108537014A (en) * | 2018-04-04 | 2018-09-14 | 深圳大学 | A kind of method for authenticating user identity and system based on mobile device |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020063347A1 (en) * | 2018-09-26 | 2020-04-02 | 杭州大拿科技股份有限公司 | Answer marking method for mental calculation questions, device, electronic apparatus, and storage medium |
| US11721229B2 (en) | 2018-09-26 | 2023-08-08 | Hangzhou Dana Technology Inc. | Question correction method, device, electronic equipment and storage medium for oral calculation questions |
| WO2020177531A1 (en) * | 2019-03-04 | 2020-09-10 | 杭州大拿科技股份有限公司 | Question assistance method and system |
| CN111666799A (en) * | 2019-03-08 | 2020-09-15 | 小船出海教育科技（北京）有限公司 | Method and terminal for checking oral calculation questions |
| CN112307858A (en) * | 2019-08-30 | 2021-02-02 | 北京字节跳动网络技术有限公司 | Image recognition and processing method, device, equipment and storage medium |
| CN110929582A (en) * | 2019-10-25 | 2020-03-27 | 广州视源电子科技股份有限公司 | Automatic correction method and device for oral calculation questions, storage medium and electronic equipment |
| CN112396009A (en) * | 2020-11-24 | 2021-02-23 | 广东国粒教育技术有限公司 | Calculation question correcting method and device based on full convolution neural network model |
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