CN113688268B - Picture information extraction method, device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of artificial intelligence, is applied to the medical field, and relates to a picture information extraction method, which comprises the steps of obtaining a picture to be processed, and carrying out text recognition on the picture to be processed to obtain a text to be processed; extracting features of the text to be processed according to a preset word bag model to obtain feature data, and classifying the pictures to be processed based on the feature data; when the picture type is a non-standard file type, inputting characteristic data to a preset extractor to obtain a first extraction text, and inputting the characteristic data to a target extraction model to obtain a second extraction text; and carrying out text screening on the first extraction text and the second extraction text to obtain a target extraction text, inputting the target extraction text into a target knowledge base, and generating structured data. The application also provides a picture information extraction device, a computer device and a storage medium. In addition, the present application relates to blockchain technology in which structured data may be stored. The method and the device realize efficient extraction of the picture information.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for extracting picture information, a computer device, and a storage medium.
Background
In the process of evaluating the patient's risk of illness, the patient is often required to submit relevant case data, and the patient's risk of illness is judged according to the case data. Wherein, the case data comprises patient certificate data, medical diagnosis and treatment data and physical examination data, and the medical diagnosis and treatment data comprises different data pictures such as outpatient medical records, inpatient medical records, pathological reports and the like.
The traditional risk decision is a manual decision mode, the whole process is time-consuming and labor-consuming, and because of huge differences of patient data types, accurate information extraction can not be carried out on data pictures provided by patients, and finally the problem of low accuracy of patient information extraction is caused.
Disclosure of Invention
An embodiment of the application aims to provide a picture information extraction method, a device, computer equipment and a storage medium, so as to solve the technical problem of inaccurate picture information extraction.
In order to solve the above technical problems, the embodiments of the present application provide a method for extracting picture information, which adopts the following technical schemes:
Acquiring a picture to be processed, and performing text recognition on the picture to be processed to obtain a text to be processed;
acquiring a preset word bag model, extracting features of the text to be processed according to the preset word bag model to obtain feature data, classifying the pictures to be processed based on the feature data, and determining the picture types of the pictures to be processed;
when the picture type is a non-standard file type, inputting the characteristic data to a preset extractor to obtain a first extraction text, inputting the characteristic data to a target extraction model, and extracting to obtain a second extraction text;
and carrying out text screening on the first extraction text and the second extraction text to obtain a target extraction text, inputting the target extraction text to a target knowledge base, and generating structured data corresponding to the picture to be processed.
Further, the step of extracting features of the text to be processed according to the preset bag-of-words model to obtain feature data includes:
performing morphological reduction, part-of-speech tagging and numerical normalization on the text to be processed to obtain a preprocessed text of the text to be processed;
inputting the preprocessing text to the preset bag-of-words model, and calculating to obtain the characteristic data.
Further, the step of classifying the to-be-processed picture based on the feature data, and determining the picture type of the to-be-processed picture includes:
acquiring normalized coordinate information of the text to be processed;
and classifying the picture to be processed according to the normalized coordinate information and the characteristic data to obtain the picture type of the picture to be processed.
Further, the step of inputting the feature data to a preset extractor to extract a first extracted text includes:
matching the feature data with word vectors corresponding to preset standard keywords according to the preset extractor to obtain hit keywords matched with the standard keywords in the text to be processed;
and carrying out neighborhood search on the hit keywords to obtain the first extraction text.
Further, the step of inputting the feature data to a target extraction model and extracting to obtain a second extracted text includes:
the target extraction model comprises a two-way long-short-term memory network and a conditional random field model, the characteristic data are input into the two-way long-short-term memory network, and the characteristic vector of the text to be processed is obtained through calculation;
And calculating the feature vector according to the conditional random field model to obtain entity information corresponding to the feature vector, and determining the entity information as the second extraction text.
Further, the step of text filtering the first extracted text and the second extracted text includes:
acquiring confidence degrees corresponding to the first extraction text and the second extraction text respectively;
and sequencing the first extraction text and the second extraction text according to the confidence, and selecting a preset number of texts from high to low according to the confidence as the target extraction text.
Further, after the step of determining the picture type of the picture to be processed, the method further includes:
when the picture type is a standard file type, acquiring a preset extraction template;
and carrying out text extraction on the text to be processed according to the preset extraction template to obtain a target extraction text corresponding to the text to be processed.
In order to solve the above technical problems, the embodiment of the present application further provides a picture information extraction device, which adopts the following technical scheme:
the recognition module is used for acquiring a picture to be processed, and carrying out text recognition on the picture to be processed to obtain a text to be processed;
The acquisition module is used for acquiring a preset word bag model, extracting the characteristics of the text to be processed according to the preset word bag model to obtain characteristic data, classifying the pictures to be processed based on the characteristic data, and determining the picture types of the pictures to be processed;
the extraction module is used for inputting the characteristic data to a preset extractor to obtain a first extraction text when the picture type is a non-standard file type, inputting the characteristic data to a target extraction model and obtaining a second extraction text;
the generation module is used for conducting text screening on the first extraction text and the second extraction text to obtain a target extraction text, inputting the target extraction text to a target knowledge base and generating structured data corresponding to the picture to be processed.
In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:
acquiring a picture to be processed, and performing text recognition on the picture to be processed to obtain a text to be processed;
acquiring a preset word bag model, extracting features of the text to be processed according to the preset word bag model to obtain feature data, classifying the pictures to be processed based on the feature data, and determining the picture types of the pictures to be processed;
When the picture type is a non-standard file type, inputting the characteristic data to a preset extractor to obtain a first extraction text, inputting the characteristic data to a target extraction model, and extracting to obtain a second extraction text;
and carrying out text screening on the first extraction text and the second extraction text to obtain a target extraction text, inputting the target extraction text to a target knowledge base, and generating structured data corresponding to the picture to be processed.
In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:
acquiring a picture to be processed, and performing text recognition on the picture to be processed to obtain a text to be processed;
acquiring a preset word bag model, extracting features of the text to be processed according to the preset word bag model to obtain feature data, classifying the pictures to be processed based on the feature data, and determining the picture types of the pictures to be processed;
when the picture type is a non-standard file type, inputting the characteristic data to a preset extractor to obtain a first extraction text, inputting the characteristic data to a target extraction model, and extracting to obtain a second extraction text;
And carrying out text screening on the first extraction text and the second extraction text to obtain a target extraction text, inputting the target extraction text to a target knowledge base, and generating structured data corresponding to the picture to be processed.
According to the method, the picture to be processed is acquired, text recognition is carried out on the picture to be processed, and the text to be processed is obtained; then, acquiring a preset word bag model, extracting features of the text to be processed according to the preset word bag model to obtain feature data, classifying the pictures to be processed based on the feature data, determining the picture types of the pictures to be processed, and carrying out different processing on the text to be processed according to different picture types to further improve the picture information extraction efficiency; when the picture type is a non-standard file type, inputting characteristic data to a preset extractor to obtain a first extraction text, inputting the characteristic data to a target extraction model, and extracting to obtain a second extraction text, so that the target extraction text can be accurately obtained according to the first extraction text and the second extraction text; and then, text screening is carried out on the first extraction text and the second extraction text to obtain target extraction text, the target extraction text is input to a target knowledge base, and structured data corresponding to the picture to be processed is generated, so that efficient extraction of picture information is realized, the extraction efficiency and the extraction accuracy of the picture information are improved, the decision efficiency of intelligent decision according to the extracted text information is further improved, and the decision period is reduced.
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For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a picture information extraction method according to the present application;
fig. 3 is a schematic structural view of an embodiment of a picture information extraction apparatus according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to the present application.
Reference numerals: picture information extracting apparatus 300, identifying module 301, acquiring module 302, extracting module 303, and generating module 304.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The patient may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the method for extracting picture information provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the picture information extracting apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow chart of one embodiment of a method of picture information extraction according to the present application is shown. The picture information extraction method comprises the following steps:
Step S201, obtaining a picture to be processed, and carrying out text recognition on the picture to be processed to obtain a text to be processed;
in this embodiment, the to-be-processed picture is a picture with patient information that needs to be extracted. For example, in the medical field, the image to be processed may be various images including patient diagnosis information or identity information. And acquiring a picture to be processed, and performing text recognition on the picture to be processed to obtain a text to be processed, wherein the text to be processed can be obtained by recognizing the picture to be processed through OCR (Optical Character Recognition ) technology.
Step S202, acquiring a preset word bag model, extracting features of the text to be processed according to the preset word bag model to obtain feature data, classifying the pictures to be processed based on the feature data, and determining the picture types of the pictures to be processed;
in this embodiment, the bag of words model (Bag of words model) is a document representation, in information retrieval, the bag of words model assumes that for a document, the word order and grammar, syntax, etc. of the document are ignored, the document is taken as a set of words, each word in the document is independent of other words, and for any word in the document is independently selected without being affected by the semantics of the document. Therefore, a preset word bag model is obtained, the text to be processed is used as a text set according to the preset word bag model, the occurrence times of each word are calculated from the text set, and the occurrence times of each word are used as word vectors corresponding to each word in the text to be processed. Specifically, when a text to be processed is obtained, preprocessing the text to be processed according to sentences to obtain the preprocessed text to be processed; inputting the preprocessed text to be processed into a preset word bag model, and carrying out vector representation on words in the preprocessed text to be processed through the preset word bag model to obtain word vectors corresponding to each word, wherein the word vectors are feature data. And classifying the picture to be processed according to the characteristic data, and determining the picture type of the picture to be processed. The picture types comprise a non-standard file type and a standard file type, wherein the standard file type is a picture type with a fixed standard such as an identity card, and the non-standard file type is all picture types except a certificate, such as a patient diagnosis information picture. When the feature data is obtained, matching the feature data with element words in an element library, namely, calculating the similarity between the feature data and vectors corresponding to the element words in the element library to obtain element matching degree; and determining the element words with the element matching degree larger than or equal to a preset threshold value as the element words matched with the feature data, obtaining the picture types of the element words, and determining the picture types of the current to-be-processed pictures according to the picture types of the element words.
Step S203, when the picture type is a non-standard file type, inputting the characteristic data to a preset extractor to obtain a first extraction text, inputting the characteristic data to a target extraction model, and extracting to obtain a second extraction text;
in this embodiment, when the picture type is a non-standard file type, the text to be processed is input to a preset extractor, the preset extractor is a preset text extractor, a plurality of groups of extraction instructions are set in the preset extractor, and text extraction is performed on the text to be processed according to extraction instructions corresponding to different extraction rules set in the preset extractor, so as to obtain a first extraction text. Meanwhile, the text to be processed is input into a target extraction model, wherein the target extraction model is a preset neural network text extraction model, such as a text extraction model consisting of a Bi-directional long-short Term Memory network (BILSTM, bi-directional Long Short-Term Memory) and a conditional random field model (CRF, conditional Random Fields). And extracting the text to be processed according to the target extraction model to obtain a second extracted text.
Step S204, text screening is carried out on the first extraction text and the second extraction text to obtain a target extraction text, the target extraction text is input to a target knowledge base, and structured data corresponding to the picture to be processed is generated.
In this embodiment, when the first extraction text and the second extraction text are obtained, text screening is performed on the first extraction text and the second extraction text to obtain the target extraction text. Specifically, when the first extraction text and the second extraction text are obtained, ordering the occurrence frequency of all texts in the first extraction text and the second extraction text from high to low, and selecting a preset number of texts from high to low as target extraction texts. When the target extraction text is obtained, the target extraction text is input into a target knowledge base, and a knowledge graph is stored in the target knowledge base, wherein the knowledge graph contains all data associated with the target extraction text. And according to the knowledge graph, all data associated with the current target extraction text can be searched, and the structured data corresponding to the picture to be processed is formed by all data associated with the current target extraction text and the target extraction text.
It is emphasized that to further ensure the privacy and security of the structured data, the structured data may also be stored in nodes of a blockchain.
The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The method and the device realize efficient extraction of the picture information, improve the extraction efficiency and the extraction accuracy of the picture information, further improve the decision efficiency of intelligent decision according to the extracted text information, and reduce the decision period.
In some optional implementations of this embodiment, the step of extracting features of the text to be processed according to the preset word bag model to obtain feature data includes:
performing morphological reduction, part-of-speech tagging and numerical normalization on the text to be processed to obtain a preprocessed text of the text to be processed;
inputting the preprocessing text to the preset bag-of-words model, and calculating to obtain the characteristic data.
In this embodiment, when a text to be processed is obtained, word shape reduction, part-of-speech tagging and number normalization are performed on the text to be processed to obtain a preprocessed text. Specifically, the morphological reduction is a process of reducing words into prototype words in a dictionary according to the parts of speech after the English is segmented; the part of speech tagging is a process of tagging each word with part of speech according to a preset dictionary to obtain the part of speech (such as nouns and verbs) of each word; and the digit normalization is a process of mapping digits in the text to be processed in a standardized format so that the digits are in a preset format. The text to be processed after the word shape reduction, the part of speech tagging and the number normalization is the preprocessing text. When the preprocessed text is obtained, the preprocessed text is input into a preset word bag model, and feature data of the text to be processed are obtained through calculation.
According to the method, the device and the system, the preprocessing text is obtained through morphological reduction, part-of-speech tagging and digital normalization of the text to be processed, and then the feature data is obtained through feature extraction of the preprocessing text, so that the text can be accurately represented according to the feature data, and the accuracy of classifying the pictures to be processed is further improved.
In some optional implementations of this embodiment, the step of classifying the to-be-processed picture based on the feature data, and determining the picture type of the to-be-processed picture includes:
acquiring normalized coordinate information of the text to be processed;
and classifying the picture to be processed according to the normalized coordinate information and the characteristic data to obtain the picture type of the picture to be processed.
In this embodiment, in order to classify a picture to be processed more accurately, when feature data is obtained, normalized coordinate information of a text to be processed is obtained, and the picture to be processed is classified according to the feature data and the normalized coordinate information. Specifically, the normalized coordinate information is coordinate information obtained by normalizing characters in the picture to be processed. And acquiring pixel information of each text in the picture to be processed, and mapping the pixel information to a unified coordinate dimension according to a preset mapping standard to obtain normalized coordinate information of the text to be processed. Comparing the normalized coordinate information with standard coordinate information, and acquiring feature data corresponding to the normalized coordinate information when the normalized coordinate information is matched with the standard coordinate information; and then, calculating the matching degree of the feature data corresponding to the normalized coordinate information and the standard word vector corresponding to the standard coordinate information, and if the feature data corresponding to the normalized coordinate information and the standard word vector corresponding to the standard coordinate information are successfully matched, determining that the picture type to which the standard word vector corresponding to the standard coordinate information belongs is the picture type of the picture to be processed.
According to the method and the device for classifying the images to be processed, the normalized coordinate information of the text to be processed is obtained, and the images to be processed are classified according to the normalized coordinate information and the feature data, so that the accuracy of classifying the images to be processed is improved.
In some optional implementations of this embodiment, the step of inputting the feature data to a preset extractor, and extracting to obtain the first extracted text includes:
matching the feature data with word vectors corresponding to preset standard keywords according to the preset extractor to obtain hit keywords matched with the standard keywords in the text to be processed;
and carrying out neighborhood search on the hit keywords to obtain the first extraction text.
In this embodiment, when extracting a text to be processed according to a preset extractor, a preset standard keyword is obtained, and the feature data of the text to be processed and the word vector of the standard keyword are subjected to cosine similarity calculation to obtain a hit keyword matched with the standard keyword in the text to be processed. And when the hit key word is obtained, carrying out neighborhood search on the hit key word to obtain a first extraction text. Specifically, when a hit keyword is obtained, a domain mapping set corresponding to the hit keyword is obtained, and an optimal solution of the hit keyword on the domain mapping set is calculated according to a preset domain searching algorithm, so that a first extraction text is obtained. The domain searching algorithm comprises searching calculation scores of a local searching algorithm, a variable domain searching algorithm and the like, and different searching algorithms correspond to different domain functions; and calculating an optimal solution of the hit keywords on the corresponding domain mapping set according to the domain function, and obtaining a first extraction text.
According to the method and the device for extracting the first extraction text, the first extraction text is obtained through the field search of the hit keywords, the accurate extraction of the first extraction text is achieved, and the extraction accuracy and the extraction efficiency of the first extraction text are improved.
In some optional implementations of this embodiment, the step of inputting the feature data into the target extraction model, and extracting to obtain the second extracted text includes:
the target extraction model comprises a two-way long-short-term memory network and a conditional random field model, the characteristic data are input into the two-way long-short-term memory network, and the characteristic vector of the text to be processed is obtained through calculation;
and calculating the feature vector according to the conditional random field model to obtain entity information corresponding to the feature vector, and determining the entity information as the second extraction text.
In this embodiment, the target extraction model includes a two-way long and short term memory network and a conditional random field model. According to the two-way long-short-term memory network, semantic features can be extracted from the input feature data, so that the extracted feature vectors can better represent semantic information of sentence context. When the feature data corresponding to the text to be processed is obtained, inputting the feature data to a two-way long-short-term memory network in a target extraction model, and calculating to obtain feature vectors according to the two-way long-term memory network; and then, inputting the feature vector into a conditional random field model in a target extraction model, and outputting entity information which corresponds to the feature vector and is optimal according to the conditional random field model to obtain a second extraction text which corresponds to the text to be processed.
According to the method and the device for extracting the second extraction text, the second extraction text is extracted through the target extraction model, so that efficient extraction of the second text is achieved, and further picture information can be accurately extracted and determined according to the second extraction text.
In some optional implementations of this embodiment, the step of performing text filtering on the first extracted text and the second extracted text includes:
acquiring confidence degrees corresponding to the first extraction text and the second extraction text respectively;
and sequencing the first extraction text and the second extraction text according to the confidence, and selecting a preset number of texts from high to low according to the confidence as the target extraction text.
In this embodiment, the confidence level corresponding to the first extracted text is a first confidence level, where the first confidence level is a similarity between the first extracted text and the standard keyword, which is calculated according to a preset extractor; the confidence corresponding to the second extraction text is the second confidence, and the second confidence is a predicted value of the second extraction text calculated according to the target extraction model. Specifically, when a first extraction text and a second extraction text are obtained, obtaining the similarity corresponding to the first extraction text and the standard keyword, and taking the similarity as a first confidence coefficient; and simultaneously, acquiring a predicted value corresponding to the second extraction text, wherein when the target extraction model carries out entity output on the second extraction text, the probability information of the entity corresponding to the second extraction text is the predicted value. And uniformly sequencing the first extraction text and the second extraction text according to the first confidence coefficient and the second confidence coefficient, and selecting a preset number of texts from high to low as target extraction texts.
According to the method and the device for extracting the text from the image, the target extraction text is determined through the confidence, and the accuracy of text information extraction in the image to be processed is improved.
The method further includes, in some optional implementations of the present embodiment, after the step of determining the picture type of the to-be-processed picture, the steps of:
when the picture type is a standard file type, acquiring a preset extraction template;
and carrying out text extraction on the text to be processed according to the preset extraction template to obtain a target extraction text corresponding to the text to be processed.
In this embodiment, when the picture type of the picture to be processed is a standard file type, a preset extraction template is obtained, where the preset extraction template includes a text template corresponding to each standard file type. And carrying out text extraction on the text to be processed according to the preset extraction template to obtain a target extraction text corresponding to the text to be processed. Specifically, when a preset extraction template is obtained, a template field of the preset extraction template is obtained, the template field is matched with a text field in a text to be processed, a text field matched with the template field is obtained, text information under the text field in the text to be processed is obtained, and a target extraction text is obtained.
According to the method and the device for extracting the text information of the picture to be processed, the text information of the picture to be processed of the standard file type is extracted through the preset extraction template, and the text information extraction efficiency of the picture to be processed of the standard file type is improved.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a picture information extraction apparatus, where an embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 3, the picture information extraction apparatus 300 according to the present embodiment includes: an identification module 301, an acquisition module 302, an extraction module 303 and a generation module 304. Wherein:
the recognition module 301 is configured to obtain a to-be-processed picture, and perform text recognition on the to-be-processed picture to obtain a to-be-processed text;
in this embodiment, the to-be-processed picture is a picture with patient information that needs to be extracted. For example, in the medical field, the image to be processed may be various images including patient diagnosis information or identity information. And acquiring a picture to be processed, and performing text recognition on the picture to be processed to obtain a text to be processed, wherein the text to be processed can be obtained by recognizing the picture to be processed through OCR (Optical Character Recognition ) technology.
The obtaining module 302 is configured to obtain a preset bag-of-words model, perform feature extraction on the text to be processed according to the preset bag-of-words model, obtain feature data, classify the picture to be processed based on the feature data, and determine a picture type of the picture to be processed;
In this embodiment, the bag of words model (Bag of words model) is a document representation, in information retrieval, the bag of words model assumes that for a document, the word order and grammar, syntax, etc. of the document are ignored, the document is taken as a set of words, each word in the document is independent of other words, and for any word in the document is independently selected without being affected by the semantics of the document. Therefore, a preset word bag model is obtained, the text to be processed is used as a text set according to the preset word bag model, the occurrence times of each word are calculated from the text set, and the occurrence times of each word are used as word vectors corresponding to each word in the text to be processed. Specifically, when a text to be processed is obtained, preprocessing the text to be processed according to sentences to obtain the preprocessed text to be processed; inputting the preprocessed text to be processed into a preset word bag model, and carrying out vector representation on words in the preprocessed text to be processed through the preset word bag model to obtain word vectors corresponding to each word, wherein the word vectors are feature data. And classifying the picture to be processed according to the characteristic data, and determining the picture type of the picture to be processed. The picture types comprise a non-standard file type and a standard file type, wherein the standard file type is a picture type with a fixed standard such as an identity card, and the non-standard file type is all picture types except a certificate, such as a patient diagnosis information picture. When the feature data is obtained, matching the feature data with element words in an element library, namely, calculating the similarity between the feature data and vectors corresponding to the element words in the element library to obtain element matching degree; and determining the element words with the element matching degree larger than or equal to a preset threshold value as the element words matched with the feature data, obtaining the picture types of the element words, and determining the picture types of the current to-be-processed pictures according to the picture types of the element words.
In some alternative implementations of the present embodiment, the acquiring module 302 includes:
the preprocessing unit is used for performing morphological reduction, part-of-speech tagging and numerical normalization on the text to be processed to obtain a preprocessed text of the text to be processed;
the processing unit is used for inputting the preprocessing text to the preset word bag model and calculating to obtain the characteristic data.
In this embodiment, when a text to be processed is obtained, word shape reduction, part-of-speech tagging and number normalization are performed on the text to be processed to obtain a preprocessed text. Specifically, the morphological reduction is a process of reducing words into prototype words in a dictionary according to the parts of speech after the English is segmented; the part of speech tagging is a process of tagging each word with part of speech according to a preset dictionary to obtain the part of speech (such as nouns and verbs) of each word; and the digit normalization is a process of mapping digits in the text to be processed in a standardized format so that the digits are in a preset format. The text to be processed after the word shape reduction, the part of speech tagging and the number normalization is the preprocessing text. When the preprocessed text is obtained, the preprocessed text is input into a preset word bag model, and feature data of the text to be processed are obtained through calculation.
In some optional implementations of this embodiment, the acquiring module 302 further includes:
the first acquisition unit is used for acquiring normalized coordinate information of the text to be processed;
and the classification unit is used for classifying the picture to be processed according to the normalized coordinate information and the characteristic data to obtain the picture type of the picture to be processed.
In this embodiment, in order to classify a picture to be processed more accurately, when feature data is obtained, normalized coordinate information of a text to be processed is obtained, and the picture to be processed is classified according to the feature data and the normalized coordinate information. Specifically, the normalized coordinate information is coordinate information obtained by normalizing characters in the picture to be processed. And acquiring pixel information of each text in the picture to be processed, and mapping the pixel information to a unified coordinate dimension according to a preset mapping standard to obtain normalized coordinate information of the text to be processed. Comparing the normalized coordinate information with standard coordinate information, and acquiring feature data corresponding to the normalized coordinate information when the normalized coordinate information is matched with the standard coordinate information; and then, calculating the matching degree of the feature data corresponding to the normalized coordinate information and the standard word vector corresponding to the standard coordinate information, and if the feature data corresponding to the normalized coordinate information and the standard word vector corresponding to the standard coordinate information are successfully matched, determining that the picture type to which the standard word vector corresponding to the standard coordinate information belongs is the picture type of the picture to be processed.
The extraction module 303 is configured to input the feature data to a preset extractor to obtain a first extraction text, input the feature data to a target extraction model, and obtain a second extraction text when the picture type is a non-standard file type;
in this embodiment, when the picture type is a non-standard file type, the text to be processed is input to a preset extractor, the preset extractor is a preset text extractor, a plurality of groups of extraction instructions are set in the preset extractor, and text extraction is performed on the text to be processed according to extraction instructions corresponding to different extraction rules set in the preset extractor, so as to obtain a first extraction text. Meanwhile, the text to be processed is input into a target extraction model, wherein the target extraction model is a preset neural network text extraction model, such as a text extraction model consisting of a Bi-directional long-short Term Memory network (BILSTM, bi-directional Long Short-Term Memory) and a conditional random field model (CRF, conditional Random Fields). And extracting the text to be processed according to the target extraction model to obtain a second extracted text.
In some alternative implementations of the present embodiment, the extraction module 303 includes:
The matching unit is used for matching the feature data with word vectors corresponding to preset standard keywords according to the preset extractor to obtain hit keywords matched with the standard keywords in the text to be processed;
and the searching unit is used for carrying out neighborhood searching on the hit keywords to obtain the first extraction text.
In this embodiment, when extracting a text to be processed according to a preset extractor, a preset standard keyword is obtained, and the feature data of the text to be processed and the word vector of the standard keyword are subjected to cosine similarity calculation to obtain a hit keyword matched with the standard keyword in the text to be processed. And when the hit key word is obtained, carrying out neighborhood search on the hit key word to obtain a first extraction text. Specifically, when a hit keyword is obtained, a domain mapping set corresponding to the hit keyword is obtained, and an optimal solution of the hit keyword on the domain mapping set is calculated according to a preset domain searching algorithm, so that a first extraction text is obtained. The domain searching algorithm comprises searching calculation scores of a local searching algorithm, a variable domain searching algorithm and the like, and different searching algorithms correspond to different domain functions; and calculating an optimal solution of the hit keywords on the corresponding domain mapping set according to the domain function, and obtaining a first extraction text.
In some optional implementations of this embodiment, the extracting module 303 further includes:
the first calculation unit is used for the target extraction model to comprise a two-way long-short-term memory network and a conditional random field model, inputting the characteristic data into the two-way long-short-term memory network and calculating to obtain the characteristic vector of the text to be processed;
and the second calculation unit is used for calculating the feature vector according to the conditional random field model to obtain entity information corresponding to the feature vector, and determining the entity information as the second extraction text.
In this embodiment, the target extraction model includes a two-way long and short term memory network and a conditional random field model. According to the two-way long-short-term memory network, semantic features can be extracted from the input feature data, so that the extracted feature vectors can better represent semantic information of sentence context. When the feature data corresponding to the text to be processed is obtained, inputting the feature data to a two-way long-short-term memory network in a target extraction model, and calculating to obtain feature vectors according to the two-way long-term memory network; and then, inputting the feature vector into a conditional random field model in a target extraction model, and outputting entity information which corresponds to the feature vector and is optimal according to the conditional random field model to obtain a second extraction text which corresponds to the text to be processed.
The generating module 304 is configured to perform text screening on the first extraction text and the second extraction text to obtain a target extraction text, input the target extraction text to a target knowledge base, and generate structured data corresponding to the to-be-processed picture.
In this embodiment, when the first extraction text and the second extraction text are obtained, text screening is performed on the first extraction text and the second extraction text to obtain the target extraction text. Specifically, when the first extraction text and the second extraction text are obtained, ordering the occurrence frequency of all texts in the first extraction text and the second extraction text from high to low, and selecting a preset number of texts from high to low as target extraction texts. When the target extraction text is obtained, the target extraction text is input into a target knowledge base, and a knowledge graph is stored in the target knowledge base, wherein the knowledge graph contains all data associated with the target extraction text. And according to the knowledge graph, all data associated with the current target extraction text can be searched, and the structured data corresponding to the picture to be processed is formed by all data associated with the current target extraction text and the target extraction text.
It is emphasized that to further ensure the privacy and security of the structured data, the structured data may also be stored in nodes of a blockchain.
The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
In some alternative implementations of the present embodiment, the generating module 304 includes:
the second acquisition unit is used for acquiring confidence degrees corresponding to the first extraction text and the second extraction text respectively;
the sorting unit is used for sorting the first extraction text and the second extraction text according to the confidence, and selecting a preset number of texts from high to low according to the confidence as the target extraction text.
In this embodiment, the confidence level corresponding to the first extracted text is a first confidence level, where the first confidence level is a similarity between the first extracted text and the standard keyword, which is calculated according to a preset extractor; the confidence corresponding to the second extraction text is the second confidence, and the second confidence is a predicted value of the second extraction text calculated according to the target extraction model. Specifically, when a first extraction text and a second extraction text are obtained, obtaining the similarity corresponding to the first extraction text and the standard keyword, and taking the similarity as a first confidence coefficient; and simultaneously, acquiring a predicted value corresponding to the second extraction text, wherein when the target extraction model carries out entity output on the second extraction text, the probability information of the entity corresponding to the second extraction text is the predicted value. And uniformly sequencing the first extraction text and the second extraction text according to the first confidence coefficient and the second confidence coefficient, and selecting a preset number of texts from high to low as target extraction texts.
In some optional implementations of this embodiment, the above-mentioned picture information extraction apparatus 300 further includes:
the third acquisition unit is used for acquiring a preset extraction template when the picture type is the standard file type;
And the extraction unit is used for extracting the text of the text to be processed according to the preset extraction template to obtain a target extraction text corresponding to the text to be processed.
In this embodiment, when the picture type of the picture to be processed is a standard file type, a preset extraction template is obtained, where the preset extraction template includes a text template corresponding to each standard file type. And carrying out text extraction on the text to be processed according to the preset extraction template to obtain a target extraction text corresponding to the text to be processed. Specifically, when a preset extraction template is obtained, a template field of the preset extraction template is obtained, the template field is matched with a text field in a text to be processed, a text field matched with the template field is obtained, text information under the text field in the text to be processed is obtained, and a target extraction text is obtained.
The picture information extraction device provided by the embodiment realizes efficient extraction of picture information, improves the extraction efficiency and the extraction accuracy of the picture information, further improves the decision efficiency of intelligent decision according to the extracted text information, and reduces the decision period.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only computer device 6 having components 61-63 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer device can perform man-machine interaction with a patient through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 61 includes at least one type of readable storage media including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal memory unit of the computer device 6 and an external memory device. In this embodiment, the memory 61 is generally used to store an operating system and various application software installed on the computer device 6, such as computer readable instructions of a picture information extraction method. Further, the memory 61 may be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, such as computer readable instructions for executing the picture information extraction method.
The network interface 63 may comprise a wireless network interface or a wired network interface, which network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The computer equipment provided by the embodiment realizes efficient extraction of the picture information, improves the extraction efficiency and the extraction accuracy of the picture information, further improves the decision efficiency of intelligent decision according to the extracted text information, and reduces the decision period.
The present application also provides another embodiment, namely, a computer-readable storage medium, where computer-readable instructions are stored, where the computer-readable instructions are executable by at least one processor, so that the at least one processor performs the steps of the picture information extraction method as described above.
The computer readable storage medium provided by the embodiment realizes efficient extraction of the picture information, improves the extraction efficiency and the extraction accuracy of the picture information, further improves the decision efficiency of intelligent decision according to the extracted text information, and reduces the decision period.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (7)
1. A picture information extraction method, characterized by comprising the steps of:
acquiring a picture to be processed, and performing text recognition on the picture to be processed to obtain a text to be processed;
acquiring a preset word bag model, extracting features of the text to be processed according to the preset word bag model to obtain feature data, classifying the pictures to be processed based on the feature data, and determining the picture types of the pictures to be processed;
when the picture type is a non-standard file type, inputting the characteristic data to a preset extractor to obtain a first extraction text, inputting the characteristic data to a target extraction model, and extracting to obtain a second extraction text;
performing text screening on the first extraction text and the second extraction text to obtain a target extraction text, inputting the target extraction text to a target knowledge base, and generating structured data corresponding to the picture to be processed;
the step of inputting the characteristic data to a preset extractor to extract a first extracted text comprises the following steps:
matching the feature data with word vectors corresponding to preset standard keywords according to the preset extractor to obtain hit keywords matched with the standard keywords in the text to be processed;
Performing neighborhood searching on the hit keywords to obtain the first extraction text;
the step of inputting the characteristic data to a target extraction model and extracting to obtain a second extraction text comprises the following steps:
the target extraction model comprises a two-way long-short-term memory network and a conditional random field model, the characteristic data are input into the two-way long-short-term memory network, and the characteristic vector of the text to be processed is obtained through calculation;
calculating the feature vector according to the conditional random field model to obtain entity information corresponding to the feature vector, and determining the entity information as the second extraction text;
the step of text filtering the first extracted text and the second extracted text includes:
acquiring confidence degrees corresponding to the first extraction text and the second extraction text respectively;
and sequencing the first extraction text and the second extraction text according to the confidence, and selecting a preset number of texts from high to low according to the confidence as the target extraction text.
2. The method for extracting picture information according to claim 1, wherein the step of extracting features of the text to be processed according to the preset bag-of-words model to obtain feature data includes:
Performing morphological reduction, part-of-speech tagging and numerical normalization on the text to be processed to obtain a preprocessed text of the text to be processed;
inputting the preprocessing text to the preset bag-of-words model, and calculating to obtain the characteristic data.
3. The picture information extraction method according to claim 1, wherein the step of classifying the picture to be processed based on the feature data, and determining a picture type of the picture to be processed includes:
acquiring normalized coordinate information of the text to be processed;
and classifying the picture to be processed according to the normalized coordinate information and the characteristic data to obtain the picture type of the picture to be processed.
4. The picture information extraction method according to claim 1, characterized by further comprising, after the step of determining the picture type of the picture to be processed:
when the picture type is a standard file type, acquiring a preset extraction template;
and carrying out text extraction on the text to be processed according to the preset extraction template to obtain a target extraction text corresponding to the text to be processed.
5. A picture information extracting apparatus, comprising:
The recognition module is used for acquiring a picture to be processed, and carrying out text recognition on the picture to be processed to obtain a text to be processed;
the acquisition module is used for acquiring a preset word bag model, extracting the characteristics of the text to be processed according to the preset word bag model to obtain characteristic data, classifying the pictures to be processed based on the characteristic data, and determining the picture types of the pictures to be processed;
the extraction module is used for inputting the characteristic data to a preset extractor to obtain a first extraction text when the picture type is a non-standard file type, inputting the characteristic data to a target extraction model and obtaining a second extraction text;
the generation module is used for carrying out text screening on the first extraction text and the second extraction text to obtain a target extraction text, inputting the target extraction text to a target knowledge base and generating structured data corresponding to the picture to be processed;
the step of inputting the characteristic data to a preset extractor, and extracting to obtain a first extracted text comprises the following steps: matching the feature data with word vectors corresponding to preset standard keywords according to the preset extractor to obtain hit keywords matched with the standard keywords in the text to be processed; performing neighborhood searching on the hit keywords to obtain the first extraction text;
The step of inputting the characteristic data into a target extraction model, and extracting to obtain a second extraction text comprises the following steps: the target extraction model comprises a two-way long-short-term memory network and a conditional random field model, the characteristic data are input into the two-way long-short-term memory network, and the characteristic vector of the text to be processed is obtained through calculation; calculating the feature vector according to the conditional random field model to obtain entity information corresponding to the feature vector, and determining the entity information as the second extraction text;
the text filtering the first extracted text and the second extracted text includes: acquiring confidence degrees corresponding to the first extraction text and the second extraction text respectively; and sequencing the first extraction text and the second extraction text according to the confidence, and selecting a preset number of texts from high to low according to the confidence as the target extraction text.
6. A computer device comprising a memory and a processor, wherein the memory has stored therein computer readable instructions which when executed by the processor implement the steps of the picture information extraction method of any one of claims 1 to 4.
7. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the picture information extraction method according to any of claims 1 to 4.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108280190A (en) * | 2018-01-24 | 2018-07-13 | 深圳前海大数金融服务有限公司 | Image classification method, server and storage medium |
| CN109635627A (en) * | 2018-10-23 | 2019-04-16 | 中国平安财产保险股份有限公司 | Pictorial information extracting method, device, computer equipment and storage medium |
| CN112417886A (en) * | 2020-11-20 | 2021-02-26 | 平安普惠企业管理有限公司 | Intention entity information extraction method and device, computer equipment and storage medium |
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|---|---|---|---|---|
| TWI645348B (en) * | 2015-05-26 | 2018-12-21 | 鴻海精密工業股份有限公司 | System and method for automatically summarizing images and comments within commodity-related web articles |
| US10489439B2 (en) * | 2016-04-14 | 2019-11-26 | Xerox Corporation | System and method for entity extraction from semi-structured text documents |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108280190A (en) * | 2018-01-24 | 2018-07-13 | 深圳前海大数金融服务有限公司 | Image classification method, server and storage medium |
| CN109635627A (en) * | 2018-10-23 | 2019-04-16 | 中国平安财产保险股份有限公司 | Pictorial information extracting method, device, computer equipment and storage medium |
| CN112417886A (en) * | 2020-11-20 | 2021-02-26 | 平安普惠企业管理有限公司 | Intention entity information extraction method and device, computer equipment and storage medium |
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