WO2021143088A1 - Synchronous check method and apparatus for multiple certificate types, and computer device and storage medium - Google Patents

Abstract

A synchronous check method and apparatus for multiple certificate types, and a computer device and a storage medium, which relate to the field of artificial intelligence image checking. The method comprises: receiving a certificate check instruction, and acquiring an image file to be checked which includes a check number; according to the check number of the image file to be checked and a preset numbering rule, determining a check list and a certificate check model corresponding thereto, wherein the check list includes multiple certificate types; inputting all image pieces to be checked into the certificate check model, and by means of extracting certificate features, acquiring an output recognition result including a certificate type list of all the image pieces to be checked; and when the certificate types included in the check list are completely consistent with certificate types in the certificate type list, determining that the certificate type of the image file to be checked is checked such that same is up to standard, and marking the image file to be checked as a checked image file and storing same in a database. By means of the method, rapid and synchronous checking of multiple certificate types is realized, thereby improving checking efficiency and reducing costs.

Description

Synchronous detection method, device, computer equipment and storage medium for multiple certificate types

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on January 19, 2020, the application number is 202010061431.4, and the invention title is "Multi-document type synchronous detection method, device, computer equipment and storage medium", and its entire content Incorporated in this application by reference.

Technical field

This application relates to the field of artificial intelligence image detection, and in particular to a method, device, computer equipment, and storage medium for synchronous detection of multiple document types.

Background technique

With the development of the credit society, more and more application scenarios (such as: application scenarios involving finance, insurance, bad assets, and security) need to provide multiple documents (such as ID cards, household registration documents, real estate certificates, etc.) at the same time. (Bank card) image file to verify the user's identity. The inventor realized that since the review of the image data of a variety of documents is mainly through manual verification, and the image quality of the image data is uneven, the manual verification by professionals alone will undoubtedly waste huge manpower and material resources. Moreover, in the prior art, it is often only possible to input an image containing a type of ID photo and recognize it through the traditional OCR technology. The disadvantage of this solution is that the OCR technology can only perform a high-quality image of one ID in a recognition process. Effective recognition, as such, has limitations in application.

Application content

The present application provides a method, device, computer equipment, and storage medium for synchronous detection of multiple certificate types, which realize rapid synchronous detection of multiple certificate types, thereby improving detection efficiency and reducing costs.

A synchronous detection method for multiple certificate types, including:

Receiving a certificate detection instruction, and obtaining a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file;

Obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the numbering rule; the checklist includes Multiple document types;

Input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the recognition result output by the document detection model according to the document characteristics; The recognition result includes a list of certificate types of all the images to be detected, and the list of certificate types includes the certificate types identified from all the images to be detected;

When the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, confirm that the certificate type of the image file to be detected is qualified, and mark the image file to be detected as a detected image file And stored in the database.

A synchronous detection device for multiple certificate types, including:

The receiving module is used to receive the certificate detection instruction and obtain the image file to be detected containing the detection number; wherein the image file to be detected includes the image file to be detected;

The determining module is configured to obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the number rule; The checklist includes multiple types of documents;

The recognition module is used to input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the document detection model to output according to the document characteristics The recognition result; the recognition result includes a list of document types for all the image pieces to be detected, and the list of document types includes the type of document identified from all the image pieces to be detected;

The detection module is used to confirm that the certificate type of the image file to be detected is qualified when the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, and to mark the image file to be detected It is the detected image file and stored in the database.

A computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:

Receiving a certificate detection instruction, and obtaining a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file;

Obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the numbering rule; the checklist includes Multiple document types;

Input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the recognition result output by the document detection model according to the document characteristics; The recognition result includes a list of certificate types of all the images to be detected, and the list of certificate types includes the certificate types identified from all the images to be detected;

When the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, confirm that the certificate type of the image file to be detected is qualified, and mark the image file to be detected as a detected image file And stored in the database.

One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:

Receiving a certificate detection instruction, and obtaining a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file;

Obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the numbering rule; the checklist includes Multiple document types;

Input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the recognition result output by the document detection model according to the document characteristics; The recognition result includes a list of certificate types of all the images to be detected, and the list of certificate types includes the certificate types identified from all the images to be detected;

When the certificate type included in the check list is exactly the same as the certificate type in the certificate type list, confirm that the certificate type of the image file to be detected is qualified, and mark the image file to be detected

The method, device, computer equipment, and storage medium for synchronous detection of multiple certificate types provided in this application obtain the image file to be detected (including multiple image files to be detected) containing the detection number, and determine the location according to the detection number and numbering rules. A checklist of the image files to be detected and a certificate detection model corresponding to the checklist, the checklist contains multiple certificate types, all the images to be detected are input into the certificate detection model, and the certificate detection model is passed The document features are extracted from all the images to be detected, and the recognition results output by the document detection model according to the features of the documents are obtained. The recognition results include a list of the document types of all the images to be detected (including from all the said images). The certificate type identified in the image file to be detected), when the certificate type included in the checklist is exactly the same as the certificate type in the certificate type list, confirm that the certificate type of the image file to be detected is qualified and mark it at the same time It is the detected image file and stored in the database.

In this way, according to the checklist containing multiple certificate types, a certificate detection model that only targets all the certificate types included in the checklist is formed, and a one-to-one identification method is formed. This application is more targeted. Moreover, the training time of the document detection model is shorter, and the neural network structure of the document detection model is simpler and more accurate. At the same time, this application realizes rapid and synchronous detection of multiple document types, and different document types can be mixed in one image. , It is not necessary to confirm in advance that a type of document is in an image to be recognized, thereby improving the detection efficiency and reducing the cost.

The details of one or more embodiments of the present application are presented in the following drawings and description, and other features and advantages of the present application will become apparent from the description, drawings and claims.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present application more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor.

FIG. 1 is a schematic diagram of an application environment of a method for synchronous detection of multiple certificate types in an embodiment of the present application;

2 is a flowchart of a method for synchronous detection of multiple certificate types in an embodiment of the present application;

3 is a flowchart of a method for synchronous detection of multiple certificate types in another embodiment of the present application;

4 is a flowchart of step S30 of the method for synchronous detection of multiple certificate types in an embodiment of the present application;

FIG. 5 is a flowchart before step S304 of the method for synchronous detection of multiple certificate types in an embodiment of the present application;

FIG. 6 is a flowchart after step S304 of the method for synchronous detection of multiple certificate types in an embodiment of the present application;

FIG. 7 is a flowchart after step S40 of the method for synchronous detection of multiple certificate types in an embodiment of the present application;

FIG. 8 is a flowchart after step S50 of the method for synchronous detection of multiple certificate types in an embodiment of the present application;

Fig. 9 is a schematic block diagram of a device for synchronous detection of multiple certificate types in an embodiment of the present application;

Fig. 10 is a schematic diagram of a computer device in an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

The multi-certificate type synchronization detection method provided by this application can be applied in the application environment as shown in Fig. 1, in which the client (computer equipment) communicates with the server through the network. Among them, the client (computer equipment) includes, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, cameras, and portable wearable devices. The server can be implemented as an independent server or a server cluster composed of multiple servers.

In an embodiment, as shown in FIG. 2, a method for synchronous detection of multiple certificate types is provided, and the technical solution mainly includes the following steps S10-S40:

S10: Receive a certificate detection instruction, and obtain a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file.

Understandably, the document detection instruction is received to obtain the image file to be inspected, and the document inspection instruction is an instruction to select the image file to be inspected to be inspected and to be triggered after confirmation. After the document inspection instruction is received, To obtain the image file to be detected, the method of obtaining it can be set as required. For example, the method of obtaining may be to obtain the image file to be detected through the certificate detection instruction (the certificate detection instruction includes the image file to be detected). ). Obtain the image file to be detected and so on according to the storage path of the image file to be detected included in the certificate instruction.

Wherein, the to-be-detected image files are a plurality of to-be-detected image pieces that need to be detected and identified, and the to-be-detected image pieces include images of multiple certificate types, and the to-be-detected image files include the detection number, and the The detection number is a unique number assigned to the detection of multiple certificate types that determine the image file to be detected. The component factors of the detection number can be set according to requirements, for example, the component factors of the detection number can be set by Composed of numbers or/and letters, the image file to be detected may be a folder, the image file to be detected is a single file under the folder, and the attribute of the folder contains the detection number of the image file to be detected For example, the folder name of the image file to be detected contains the detection number WJJAF201909100; the image to be detected can also be a file, and the file contains multiple pages of the image to be detected, and the properties of the folder include the The detection number of the image file to be detected, for example, the file name of the image file to be detected contains the detection number WJAF201909110.

S20. Obtain a preset numbering rule, and determine a checklist of the image file to be detected and a certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the number rule; the checklist Contains a variety of document types.

Understandably, the numbering rule is a rule formulated according to the requirements to establish a corresponding relationship between the detection category and the numbering range. For example, the numbering rule defines: 1. The numbering range is WJJAF201909001 to WJJAF201909999. The corresponding detection category is September 2019 security inspection. 2. The number range is WJJBX201908001 to WJJBX201908999. The corresponding test category is the insurance test category in August 2019. According to the detection number of the image file to be detected and the numbering rule, the detection category of the image file to be detected is determined, that is, the detection of the image file to be detected is determined according to the degree of matching between the detection number and the numbering rule Category, such as: In the above example, the detection number of the image file to be detected is WJJAF201909100, and its detection number WJJAF201909100 falls within the number range WJJAF201909001 to WJJAF201909999 corresponding to the type of security detection. It can be determined that the detection category of the image file to be detected is security detection According to the detection category, the check list corresponding to the detection category is determined and recorded as the check list of the image file to be detected. At the same time, the certificate detection model corresponding to the detection category is determined and recorded as the check list corresponding to the detection category. The certificate detection model corresponding to the checklist of the image file to be detected, wherein one of the detection categories corresponds to one of the checklists, one of the detection categories corresponds to one of the certificate detection models, and the checklist is for all Describes the checklist of multiple types of document types for the image files to be detected. For example, the type of security inspection corresponds to a checklist of ID cards, social security card documents, and security certificates. At the same time, the document inspection model corresponding to the type of security inspection is determined. The credential detection model corresponding to the checklist of the image file to be detected is a deep convolutional neural network model trained and trained for different checklists. One type of the checklist corresponds to one credential detection model, and there are many There are a variety of document detection models corresponding to different checklists. The checklist includes multiple types of certificates, for example, the checklist includes ID certificates, bank card certificates, social security card certificates, etc., and the certificate detection model only identifies all certificate types included in the checklist.

In this way, the document detection model is highly pertinent, and only detects the document types included in the checklist, and the training time of the document detection model is short, and the accuracy of the document detection model is higher.

S30. Input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain a recognition result output by the document detection model according to the document characteristics The identification result includes a list of certificate types of all the image pieces to be detected, and the list of certificate types includes all the types of documents identified in the image pieces to be detected.

Understandably, all of the image pieces to be detected are input to the document detection model, the document detection model extracts all the document features in the image pieces to be detected, and the document detection model includes the deep convolution that has been trained A neural network model, where the document features include texture features extracted from the deep convolutional neural network model, and according to the document features, the document detection model outputs the recognition result of the image to be detected, and the recognition result includes A list of the certificate types of all the image files to be detected (the certificate types identified in all the image files to be detected). Preferably, the identification process of the document detection model is as follows. First, the document detection model performs gray-scale processing on all the image parts to be detected, generates gray-scale images of all the image parts to be detected, and adopts an edge detection method. Extract a number of document area images from all the gray-scale images; convert each of the document area images into a corresponding local binary pattern feature map through the local binary pattern method; secondly, convert the local binary pattern feature The image is input to the trained deep convolutional neural network model, the local binary pattern feature map is extracted through the deep convolutional neural network model, and the deep convolutional neural network model output is obtained The identification result of the identification result represents the identification type of the image of the identification area; finally, all identification types of the identification area image are written into the identification type list of all the images to be detected.

In this way, the credential detection model can simultaneously detect the image files to be detected of multiple credential types, and credential images of different credential types can be mixed in the same image to be detected, so that the image to be detected does not need to be required There is only one type of certificate for the file, which improves the detection efficiency and reduces the cost.

In one embodiment, as shown in FIG. 4, in the step S30, all the images to be detected are input into the credential detection model, and all the images to be detected are processed by the credential detection model. Extract the document characteristics, and obtain the recognition result output by the document detection model according to the document characteristics; the recognition result includes a list of the document types of all the images to be inspected, and the list of document types includes all the documents to be inspected. The types of documents identified in the image include:

S301: Obtain all the image components to be detected, perform grayscale processing on all the image components to be detected, and generate grayscale images of all the image components to be detected.

Understandably, the image to be inspected includes an image of three RGB channels (red channel, green channel, and blue channel), that is, each pixel in the image to be inspected has three channel component values. , Respectively, the R component value (red channel component value), G component value (green channel component value) and B component value (blue channel component value), and each pixel in the image to be detected Perform gray-scale processing, obtain the gray-scale value of each pixel through the weighted average method, thereby generating the gray-scale image of the image to be detected. In this way, the image to be detected in the three channels is transformed into one The grayscale image of the channel, and then only one channel is processed, reducing the processing of each channel separately.

S302: Recognize the grayscale images of all the image parts to be detected by an edge detection method, and extract a number of document area images in the grayscale images.

Understandably, the edge detection method is to identify the pixels in the image whose gray value is significantly different from the gray value around the pixel, because the image to be detected contains all the documents. The brightness on both sides of the edge is obviously different, and the difference in gray value on both sides of the edge of all the documents after gray-scale processing is more obvious, so the gray-scale image of the image to be detected is identified through the edge detection method The pixels with obvious changes in gray value are then extracted by extracting the area formed by the pixels with obvious changes in gray value (that is, the area where each document is located), and mark it as the document area in the gray image image.

S303: Convert each of the document area images into a local binary pattern feature map corresponding to each of the document area images by using a local binary mode method.

Understandably, the Local Binary Patterns (LBP) method uses each pixel as a central pixel, and the gray value corresponding to the pixel is used as the threshold of the central pixel, and the adjacent regions are The gray value corresponding to the pixel point within is compared with the threshold value of the central pixel. If the gray value of the adjacent pixel point is greater than the threshold value of the central pixel, the position of the pixel point is marked as binary code 1, otherwise it is Binary code 0, which combines the binary codes corresponding to the pixels in all adjacent areas in a clockwise order into a set of binary numbers, and the binary value is the corresponding local binary pattern feature value (LBP value). Preferably, the adjacent area is a 3×3 area of 8 pixels (excluding the central pixel), an 8-bit binary number is generated, and the range of the feature value of the local binary mode is an integer value from 0 to 255. Obtain the local binary pattern feature value corresponding to each pixel in each of the document area images by the local binary pattern method, thereby generating a local binary pattern feature map corresponding to each of the document area images That is, the local binary pattern feature values of all the pixels are arranged according to the positions of the corresponding pixels to generate a local binary pattern feature map corresponding to each of the document area images, thereby enhancing each document Texture features in the area image.

In this way, since the local binary mode method has the advantages of rotation invariance and gray level invariance, the local binary mode feature map has strong robustness to illumination. Through image analysis, there are some significant texture features in different document images. Therefore, the identification of the texture feature through the local binary pattern method to detect the type of document improves the reliability and accuracy of the identification.

S304. Input the local binary pattern feature map corresponding to each of the document area images to the trained deep convolutional neural network model in the document detection model, and pair The local binary pattern feature map extracts texture features, and obtains a recognition result output by the deep convolutional neural network model according to the texture feature, and the recognition result represents the document type of the document area image.

Understandably, the local binary pattern feature map corresponding to each of the document area images is input into the trained deep convolutional neural network model in the document detection model, so as to compare the local binary pattern features Image recognition processing, that is, the texture feature recognition of the local binary pattern feature map, the recognition result based on the texture feature statistics can be obtained, that is, the recognition result of the document area image is obtained, and the recognition result At the same time, it characterizes the document type of the image of the document area, such as ID card, bank card, social security card, and so on.

S305: Write the certificate types of all the images of the certificate area into the list of certificate types of all the images to be inspected.

Understandably, write the document types of all the images of the document area identified in the document type list. For example, if the document types are identified as ID documents and social security card documents, then the identity documents and social security card documents Write it in the list of certificate types.

In this way, the edge detection method is used to identify the pixels with obvious gray value changes in the grayscale image, so as to quickly extract the regional images of all documents. At the same time, the local binary mode method has rotation invariance and grayscale. With the advantage of degree invariance, the identification of texture features through the local binary pattern method for document type detection improves the accuracy and reliability of recognition.

In an embodiment, as shown in FIG. 5, before the step S304, that is, the input of the region image into the trained deep convolutional neural network model in the document detection model, the deep convolution The neural network model extracts the texture feature of the regional image and outputs the recognition result of the regional image according to the texture feature. Before the recognition result characterizes the certificate type of the regional image, it includes:

S3041. Obtain training image samples; where each training image sample is associated with a certificate type label.

Understandably, the training image samples are all associated with a certificate type label, and the training image samples are local binary pattern feature maps corresponding to the grayscale images of the training image samples.

S3042: Through transfer learning, the initial neural network model acquires all model parameters of the YOLO model, and determines all the model parameters as the initial parameters of the initial neural network model.

Understandably, the transfer learning (Transfer Learning, TL) uses the parameters of existing training models in other fields to apply to tasks in this field, that is, the initial neural network model obtains YOLO (You Only Only) through transfer learning. Look once) all the model parameters of the model, and then determine all the model parameters as the initial parameters of the initial neural network model.

S3043: Input the training image sample into an initial neural network model containing initial parameters.

Understandably, the initial neural network model includes the initial parameters, and the training image samples are input into the initial neural network model.

S3044: Extract texture features in the training image sample through the initial neural network model.

Understandably, the training image sample is processed by the initial neural network model, and the texture feature in the training image sample is extracted, and the texture feature includes the wave pattern feature, the pattern feature, and the abnormal spot feature.

S3045: Obtain a recognition result output by the initial neural network model according to the texture feature, and determine a loss value according to the degree of matching between the recognition result and the certificate type label.

Understandably, according to the texture feature extracted by the initial neural network model, the identification type of the training image sample is performed through the initial neural network model to obtain the recognition result of the initial neural network model, The identification result of the training image sample is compared with the certificate type label of the training image sample to determine the corresponding loss value, that is, the loss value is calculated by the loss function of the initial neural network model.

S3046: When the loss value reaches a preset convergence condition, record the initial neural network model after convergence as a trained deep convolutional neural network model.

Wherein, the preset convergence condition may be a condition that the value of the loss value is small and will not drop after 3000 calculations, that is, the value of the loss value is small and does not fall after 3000 calculations When it will drop again, stop training, and record the converged initial neural network model as the trained deep convolutional neural network model; the preset convergence condition can also be that the loss value is less than a set threshold The condition is that when the loss value is less than the set threshold, the training is stopped, and the converged initial neural network model is recorded as the trained deep convolutional neural network model.

In this way, since the input training image samples of the initial neural network model are grayscale images that have been grayscale processed, there is no need to perform complex image transformation processing on the image in the input layer, and only need to recognize texture features, so the initial neural network The model has fast processing speed and small capacity, so it can be used in portable mobile terminals with small capacity.

In an embodiment, after the step S3045, the method includes:

S3047: When the loss value does not reach a preset convergence condition, iteratively update the initial parameters of the initial neural network model, until the loss value reaches the preset convergence condition, converge the initial parameters after convergence. The neural network model is recorded as a deep convolutional neural network model that has been trained.

In this way, when the loss value does not reach the preset convergence condition, the initial parameters of the iterative initial neural network model are continuously updated to continuously move closer to the accurate recognition result, so that the accuracy of the recognition result becomes higher and higher.

Understandably, when the loss value does not meet the preset convergence condition, convergence is performed through the loss function of the initial neural network model, and the initial parameters of the initial neural network model are iteratively updated, and steps S3044 and S3044 are looped all the time. S3045: Stop training until the loss value meets the preset convergence condition, and record the initial neural network model after convergence as the deep convolutional neural network model that has been trained.

In this way, when the loss value does not reach the preset convergence condition, the initial parameters of the iterative initial neural network model are continuously updated to continuously move closer to the accurate recognition result, so that the accuracy of the recognition result becomes higher and higher.

In an embodiment, as shown in FIG. 6, after the step S304, the method includes:

S306: Determine a preset information area in the document area image according to the document type of the document area image.

Understandably, the certificate area image is acquired, and the certificate type of the certificate area image is acquired, and the preset information area in the certificate area image is determined according to the certificate type, wherein the different The certificate type matches the different preset information areas. For example, the preset information area corresponding to the ID card is a rectangular area at the position of the ID card number, and the preset information area corresponding to the bank card certificate is the rectangular area at the position of the bank card number.

S307: Cut a cropped image corresponding to each of the preset information areas from the image of the document area.

Understandably, the preset information area in the document area image is intercepted, and the intercepted image is marked as the corresponding cropped image.

S308: Transform the cut image by using an inverse binarization method to generate an inverse binarized cut image corresponding to the image of the document area.

Understandably, the inverse binarization method is to first perform gray-scale binarization processing on the cropped image to obtain a binarized image, and then perform segmentation processing on the binarized image by selecting an appropriate threshold, and finally Inverting the binarized image after the segmentation process, thereby obtaining the inverse binarized cropped image.

In this way, through the inverse binary method, the numerical features and letter characteristics of the inverse binary cropped image can be made more obvious, which improves the accuracy of recognition.

S309. Input the de-binarized cropped image into a credential number recognition model, and the credential number recognition model extracts digital features and letter features of the de-binarized cropped image corresponding to the credential area image. And according to the digital feature and the letter feature, output the recognition result of the de-binarized cropped image corresponding to the document area image, and the recognition result represents the document type associated with the document area image identity informaiton.

Understandably, the certificate number recognition model is a trained and trained deep neural network model, wherein the training method and network structure of the certificate number recognition model can be set according to requirements. Preferably, the certificate number The training mode of the recognition model is a migration learning training mode, and the network structure of the ID number recognition model is a VGG16 network structure. The digital feature and letter feature of the de-binarized crop image are extracted by the document number recognition model, and the document number recognition model recognizes the document area image according to the digital feature and the letter feature So that the certificate number recognition model outputs the certificate information associated with the certificate type of the certificate area image.

In this way, this application uses the deep neural network model to recognize the numbers and letters in the image, which speeds up the recognition speed and reduces the requirements for image quality. Because the deep neural network model recognizes through the number and letter features, input fuzzy images The effect of accurate identification can also be achieved.

S310: Write the credential information and the credential type of the regional image in a credential type list of all the image files to be detected in association with each other.

In an embodiment, as shown in FIG. 3, after the step S30, the method further includes:

S100: When the check list is inconsistent with the certificate type list, confirm that the detection of the to-be-detected image file has failed.

Understandably, the method for confirming the detection failure of the image file to be detected can be set according to requirements, for example, a corresponding failure window pops up, and the failure window indicates the "XXX certificate type of the image file to be detected" "Error" and other words.

In this way, through an obvious and corresponding detection failure prompt, the scope of credential type errors can be narrowed, and error correction of the image file to be detected can be facilitated.

S40: When the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, confirm that the certificate type of the image file to be inspected is qualified, and mark the image file to be inspected as inspected Image files are stored in the database.

Understandably, obtain the checklist and the certificate type list, compare the certificate types contained in the checklist with the certificate types in the certificate type list, and the certificate types contained in the checklist When the certificate type is completely consistent with the certificate type in the certificate type list, it is confirmed that the certificate type of the image file to be inspected is qualified, that is, the certificate type contained in the image file to be inspected meets the inspection requirements, and the image to be inspected The file is marked as a detected image file and stored in the database, and the detection result is recorded, so that the detection result corresponding to the user can be retrieved in the subsequent user authentication process.

This application obtains the image file to be inspected (including multiple image files to be inspected) containing the inspection number, and determines the check list of the image file to be inspected and the certificate detection model corresponding to the check list according to the inspection number and numbering rules , The check list contains multiple types of documents, all the image pieces to be detected are input into the document detection model, and the document features are extracted from all the image pieces to be detected through the document detection model to obtain the document detection The model outputs the recognition results based on the document features, and the recognition results include a list of document types for all the images to be inspected (including the document types identified from all the images to be inspected), in the check list When the included certificate type is completely consistent with the certificate type in the certificate type list, it is confirmed that the certificate type of the image file to be detected is qualified, and the image file to be detected is also marked as a detected image file and stored in the database.

In this way, according to the checklist containing multiple certificate types, a certificate detection model that only targets all the certificate types included in the checklist is formed, and a one-to-one identification method is formed. This application is more targeted. Moreover, the training time of the document detection model is shorter, and the neural network structure of the document detection model is simpler and more accurate. At the same time, this application realizes rapid and synchronous detection of multiple document types, and different document types can be mixed in one image. , It is not necessary to confirm in advance that a type of document is in an image to be recognized, thereby improving the detection efficiency and reducing the cost.

In an embodiment, as shown in FIG. 7, the certificate type list further includes certificate information associated with each of the certificate types identified from all the image files to be detected;

After the step S40, that is, after confirming that the certificate type of the image file to be detected is qualified, the method further includes:

S50: Obtain information to be audited associated with the detection number; the information to be audited includes certificate verification parameters corresponding to each of the certificate types included in the checklist.

Understandably, the information to be reviewed is the information associated with the detection number, and includes the certificate verification parameters corresponding to the certificate type, for example: ID card number corresponds to XXXXXXXXXXXXXXXXXX (13-digit certificate number), and bank card certificate corresponds to XXXXXXXXXXXXXXXX ( 16-digit card number), etc., wherein the certificate type included in the information to be reviewed is consistent with the certificate type included in the checklist.

S60. Compare the certificate verification parameters of the same certificate type with the certificate information, and when the certificate verification parameters match the certificate information, confirm that the certificate type corresponding to the certificate verification parameters is successfully verified .

Understandably, the certificate verification parameters and the certificate information of the same certificate type are obtained, and the certificate verification parameters of the same certificate type are compared with the certificate information, and the certificate verification parameters are compared with the certificate information. When the credential information matches, it is confirmed that the credential type corresponding to the credential verification parameter is successfully verified.

S70: When all the certificate types of the image file to be detected are verified successfully, confirm that the image file to be detected has passed the review.

Understandably, when all the document types are verified successfully, it means that the document information associated with all the document types contained in the image file to be inspected is verified successfully, and it is confirmed that the image file to be inspected has passed the verification, such as in the case of bad assets. In the industry’s user identity verification, the user’s identity is verified by providing image files of the user’s identity. After all the document types in the image files of all user identities have been verified successfully, the user’s identity verification is confirmed.

In this way, by first confirming whether the certificate type of the image file to be inspected is qualified, then confirming whether the certificate information corresponding to the certificate type of the image file to be inspected is successfully verified, and finally confirming whether the image file to be inspected has passed the audit, reducing The operation time of the certificate information verification corresponding to the certificate type of the image file to be detected in the case that the certificate type of the image file to be detected fails to be detected, thereby improving efficiency and saving costs.

In one embodiment, as shown in FIG. 8, after the step S50, that is, after the obtaining information to be audited associated with the detection number, the method includes:

S80. Compare the certificate verification parameters with the same certificate type with the certificate information, and when the certificate verification parameters do not match the certificate information, confirm the certificate type verification corresponding to the certificate verification parameters If it fails, it is confirmed that the image file to be detected is not approved.

Understandably, when the certificate verification parameter does not match the certificate information, that is, the certificate verification parameter is not equal to the certificate information, as long as one of the certificate verification parameters is not equal to the certificate information, then It is confirmed that the review result of the image file to be detected is not passed, and a failure prompt message of failing the review is prompted. The failure prompt information can be set according to requirements, for example, a prompt "XXXX document information does not match" pops up.

In this way, as long as one of the certificate types corresponding to the certificate verification parameters fails, the verification of the image file to be detected is confirmed to be unsuccessful, which reduces the number of certificate verification parameters and the certificate that are consistent with other certificate types. The time when the information is compared.

In one embodiment, there is provided a multi-certificate type synchronization detection device, which corresponds to the multi-certificate type synchronization detection method in the foregoing embodiment in a one-to-one correspondence. As shown in FIG. 9, the multi-document type synchronous detection device includes a receiving module 11, a determining module 12, an identifying module 13 and a detecting module 14. The detailed description of each functional module is as follows:

The receiving module 11 is configured to receive a certificate detection instruction, and obtain a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file;

The determining module 12 is configured to obtain a preset numbering rule, and determine a checklist of the image file to be detected and a certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the number rule; The checklist includes multiple types of certificates;

The recognition module 13 is configured to input all the image pieces to be detected into the document detection model, extract the document features from all the image pieces to be detected through the document detection model, and obtain the document detection model according to the document characteristics Output recognition result; the recognition result includes a list of certificate types of all the image files to be detected, and the list of certificate types includes the certificate types identified from all the image files to be detected;

The detection module 14 is used for confirming that the certificate type of the image file to be detected is qualified when the certificate type included in the check list is exactly the same as the certificate type in the certificate type list, and the image file to be detected Mark the detected image file and store it in the database.

In an embodiment, the device for simultaneous detection of multiple certificate types further includes:

An obtaining module, configured to obtain information to be audited associated with the detection number; the information to be audited includes certificate verification parameters corresponding to each of the certificate types included in the checklist;

The comparison module is configured to compare the certificate verification parameters of the same certificate type with the certificate information, and when the certificate verification parameters match the certificate information, confirm the certificate verification parameters corresponding to the certificate verification parameters. The certificate type verification is successful;

The confirmation module is used for confirming that the image file to be inspected has passed the review when all the certificate types of the image file to be inspected are verified successfully.

In an embodiment, the acquisition module further includes:

Comparing the certificate verification parameters with the same certificate types with the certificate information, and when the certificate verification parameters do not match the certificate information, confirming that the certificate type verification corresponding to the certificate verification parameters has failed, At the same time, it is confirmed that the image file to be detected is not approved.

In an embodiment, the identification module 13 includes:

The detection failure unit is configured to confirm that the detection of the to-be-detected image file fails when the check list is inconsistent with the certificate type list.

In an embodiment, the identification module 13 further includes:

The first acquiring unit is configured to acquire all the image parts to be inspected, perform gray-scale processing on all the image parts to be inspected, and generate gray-scale images of all the image parts to be inspected;

The first extraction unit is used to identify the gray-scale images of all the image parts to be detected by the edge detection method, and extract a number of document area images in the gray-scale images;

A conversion unit, configured to convert each of the document area images into a local binary pattern feature map corresponding to each of the document area images by using a local binary mode method;

The recognition unit is configured to input the local binary pattern feature map corresponding to each of the document region images into the trained deep convolutional neural network model in the document detection model, and pass the deep convolutional neural network model. The network model extracts the texture feature of the local binary pattern feature map, and obtains the recognition result output by the deep convolutional neural network model according to the texture feature, and the recognition result represents the document of the document area image Types of;

The successful detection unit is used to write the certificate types of all the images of the certificate area into the list of certificate types of all the images to be detected.

In an embodiment, the identification unit includes:

The second acquiring unit is configured to acquire training image samples; wherein each of the training image samples is associated with a certificate type label;

The migration unit is used to obtain all the model parameters of the YOLO model by the initial neural network model through migration learning, and determine all the model parameters as the initial parameters of the initial neural network model;

An input unit for inputting the training image sample into an initial neural network model containing initial parameters;

A second extraction unit, configured to extract texture features in the training image sample through the initial neural network model;

A determining unit, configured to obtain a recognition result output by the initial neural network model according to the texture feature, and determine a loss value according to the degree of matching between the recognition result and the certificate type label;

The training completion unit is configured to record the initial neural network model after convergence as the trained deep convolutional neural network model when the loss value reaches a preset convergence condition.

In an embodiment, the identification unit further includes:

A determining subunit, configured to determine a preset information area in the image of the image of the image of the image of the image of the image of the image of the preset of the preset information area of the preset information area according to the type of the image of the image of the image of the image of the image of the image;

A cropping subunit, configured to crop a cropped image corresponding to each of the preset information areas from the image of the document area;

A transforming subunit, configured to transform the cropped image by using an inverse binarization method to generate an inverse binarized cropped image corresponding to the image of the document area;

The recognition subunit is used to input the de-binarized cropped image into a credential number recognition model, and the credential number recognition model extracts the digital features of the de-binarized cropped image corresponding to the credential area image And the letter feature, and output the recognition result of the de-binarized cropped image corresponding to the document area image according to the number feature and the letter characteristic, and the recognition result characterizes the difference with the document area image Document information associated with the document type;

The credential information is associated with the credential type of the regional image and written into the credential type list of all the image files to be detected.

Regarding the specific limitation of the multi-certificate type synchronization detection device, please refer to the above definition of the multi-certificate type synchronization detection method, which will not be repeated here. Each module in the above-mentioned multi-certificate type synchronous detection device can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 10. The computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes computer readable instructions and internal memory. The computer readable instructions are stored with an operating system, computer readable instructions and a database. The internal memory provides an environment for the operation of the operating system and the computer-readable instructions in the computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a method for synchronous detection of multiple certificate types is realized. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and capable of running on the processor. Synchronous detection method of certificate type.

In one embodiment, one or more readable storage media storing computer readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage. Medium; the readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by one or more processors, the one or more processors implement the method for synchronous detection of multiple certificate types in the foregoing embodiment.

A person of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by computer-readable instructions to instruct relevant hardware. The computer-readable instructions can be stored in a non-volatile computer. In a readable storage medium or a volatile readable storage medium, when the computer readable instruction is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and/or volatile memory. Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not a limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In practical applications, the above functions can be allocated to different functional units and modules as needed. Module completion, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of protection of this application.

Claims (20)

1. A synchronous detection method for multiple certificate types, which includes:

   Receiving a certificate detection instruction, and obtaining a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file;

   Obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the numbering rule; the checklist includes Multiple document types;

   Input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the recognition result output by the document detection model according to the document characteristics; The recognition result includes a list of certificate types of all the images to be detected, and the list of certificate types includes the certificate types identified from all the images to be detected;

   When the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, confirm that the certificate type of the image file to be detected is qualified, and mark the image file to be detected as a detected image file And stored in the database.
2. The method for simultaneous detection of multiple certificate types according to claim 1, wherein the list of certificate types further includes certificate information associated with each of the certificate types identified from all the image files to be detected; After the certificate type of the image file to be tested is qualified, it also includes:

   Acquiring information to be audited associated with the detection number; the information to be audited includes certificate verification parameters corresponding to each of the certificate types included in the check list;

   Comparing the certificate verification parameters with the same certificate type with the certificate information, and confirming that the certificate type corresponding to the certificate verification parameter is successfully verified when the certificate verification parameters match the certificate information;

   When all the certificate types of the to-be-detected image file are verified successfully, it is confirmed that the to-be-detected image file is approved.
3. 3. The method for simultaneous detection of multiple certificate types according to claim 2, wherein said obtaining the pending information associated with said detection number comprises:

   Comparing the certificate verification parameters with the same certificate types with the certificate information, and when the certificate verification parameters do not match the certificate information, confirming that the certificate type verification corresponding to the certificate verification parameters has failed, At the same time, it is confirmed that the image file to be detected is not approved.
4. The method for simultaneous detection of multiple document types according to claim 1, wherein said inputting all said image pieces to be detected into said document detection model, and extracting document features from all said image pieces to be detected through said document detection model , After obtaining the recognition result output by the certificate detection model according to the characteristics of the certificate, the method includes:

   When the check list is inconsistent with the certificate type list, it is confirmed that the detection of the to-be-detected image file has failed.
5. The method for simultaneous detection of multiple document types according to claim 1, wherein said inputting all said image pieces to be detected into said document detection model, and extracting document features from all said image pieces to be detected through said document detection model , Obtaining the recognition result output by the certificate detection model according to the characteristics of the certificate, including:

   Acquiring all the image parts to be detected, performing gray-scale processing on all the image parts to be detected, and generating gray-scale images of all the image parts to be detected;

   Recognizing the gray-scale images of all the image parts to be detected by an edge detection method, and extracting a number of document area images in the gray-scale images;

   Converting each of the document area images into a local binary pattern feature map corresponding to each of the document area images by using a local binary mode method;

   The local binary pattern feature map corresponding to each of the document area images is input to the trained deep convolutional neural network model in the document detection model, and the deep convolutional neural network model is used to compare the The local binary pattern feature map extracts texture features, and obtains the recognition result output by the deep convolutional neural network model according to the texture feature, and the recognition result represents the document type of the document area image;

   Write the certificate types of all the images of the certificate area into the list of certificate types of all the images to be inspected.
6. The method for simultaneous detection of multiple document types according to claim 5, wherein said inputting said regional image into said document detection model is a trained deep convolutional neural network model, said deep convolutional neural network model By extracting the texture feature of the regional image, and outputting the recognition result of the regional image according to the texture feature, before the recognition result characterizes the certificate type of the regional image, it includes:

   Obtain training image samples; wherein each of the training image samples is associated with a certificate type label;

   Through transfer learning, the initial neural network model acquires all model parameters of the YOLO model, and determines all the model parameters as the initial parameters of the initial neural network model;

   Input the training image sample into an initial neural network model containing initial parameters;

   Extracting texture features in the training image sample through the initial neural network model;

   Acquiring a recognition result output by the initial neural network model according to the texture feature, and determining a loss value according to the degree of matching between the recognition result and the certificate type label;

   When the loss value reaches a preset convergence condition, the initial neural network model after convergence is recorded as the trained deep convolutional neural network model.
7. The method for simultaneous detection of multiple document types according to claim 5, wherein said inputting said regional image into said document detection model is a trained deep convolutional neural network model, said deep convolutional neural network model By extracting the texture feature of the regional image, and outputting the recognition result of the regional image according to the texture feature, after the recognition result characterizes the certificate type of the regional image, it includes:

   Determining the preset information area in the document area image according to the document type of the document area image;

   Intercepting a cropped image corresponding to each of the preset information areas from the image of the document area;

   Transforming the cut image by using an inverse binarization method to generate an inverse binarized cut image corresponding to the image of the document area;

   The de-binarized cropped image is input into a credential number recognition model, and the credential number recognition model extracts the digital and letter features of the de-binarized cropped image corresponding to the credential area image, and according to The digital feature and the letter feature output the recognition result of the inverse binarization cropped image corresponding to the document area image, and the recognition result represents the document information associated with the document type of the document area image ；

   The credential information is associated with the credential type of the regional image and written into the credential type list of all the image files to be detected.
8. A synchronous detection device for multiple certificate types, which includes:

   The receiving module is used to receive the certificate detection instruction and obtain the image file to be detected containing the detection number; wherein the image file to be detected includes the image file to be detected;

   The determining module is configured to obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the number rule; The checklist includes multiple types of documents;

   The recognition module is used to input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the document detection model to output according to the document characteristics The recognition result; the recognition result includes a list of document types for all the image pieces to be detected, and the list of document types includes the type of document identified from all the image pieces to be detected;

   The detection module is used to confirm that the certificate type of the image file to be detected is qualified when the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, and to mark the image file to be detected It is the detected image file and stored in the database.
9. A computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, wherein the processor implements the following steps when the processor executes the computer-readable instructions:

   Receiving a certificate detection instruction, and obtaining a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file;

   Obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the numbering rule; the checklist includes Multiple document types;

   Input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the recognition result output by the document detection model according to the document characteristics; The recognition result includes a list of certificate types of all the images to be detected, and the list of certificate types includes the certificate types identified from all the images to be detected;

   When the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, confirm that the certificate type of the image file to be detected is qualified, and mark the image file to be detected as a detected image file And stored in the database.
10. 9. The computer device according to claim 9, wherein the list of certificate types further includes certificate information associated with each of the certificate types identified from all the images to be detected; the confirmation of the image to be detected After the certificate type of the file is qualified, the processor further implements the following steps when executing the computer-readable instruction:

    Acquiring information to be audited associated with the detection number; the information to be audited includes certificate verification parameters corresponding to each of the certificate types included in the check list;

    Comparing the certificate verification parameters with the same certificate type with the certificate information, and confirming that the certificate type corresponding to the certificate verification parameter is successfully verified when the certificate verification parameters match the certificate information;

    When all the certificate types of the to-be-detected image file are verified successfully, it is confirmed that the to-be-detected image file is approved.
11. 10. The computer device according to claim 10, wherein after said obtaining the pending information associated with the detection number, the processor further implements the following steps when executing the computer-readable instruction:

    Comparing the certificate verification parameters with the same certificate types with the certificate information, and when the certificate verification parameters do not match the certificate information, confirming that the certificate type verification corresponding to the certificate verification parameters has failed, At the same time, it is confirmed that the image file to be detected is not approved.
12. 8. The computer device according to claim 9, wherein said inputting all said image pieces to be detected into said document detection model, and extracting document features from all said image pieces to be detected through said document detection model, to obtain said After the identification result output by the credential detection model according to the credential feature, the processor further implements the following steps when executing the computer-readable instruction:

    When the check list is inconsistent with the certificate type list, it is confirmed that the detection of the to-be-detected image file has failed.
13. 8. The computer device according to claim 9, wherein said inputting all said image pieces to be detected into said document detection model, and extracting document features from all said image pieces to be detected through said document detection model, to obtain said The identification result output by the document detection model based on the characteristics of the document includes:

    Acquiring all the image parts to be detected, performing gray-scale processing on all the image parts to be detected, and generating gray-scale images of all the image parts to be detected;

    Recognizing the gray-scale images of all the image parts to be detected by an edge detection method, and extracting a number of document area images in the gray-scale images;

    Converting each of the document area images into a local binary pattern feature map corresponding to each of the document area images by using a local binary mode method;

    The local binary pattern feature map corresponding to each of the document area images is input to the trained deep convolutional neural network model in the document detection model, and the deep convolutional neural network model is used to compare the The local binary pattern feature map extracts texture features, and obtains the recognition result output by the deep convolutional neural network model according to the texture feature, and the recognition result represents the document type of the document area image;

    Write the certificate types of all the images of the certificate area into the list of certificate types of all the images to be inspected.
14. The computer device according to claim 9, wherein said inputting said region image into said document detection model is a trained deep convolutional neural network model, and said deep convolutional neural network model extracts said The texture feature of the regional image, and output the recognition result of the regional image according to the texture feature. Before the recognition result characterizes the document type of the regional image, the processor executes the computer-readable instruction. The following steps:

    Obtain training image samples; wherein each of the training image samples is associated with a certificate type label;

    Through transfer learning, the initial neural network model acquires all model parameters of the YOLO model, and determines all the model parameters as the initial parameters of the initial neural network model;

    Input the training image sample into an initial neural network model containing initial parameters;

    Extracting texture features in the training image sample through the initial neural network model;

    Acquiring a recognition result output by the initial neural network model according to the texture feature, and determining a loss value according to the degree of matching between the recognition result and the certificate type label;

    When the loss value reaches a preset convergence condition, the initial neural network model after convergence is recorded as the trained deep convolutional neural network model.
15. The computer device according to claim 9, wherein said inputting said region image into said document detection model is a trained deep convolutional neural network model, and said deep convolutional neural network model extracts said The texture feature of the regional image, and output the recognition result of the regional image according to the texture feature. After the recognition result characterizes the document type of the regional image, the processor executes the computer-readable instruction. The following steps:

    Determining the preset information area in the document area image according to the document type of the document area image;

    Intercepting a cropped image corresponding to each of the preset information areas from the image of the document area;

    Transforming the cut image by using an inverse binarization method to generate an inverse binarized cut image corresponding to the image of the document area;

    The de-binarized cropped image is input into the ID number recognition model, and the ID number recognition model extracts the digital and letter features of the de-binarized cropped image corresponding to the image of the ID area, and according to The digital feature and the letter feature output the recognition result of the inverse binarization cropped image corresponding to the document area image, and the recognition result represents the document information associated with the document type of the document area image ；

    The credential information is associated with the credential type of the regional image and written into the credential type list of all the image files to be detected.
16. One or more readable storage media storing computer readable instructions, where when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:

    Receiving a certificate detection instruction, and obtaining a to-be-detected image file containing a detection number; wherein the to-be-detected image file includes the to-be-detected image file;

    Obtain a preset numbering rule, and determine the checklist of the image file to be detected and the certificate detection model corresponding to the checklist according to the detection number of the image file to be detected and the numbering rule; the checklist includes Multiple document types;

    Input all the image pieces to be detected into the document detection model, extract document features from all the image pieces to be detected through the document detection model, and obtain the recognition result output by the document detection model according to the document characteristics; The recognition result includes a list of certificate types of all the images to be detected, and the list of certificate types includes the certificate types identified from all the images to be detected;

    When the certificate type included in the check list is completely consistent with the certificate type in the certificate type list, confirm that the certificate type of the image file to be detected is qualified, and mark the image file to be detected as a detected image file And stored in the database.
17. The readable storage medium according to claim 16, wherein the list of certificate types further includes certificate information associated with each of the certificate types identified from all the images to be detected; the confirmation of the pending After the certificate type of the detected image file is qualified, when the computer-readable instructions are executed by one or more processors, the one or more processors further execute the following steps:

    Acquiring information to be audited associated with the detection number; the information to be audited includes certificate verification parameters corresponding to each of the certificate types included in the check list;

    Comparing the certificate verification parameters with the same certificate type with the certificate information, and confirming that the certificate type corresponding to the certificate verification parameter is successfully verified when the certificate verification parameters match the certificate information;

    When all the certificate types of the to-be-detected image file are verified successfully, it is confirmed that the to-be-detected image file is approved.
18. The readable storage medium according to claim 17, wherein, after the obtaining of the pending information associated with the detection number, when the computer-readable instruction is executed by one or more processors, the one or Multiple processors also perform the following steps:

    Comparing the certificate verification parameters with the same certificate types with the certificate information, and when the certificate verification parameters do not match the certificate information, confirming that the certificate type verification corresponding to the certificate verification parameters has failed, At the same time, it is confirmed that the image file to be detected is not approved.
19. The readable storage medium according to claim 16, wherein said inputting all said images to be detected into said credential detection model, extracting credential features from all said images to be detected through said credential detection model, and obtaining After the identification result output by the credential detection model according to the credential features, when the computer-readable instructions are executed by one or more processors, the one or more processors further execute the following steps:

    When the check list is inconsistent with the certificate type list, it is confirmed that the detection of the to-be-detected image file has failed.
20. The readable storage medium according to claim 16, wherein said inputting all said images to be detected into said credential detection model, extracting credential features from all said images to be detected through said credential detection model, and obtaining The recognition result output by the certificate detection model according to the characteristics of the certificate includes:

    Acquiring all the image parts to be detected, performing gray-scale processing on all the image parts to be detected, and generating gray-scale images of all the image parts to be detected;

    Recognizing the gray-scale images of all the image parts to be detected by an edge detection method, and extracting a number of document area images in the gray-scale images;

    Converting each of the document area images into a local binary pattern feature map corresponding to each of the document area images by using a local binary mode method;

    The local binary pattern feature map corresponding to each of the document area images is input to the trained deep convolutional neural network model in the document detection model, and the deep convolutional neural network model is used to compare the The local binary pattern feature map extracts texture features, and obtains the recognition result output by the deep convolutional neural network model according to the texture feature, and the recognition result represents the document type of the document area image;

    Write the certificate types of all the images of the certificate area into the list of certificate types of all the images to be inspected.

Images