CN114116605A - Method and device for sequencing image documents based on semantic features and electronic equipment - Google Patents
Method and device for sequencing image documents based on semantic features and electronic equipment Download PDFInfo
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Abstract
The invention provides a method, a device and electronic equipment for sequencing image documents based on semantic features, which relate to the technical field of computer data processing and comprise the following steps: acquiring a plurality of image documents; extracting semantic features of the image documents to obtain a plurality of semantic features which correspond to the image documents one by one; judging whether a semantic feature sample related to the semantic features exists in the constructed semantic feature library or not; when the semantic feature sample related to the semantic features exists in the semantic feature library, sequencing a plurality of image documents based on the sequencing of the semantic feature sample. The image document sorting method and the image document sorting device improve the working efficiency of effective management of the image documents, release manual work, and sort the disordered image documents.
Description
Technical Field
The invention relates to the technical field of computer data processing, in particular to a method and a device for sequencing image documents based on semantic features and electronic equipment.
Background
Nowadays, cameras have become standard configurations of electronic products such as smart phones, tablet computers, and personal computers, and people can conveniently use these devices to acquire images and record some information in a photographing manner, for example, to photograph some important bills, documents, and the like. However, these photographed image documents are stored in these apparatuses, and are sorted by the apparatuses in order of information such as a file name, a photographing time, or a file size. However, the file name needs to be set manually by a user or the file name is automatically generated by the device according to the shooting sequence or time, the shooting time or the file size is very random, and the information is not necessarily connected with the shot content, so that the device cannot refer to the shot content for the storage sequence of the image documents, which causes great inconvenience for the user and cannot quickly and accurately find the required image documents.
Therefore, a method, a device and an electronic device for ranking image documents based on semantic features are provided.
Disclosure of Invention
The specification provides a method and a device for ordering image documents based on semantic features and electronic equipment.
The method for sequencing image documents based on semantic features adopts the following technical scheme that:
acquiring a plurality of image documents;
extracting semantic features of the image documents to obtain a plurality of semantic features which correspond to the image documents one by one;
judging whether a semantic feature sample related to the semantic features exists in the constructed semantic feature library or not;
when the semantic feature sample related to the semantic features exists in the semantic feature library, sequencing a plurality of image documents based on the sequencing of the semantic feature sample.
Optionally, the semantic feature extraction is performed on a plurality of image documents to obtain a plurality of semantic features corresponding to the image documents one to one, and the semantic feature extraction method includes:
identifying the image document through an image character identification model to obtain text information related to the image document;
and semantic feature extraction is carried out on the text information related to the image document through a feature extraction model, so that the semantic features which are in one-to-one correspondence with the image document are obtained.
Optionally, when the semantic feature sample related to the semantic feature exists in the semantic feature library, sorting a plurality of image documents based on the sorting of the semantic feature sample includes:
when a plurality of semantic feature samples related to the semantic features exist in the semantic feature library, performing correlation calculation on the semantic features and the semantic feature samples related to the semantic features;
and selecting the sequence of the semantic feature samples with the highest correlation degree with the semantic features as the sequence of the image documents.
Optionally, the method further includes:
when the semantic feature sample related to the semantic features does not exist in the semantic feature library, arranging the image document of the semantic feature sample without the related semantic features at the end.
Optionally, the constructed semantic feature library includes:
acquiring a plurality of image document samples;
identifying the image document sample through the image character identification model to obtain text information related to the image document sample;
semantic feature extraction is carried out on the text information related to the image document samples through the feature extraction model, and a plurality of semantic feature samples are obtained;
and binding the semantic feature samples and the related sequences thereof to obtain the semantic feature library.
The device for sequencing image documents based on semantic features adopts the following technical scheme that:
the acquisition module is used for acquiring a plurality of image documents;
the extraction module is used for extracting semantic features of the image documents to obtain a plurality of semantic features which correspond to the image documents one by one;
the judging module is used for judging whether a semantic feature sample related to the semantic features exists in the constructed semantic feature library or not;
the sorting module is used for sorting a plurality of image documents based on the sorting of the semantic feature samples when the semantic feature samples related to the semantic features exist in the semantic feature library.
Optionally, the extraction module includes:
the identification unit is used for identifying the image document through an image character identification model to obtain text information related to the image document;
and the extraction unit is used for extracting semantic features of the text information related to the image document through a feature extraction model to obtain the semantic features corresponding to the image document one by one.
Optionally, the sorting module includes:
the calculation unit is used for calculating the correlation degree of the semantic features and the semantic feature samples related to the semantic features when a plurality of semantic feature samples related to the semantic features exist in the semantic feature library;
and the selecting unit is used for selecting the sequence of the semantic feature samples with the highest correlation degree with the semantic features as the sequence of the image documents.
Optionally, the apparatus further comprises:
and the post-processing module is used for arranging the image documents of the semantic feature samples which are not related in the end when the semantic feature samples which are related to the semantic features do not exist in the semantic feature library.
Optionally, the constructed semantic feature library includes:
acquiring a plurality of image document samples;
identifying the image document sample through the image character identification model to obtain text information related to the image document sample;
semantic feature extraction is carried out on the text information related to the image document samples through the feature extraction model, and a plurality of semantic feature samples are obtained;
and binding the semantic feature samples and the related sequences thereof to obtain the semantic feature library.
The present specification also provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods described above.
In the description, the semantic features are extracted from the image documents, and then the corresponding sorting is determined according to the semantic features and the semantic feature library, so that the working efficiency of effective management of the image documents is improved, manual work is released, and the disordered image documents are sorted.
Drawings
FIG. 1 is a flowchart illustrating an overall method for ranking image documents based on semantic features according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the process of S102 in a method for ranking image documents based on semantic features according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an apparatus for ranking image documents based on semantic features according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating an extraction module 302 in an apparatus for ranking image documents based on semantic features according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an electronic device for ranking image documents based on semantic features according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, a device and electronic equipment for sequencing image documents based on semantic features.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For understanding, a specific flow of the embodiment of the present invention is described below, and referring to fig. 1, an embodiment of the method for ranking image documents based on semantic features in the embodiment of the present invention includes:
s101, acquiring a plurality of image documents;
in the embodiment, the image document is an existing image document or a new image document obtained by shooting. Specifically, contract transactions are important links of company cooperation, in order to achieve effective time of contracts, the contracts are usually photographed and scanned and stored in formats such as bmp, jpg, png, tif, gif, pcx, tga and the like, or stored in a PDF format, which is also stored out of order, and then transmitted by means of mails, faxes and the like, namely image documents.
It should be noted that the execution subject of the present invention may be a device for sorting image documents based on semantic features, and may also be a terminal or a server, which is not limited herein.
S102, extracting semantic features of the image documents to obtain a plurality of semantic features corresponding to the image documents one by one;
in the embodiment of the present invention, among a plurality of image documents stored out of order, especially image documents obtained by using a shooting method, it is inevitable that a shooting background exists in an image, and the character recognition is affected by different colors of characters in some images. Therefore, the method further comprises the step of processing images in at least one mode of trimming, rotating, stretching and image enhancing for the images in the plurality of image documents which are stored out of order, and then performing character recognition for the image documents which are processed, so as to improve the accuracy of character recognition.
And semantic feature extraction is carried out on a plurality of image documents which are stored out of order, and one image document corresponds to one semantic feature.
Optionally, referring to fig. 2, the extracting semantic features of the plurality of image documents to obtain a plurality of semantic features corresponding to the image documents one to one, includes:
s201, identifying the image document through an image character identification model to obtain text information related to the image document;
s202, semantic feature extraction is carried out on the text information related to the image document through a feature extraction model, and the semantic features which are in one-to-one correspondence with the image document are obtained.
In the embodiment, the image Character Recognition model includes an OCR (Optical Character Recognition) model, which refers to a process of inspecting a Character printed on paper by an electronic device (such as a scanner or a digital camera), determining a shape of the Character by detecting a dark and light pattern, and then translating the shape into a computer Character by a Character Recognition method; the method is characterized in that characters in a paper document are converted into an image file with a black-white dot matrix in an optical mode aiming at print characters, and the characters in the image are converted into a text format through recognition software for further editing and processing by word processing software.
The feature extraction model comprises a TF-IDF (Term Frequency-Inverse text Frequency index) model and an LDA (Latent Dirichlet Allocation) model, and the TF-IDF is a statistical method for evaluating the importance degree of a word to a file set or one of files in a corpus. The importance of a word increases in proportion to the number of times it appears in a document, but at the same time decreases in inverse proportion to the frequency with which it appears in the corpus. LDA is a typical bag-of-words model, i.e. it considers a document as a set of words, with no sequential or chronological relationship between the words. A document may contain multiple topics, with each word in the document being generated from one of the topics.
Specifically, the image documents are recognized one by one through an OCR (optical character recognition) model to obtain text information related to the image documents, and then semantic features corresponding to the image documents one by one are obtained by extracting the semantic features of the image documents through an LDA (latent Dirichlet Allocation) model, and the semantic features can well summarize the semantic content of the image documents.
S103, judging whether a semantic feature sample related to the semantic features exists in the constructed semantic feature library or not;
optionally, the constructed semantic feature library includes:
acquiring a plurality of image document samples;
identifying the image document sample through the image character identification model to obtain text information related to the image document sample;
semantic feature extraction is carried out on the text information related to the image document samples through the feature extraction model, and a plurality of semantic feature samples are obtained;
and binding the semantic feature samples and the related sequences thereof to obtain the semantic feature library.
In the specific embodiment of the embodiment, the image document samples are obtained by collecting existing image document samples, the image document samples are arranged in order, the image document samples are identified one by one through an OCR model to obtain text information related to the image document samples, then the semantic features of the image document samples are extracted through an LDA model to obtain semantic feature samples corresponding to the image document samples one by one, the semantic feature samples can well summarize the semantic content of the image document samples, the semantic feature samples are bound with the page numbers of the semantic feature samples to obtain a semantic feature library, and whether semantic feature samples related to the semantic features exist in the constructed semantic feature library is judged.
S104, when the semantic feature sample related to the semantic features exists in the semantic feature library, sequencing a plurality of image documents based on the sequencing of the semantic feature sample.
In the specific embodiment of the embodiment, when the semantic feature sample related to the semantic feature exists in the semantic feature library, the image documents are sorted according to the page number of the semantic feature sample. Specifically, the semantic feature is a, the semantic feature sample related to the semantic feature sample is a, the semantic feature sample a corresponds to page 3, and then the page of the semantic feature a is 3.
Optionally, when the semantic feature sample related to the semantic feature exists in the semantic feature library, sorting a plurality of image documents based on the sorting of the semantic feature sample includes:
when a plurality of semantic feature samples related to the semantic features exist in the semantic feature library, performing correlation calculation on the semantic features and the semantic feature samples related to the semantic features;
and selecting the sequence of the semantic feature samples with the highest correlation degree with the semantic features as the sequence of the image documents.
In the specific embodiment of this embodiment, when there are a plurality of semantic feature samples related to the semantic features in the semantic feature library, the semantic feature samples related to the semantic features are subjected to relevancy calculation, the calculation of the relevancy between each semantic feature and each semantic feature sample may be implemented by converting the semantic features and the semantic feature samples into vectors, and the calculation of the vector relevancy, that is, the relevancy between the semantic features and the semantic feature samples, is implemented by using a cosine value, an euclidean distance, a dynamic time warping method, or other relevancy calculation methods. And selecting the page sequence of the semantic feature sample with the highest correlation as the page sequence of the image document.
S105, when the semantic feature sample related to the semantic features does not exist in the semantic feature library, arranging the image document of the semantic feature sample without the related semantic features at the end.
In the embodiment, when there is no semantic feature sample related to the semantic feature in the semantic feature library, the image document without related semantic feature sample is ranked at the end.
In the above description of the method for ordering image documents based on semantic features in the embodiment of the present invention, a device for ordering image documents based on semantic features in the embodiment of the present invention is described below, please refer to fig. 3, an embodiment of the device for ordering image documents based on semantic features in the embodiment of the present invention includes:
an obtaining module 301, configured to obtain a plurality of image documents;
an extraction module 302, configured to perform semantic feature extraction on the plurality of image documents to obtain a plurality of semantic features corresponding to the image documents one to one;
the judging module 303 is configured to judge whether a semantic feature sample related to the semantic feature exists in the constructed semantic feature library;
a sorting module 304, configured to, when the semantic feature samples related to the semantic features exist in the semantic feature library, sort a plurality of image documents based on the sorting of the semantic feature samples.
Optionally, referring to fig. 4, the extracting module 302 includes:
the recognition unit 401 is configured to recognize the image document through an image character recognition model, so as to obtain text information related to the image document;
an extracting unit 402, configured to perform semantic feature extraction on the text information related to the image document through a feature extraction model, to obtain the semantic features corresponding to the image document one to one.
Optionally, the sorting module 304 includes:
the calculation unit is used for calculating the correlation degree of the semantic features and the semantic feature samples related to the semantic features when a plurality of semantic feature samples related to the semantic features exist in the semantic feature library;
and the selecting unit is used for selecting the sequence of the semantic feature samples with the highest correlation degree with the semantic features as the sequence of the image documents.
Optionally, the apparatus further comprises:
a post-module 305, configured to, when the semantic feature sample related to the semantic feature does not exist in the semantic feature library, arrange an image document without the related semantic feature sample at the end.
Optionally, the constructed semantic feature library includes:
acquiring a plurality of image document samples;
identifying the image document sample through the image character identification model to obtain text information related to the image document sample;
semantic feature extraction is carried out on the text information related to the image document samples through the feature extraction model, and a plurality of semantic feature samples are obtained;
and binding the semantic feature samples and the related sequences thereof to obtain the semantic feature library.
In the embodiment of the invention, the semantic features are extracted from the image documents, and then the corresponding sequence is determined according to the semantic features and the semantic feature library, so that the working efficiency of effective management of the image documents is improved, the manual work is released, and the disordered image documents are sequenced.
Fig. 3 and fig. 4 above describe in detail an apparatus for ranking image documents based on semantic features in an embodiment of the present invention from the perspective of a modular functional entity, and in the following, describe in detail an electronic device for ranking image documents based on semantic features in an embodiment of the present invention from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of an electronic device for sorting image documents based on semantic features, where the electronic device 500 for sorting image documents based on semantic features may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations in an electronic device 500 for ranking image documents based on semantic features. Still further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on an electronic device 500 that ranks image documents based on semantic features.
An electronic device 500 for ranking image documents based on semantic features may also include one or more priority or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and so forth. Those skilled in the art will appreciate that the configuration of an electronic device 500 for ranking image documents based on semantic features shown in FIG. 5 does not constitute a limitation of an electronic device 500 for ranking image documents based on semantic features and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, which may also be a volatile computer readable storage medium, having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the method of ordering image documents based on semantic features.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the method, the apparatus, and the electronic device described above may refer to corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for ranking image documents based on semantic features, comprising:
acquiring a plurality of image documents;
extracting semantic features of the image documents to obtain a plurality of semantic features which correspond to the image documents one by one;
judging whether a semantic feature sample related to the semantic features exists in the constructed semantic feature library or not;
when the semantic feature sample related to the semantic features exists in the semantic feature library, sequencing a plurality of image documents based on the sequencing of the semantic feature sample.
2. The method of claim 1, wherein the semantic feature extraction is performed on a plurality of image documents to obtain a plurality of semantic features corresponding to the image documents one to one, and the method comprises:
identifying the image document through an image character identification model to obtain text information related to the image document;
and semantic feature extraction is carried out on the text information related to the image document through a feature extraction model, so that the semantic features which are in one-to-one correspondence with the image document are obtained.
3. The method of claim 1, wherein when the semantic feature sample related to the semantic feature exists in the semantic feature library, ranking a number of image documents based on the ranking of the semantic feature sample comprises:
when a plurality of semantic feature samples related to the semantic features exist in the semantic feature library, performing correlation calculation on the semantic features and the semantic feature samples related to the semantic features;
and selecting the sequence of the semantic feature samples with the highest correlation degree with the semantic features as the sequence of the image documents.
4. The method of ranking image documents based on semantic features of claim 1, further comprising:
when the semantic feature sample related to the semantic features does not exist in the semantic feature library, arranging the image document of the semantic feature sample without the related semantic features at the end.
5. The method of ranking image documents based on semantic features according to claim 2, wherein the constructed semantic feature library comprises:
acquiring a plurality of image document samples;
identifying the image document sample through the image character identification model to obtain text information related to the image document sample;
semantic feature extraction is carried out on the text information related to the image document samples through the feature extraction model, and a plurality of semantic feature samples are obtained;
and binding the semantic feature samples and the related sequences thereof to obtain the semantic feature library.
6. An apparatus for ranking image documents based on semantic features, comprising:
the acquisition module is used for acquiring a plurality of image documents;
the extraction module is used for extracting semantic features of the image documents to obtain a plurality of semantic features which correspond to the image documents one by one;
the judging module is used for judging whether a semantic feature sample related to the semantic features exists in the constructed semantic feature library or not;
the sorting module is used for sorting a plurality of image documents based on the sorting of the semantic feature samples when the semantic feature samples related to the semantic features exist in the semantic feature library.
7. The apparatus for ranking image documents based on semantic features according to claim 6, wherein the extraction module comprises:
the identification unit is used for identifying the image document through an image character identification model to obtain text information related to the image document;
and the extraction unit is used for extracting semantic features of the text information related to the image document through a feature extraction model to obtain the semantic features corresponding to the image document one by one.
8. The apparatus for ranking image documents based on semantic features of claim 6, wherein the ranking module comprises:
the calculation unit is used for calculating the correlation degree of the semantic features and the semantic feature samples related to the semantic features when a plurality of semantic feature samples related to the semantic features exist in the semantic feature library;
and the selecting unit is used for selecting the sequence of the semantic feature samples with the highest correlation degree with the semantic features as the sequence of the image documents.
9. An electronic device, wherein the electronic device comprises:
a processor;
and a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-5.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-5.
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| CN117275649A (en) * | 2023-11-22 | 2023-12-22 | 浙江太美医疗科技股份有限公司 | Method and device for ordering document medical record pictures, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117275649A (en) * | 2023-11-22 | 2023-12-22 | 浙江太美医疗科技股份有限公司 | Method and device for ordering document medical record pictures, electronic equipment and storage medium |
| CN117275649B (en) * | 2023-11-22 | 2024-01-30 | 浙江太美医疗科技股份有限公司 | Method and device for ordering document medical record pictures, electronic equipment and storage medium |
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