WO2018090641A1

WO2018090641A1 - Method, apparatus and device for identifying insurance policy number, and computer-readable storage medium

Info

Publication number: WO2018090641A1
Application number: PCT/CN2017/091308
Authority: WO
Inventors: 马进; 王健宗; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2016-11-15
Filing date: 2017-06-30
Publication date: 2018-05-24
Also published as: CN106557747B; CN106557747A

Abstract

A method, apparatus and device for identifying an insurance policy number, and a computer-readable storage medium. The method for identifying an insurance policy number comprises: after receiving an insurance policy picture, identifying an insurance type corresponding to the insurance policy picture, and extracting a target row character region corresponding to the insurance policy number in the insurance policy picture based on a predetermined positional relationship between an insurance type and an insurance policy number in the insurance policy picture (S1); and invoking a first identification model that is generated by training in advance to carry out character identification on the target row character region, so as to identify an insurance policy number included in the target row character region, and storing the identified insurance policy number and the insurance policy picture in an associated manner (S2). During the whole operation process, almost no manual intervention is needed, and an insurance policy number can be rapidly acquired from a lot of insurance policy pictures, so that the workload is substantially reduced and the working efficiency is improved.

Description

Method, device, device and computer readable storage medium for identifying insurance policy number

Priority claim

This application is based on the priority of the Chinese Patent Application entitled "Method and Apparatus for Identifying Insurance Policy Numbers" filed on November 15, 2016, with the application number of CN201611005112.1, which is filed on November 15, 2016. The manner of reference is incorporated in the present application.

Technical field

The present invention relates to the field of image processing technologies, and in particular, to a method, an apparatus, a device, and a computer readable storage medium for identifying an insurance policy number.

Background technique

At present, there are many insurance policies in insurance companies that are stored in the form of pictures. Each insurance policy or insurance policy picture has a unique insurance policy number corresponding to it. For the insurance policy picture, the insurance policy number is the key information, and the staff generally needs to perform the operation of searching or inquiring the insurance information according to the insurance policy number. If you want to get the picture information such as the insurance policy number from multiple insurance policy pictures, it is usually obtained by manual operation. When the number of insurance policy pictures is large, the staff cannot quickly get each insurance policy. The insurance policy number of the picture results in a very large workload and reduces work efficiency.

Summary of the invention

The object of the present invention is to provide a method, device, device and computer readable storage medium for identifying an insurance policy number, which aims to quickly obtain an insurance policy number from a large number of insurance policy pictures, reduce workload and improve work efficiency.

To achieve the above object, the present invention provides a method for identifying an insurance policy number, and the method for identifying an insurance policy number includes:

S1. After receiving the insurance policy picture, identify the insurance type corresponding to the insurance policy picture, and extract the insurance policy number based on the predetermined insurance type and the positional relationship of the insurance policy number in the insurance policy picture. The corresponding target line character area in a single picture;

S2, calling a first recognition model generated by the pre-training to perform character recognition on the target line character region to identify an insurance policy number included in the target line character region, and identify the insurance policy number and the insurance policy The image is stored in association.

In order to achieve the above object, the present invention also provides an apparatus for identifying an insurance policy number, and the apparatus for identifying an insurance policy number includes:

a first extraction module, configured to identify an insurance type corresponding to the insurance policy picture after receiving the insurance policy picture, and extract the insurance policy based on a predetermined insurance type and a positional relationship of the insurance policy number in the insurance policy picture The number is in the corresponding target line character area in the insurance policy picture;

a first identification module, configured to invoke a first recognition model generated by the pre-training to perform character recognition on the target line character region, to identify an insurance policy number included in the target line character region, And identifying the insurance policy number and storing the insurance policy picture.

To achieve the above object, the present invention also provides an apparatus for identifying an insurance policy number, the apparatus comprising the memory, a processor, and an identification insurance number stored on the memory and operable on the processor The program, when the program for identifying the insurance policy number is executed by the processor, implements the following steps:

In order to achieve the above object, the present invention also provides a computer readable storage medium having stored thereon a program for identifying an insurance policy number, wherein the program for identifying an insurance policy number is executed by a processor to implement the following steps :

The invention has the beneficial effects that the invention firstly identifies the insurance type of the insurance policy picture, and through the positional relationship between the insurance type and the insurance policy number in the insurance policy picture, the target line character area corresponding to the insurance policy number can be extracted, and then called. The first recognition model generated by the pre-training is used to identify the insurance policy number in the target line character area, and the entire operation process requires almost no manual participation, and the insurance policy number can be quickly obtained from a large number of insurance policy pictures, thereby greatly reducing the workload. ,Improve work efficiency.

DRAWINGS

1 is a schematic flow chart of a first embodiment of a method for identifying an insurance policy number according to the present invention;

2 is a schematic flow chart of a second embodiment of a method for identifying an insurance policy number according to the present invention;

3 is a schematic flow chart of a third embodiment of a method for identifying an insurance policy number according to the present invention;

4 is a schematic flow chart of a fourth embodiment of a method for identifying an insurance policy number according to the present invention;

FIG. 5 is a schematic flowchart diagram of a fifth embodiment of a method for identifying an insurance policy number according to the present invention; FIG.

6 is a schematic structural diagram of a first embodiment of an apparatus for identifying an insurance policy number according to the present invention;

7 is a schematic structural diagram of a second embodiment of an apparatus for identifying an insurance policy number according to the present invention;

8 is a schematic structural diagram of a third embodiment of an apparatus for identifying an insurance policy number according to the present invention;

9 is a block diagram showing the hardware structure of an apparatus for identifying an insurance policy number according to the present invention.

detailed description

The principles and features of the present invention are described in the following with reference to the accompanying drawings.

As shown in FIG. 1 , FIG. 1 is a schematic flowchart of an embodiment of a method for identifying an insurance policy number according to the present invention. The method for identifying an insurance policy number includes the following steps:

Step S1, after receiving the insurance policy picture, identifying the insurance type corresponding to the insurance policy picture, and extracting the insurance policy number based on the predetermined insurance type and the positional relationship of the insurance policy number in the insurance policy picture. The corresponding target line character area in the policy picture.

In this embodiment, there are various types of insurance policies or insurance policy pictures, such as a car insurance policy, a life insurance policy, and an accident insurance policy. Each insurance policy is an insurance type. For different types of insurance policies, the insurance policy numbers are not in the same position. For example, some insurance policy numbers are located above the upper right corner of the insurance policy, and some insurance policy numbers are located to the left of the upper right corner of the insurance policy. In this embodiment, different types of insurance policies are stored in association with the location of the insurance policy number. After receiving the insurance policy picture, the insurance type of the insurance policy image is first identified. The specific identification process is: The size, color and content layout of the single are comprehensively identified to determine the type of insurance to which the insurance policy picture belongs. In addition, the insurance type to which the insurance policy picture belongs may be identified by other methods, for example, by identifying the content of the insurance picture. Information to determine the type of insurance to which it belongs.

After identifying the type of insurance to which the insurance policy belongs, extracting the insurance ticket number corresponding to the target line character area in the insurance policy picture based on the positional relationship between the insurance type and the insurance policy number in the insurance policy picture, and extracting the insurance After the target line character area corresponding to the single number, only the number in the target line character area needs to be further recognized to obtain the insurance policy number.

Step S2, calling the first recognition model generated by the pre-training to perform character recognition on the target line character region to identify the insurance policy number included in the target line character region, and identify the insurance policy number and the insurance A single picture is stored in association.

In this embodiment, the first recognition model is generated in advance, and the first recognition model may be one of a plurality of models related to image processing. Preferably, the first recognition model is a time recurrent neural network model. Calling the first recognition model to perform character recognition on the target line character region to identify each character in the target line character region. Generally, the insurance policy number is a number, and when all the characters are recognized, an insurance policy can be obtained. number.

After the insurance policy number is identified, the insurance policy number is stored in association with the insurance policy picture, so that when the employee searches or retrieves the insurance policy number, the insurance policy number can be used to query or retrieve the associated insurance number. Insurance policy picture.

Compared with the prior art, the embodiment first identifies the insurance type of the insurance policy picture, and by using the positional relationship between the insurance type and the insurance policy number in the insurance policy picture, the target line character area corresponding to the insurance policy number can be extracted, and then Calling the first recognition model generated by the pre-training to identify the insurance policy number in the target line character area, the whole operation process requires almost no human participation, and can quickly obtain the insurance policy number from a large number of insurance policy pictures, thereby greatly reducing the work. Quantity, improve work efficiency.

In a preferred embodiment, as shown in FIG. 2, on the basis of the above embodiment of FIG. 1, the above step S1 is replaced by:

In step S0, after receiving the insurance policy picture, the second recognition model generated by the pre-training is invoked to identify the target line character area in which the insurance policy number is located in the insurance policy picture.

In this embodiment, the second recognition model is generated in advance, and the second recognition model may be one of a plurality of models related to image processing. Preferably, the second recognition model is a convolutional neural network model. After receiving the insurance policy picture, the second identification model is called to locate and identify the insurance policy picture to identify the target line character area where the insurance policy number is located.

Different from the above embodiment, the present embodiment identifies the target line character area where the insurance policy number is located in the insurance policy picture by calling the second identification model. Since the second recognition model is obtained by training a large amount of data, it is possible to Accurately identify the target line character area.

In a preferred embodiment, as shown in FIG. 3, on the basis of the foregoing embodiment of FIG. 2, before the step S0, the method further includes:

Step S01, obtaining a preset number of insurance policy sample pictures, using the insurance policy sample picture including the insurance policy number as the first picture set, and using the insurance policy sample picture not including the insurance policy number as the second picture set;

Step S02, extracting, from the first picture set and the second picture set, a first preset proportion of the insurance order sample picture as the sample picture to be trained, and collecting the remaining insurance policies in the first picture set and the second picture set. The sample image is used as a sample image to be verified;

Step S03: performing model training using each sample picture to be trained to generate the convolutional neural network model, and verifying the generated convolutional neural network model by using each sample picture to be verified;

In step S04, if the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the number of the insurance ticket sample pictures is increased to re-train and verify.

In this embodiment, the second recognition model is a convolutional neural network model. Before using the convolutional neural network model to identify the target line character region, the convolutional neural network model is first trained to generate:

Obtain a preset number of insurance policy sample images, for example, obtain 100,000 insurance policy sample images, where the insurance policy sample image including the insurance policy number is used as the first photo collection, and the insurance policy sample image not including the insurance policy number As a second collection of pictures. The first picture set and the second picture set respectively extract the first preset proportion of the insurance order sample picture as the sample picture to be trained, for example, the first picture set and the second picture set respectively extract 80% of the insurance sample picture as the to-be-trained The sample picture, the first picture set and the remaining insurance order sample pictures in the second picture set are used as sample pictures to be verified.

When the convolutional neural network model is trained for the first time, the parameters of the convolutional neural network model are trained by default parameters, and the parameters are continuously adjusted during the training process. After the training of the convolutional neural network model is generated, each of the to-be-verified models is used. The sample picture is used to verify the generated convolutional neural network model. If the verification pass rate is greater than or equal to a preset threshold, for example, the pass rate is greater than or equal to 98%, the training ends, and the convolutional neural network model obtained by the training is used to identify the target. The model of the line character area; if If the verification pass rate is less than the preset threshold, for example, less than 98%, the number of the insurance ticket sample pictures is increased, and the above steps S01, S02, S03, and S04 are performed again until the verification pass rate is greater than or equal to the preset threshold.

In a preferred embodiment, as shown in FIG. 4, based on the foregoing embodiment of FIG. 1, before the step S2, the method further includes:

S21: Obtain a preset number of insurance ticket number sample images, extract a second preset proportion of insurance policy number sample images as a training set, and test the remaining insurance policy number sample images in the preset number of insurance policy number sample images as a test set;

S22: Input the insurance ticket number sample image in the training set to the time recurrent neural network model for model training, and use the insurance ticket number sample image in the test set to perform the trained time recurrent neural network model every preset time. Testing to assess the recognition effect of the trained time recurrent neural network model;

S23. After each test, calculate the recognition error of the trained time recurrent neural network model. If the recognition error converges, the training is completed, otherwise the model parameters of the time recurrent neural network model are adjusted to re-train and test.

In this embodiment, the first recognition model is a time recurrent neural network model, and the time recurrent neural network model is first trained to identify the characters in the target line character region by using the time recurrent neural network model:

Obtain a preset number of insurance ticket number sample images, for example, obtain 100,000 insurance policy number sample images, wherein the insurance policy number sample image only contains one line of numbers, the line number is the insurance policy number, the font is black, and the background is white. The name of each insurance policy number sample picture can be named as the insurance policy number included. Extracting a second preset ratio of the insurance order number sample picture as a training set, and using the remaining number of insurance policy number sample pictures in the preset number of insurance policy number sample pictures as a test set, the number of insurance order number sample pictures in the training set is greater than The number of insurance ticket number sample pictures in the test set, for example, 80% of the insurance order number sample pictures in the insurance order number sample picture is used as the training set, and the remaining 20% of the insurance number number sample pictures are used as the test set.

In the first training time recursive neural network model, the parameters of the time recurrent neural network model are trained by default parameters, and the insurance order number sample images in the training set are input into the time recurrent neural network model for training, The preset time uses the insurance policy number sample picture in the test set to test the trained time recurrent neural network model. For example, the training set is tested with the test set after every 1000 iterations to evaluate the trained time recurrent neural network model. Identify the effect.

In the test, the trained model is used to identify the insurance policy number of the insurance policy number sample image in the test set, and compare the identification result with the used name of the insurance ticket number sample picture (the insurance policy number sample picture utilizes the The insurance policy number is named to evaluate the recognition effect of the trained time recurrent neural network model.

Specifically, after each test, the recognition error of the trained time recurrent neural network model is calculated, and the identification error is the identified insurance policy number and the name of the insurance ticket number sample picture. The edit distance of the insurance policy number used, if the recognition error converges, the training is completed, and the trained time recurrent neural network model is used as a model for identifying characters in the target line character region; if the recognition error is divergent, the time recursive nerve is adjusted. The model parameters of the network model are re-executed in steps S21, S22, and S23 described above until the recognition error converges.

In a preferred embodiment, as shown in FIG. 5, based on the foregoing embodiment, after the step S2, the method further includes:

S3. After receiving the search request for carrying the insurance policy number issued by the terminal, search for the insurance policy picture associated with the insurance policy number, and send the found insurance policy picture to the terminal.

In this embodiment, when searching or viewing the information in the insurance policy, the user first sends a search request carrying the insurance policy number to the device where the insurance policy number is located, and after receiving the search request, the device according to the search request The insurance policy number matches the stored insurance policy number. After matching the consistent insurance policy number, the insurance policy image associated with the matched insurance policy number is fed back to the terminal, so that the terminal user can view the insurance policy picture. details.

As shown in FIG. 6, FIG. 6 is a schematic structural diagram of an apparatus for identifying an insurance policy number according to an embodiment of the present invention. The apparatus for identifying an insurance policy number includes:

The first extraction module 101 is configured to identify an insurance type corresponding to the insurance policy picture after receiving the insurance policy picture, and extract the insurance based on a predetermined insurance type and a positional relationship of the insurance policy number in the insurance policy picture. The single number is in the corresponding target line character area in the insurance policy picture;

The first identification module 102 is configured to call a first recognition model generated by the pre-training to perform character recognition on the target line character region to identify an insurance policy number included in the target line character region, and identify an insurance policy The number is stored in association with the insurance policy picture.

In this embodiment, the first recognition model is generated in advance, and the first recognition model may be one of a plurality of models related to image processing. Preferably, the first recognition model is a time recurrent neural network module. type. Calling the first recognition model to perform character recognition on the target line character region to identify each character in the target line character region. Generally, the insurance policy number is a number, and when all the characters are recognized, an insurance policy can be obtained. number.

In a preferred embodiment, as shown in FIG. 7, on the basis of the foregoing embodiment of FIG. 6, the first extraction module 101 is replaced with: a second identification module 100, after receiving the insurance policy picture, The second recognition model generated by the pre-training is invoked to identify the target line character region in which the insurance policy number is located in the insurance policy picture.

In a preferred embodiment, based on the foregoing embodiment, the second identification model is a convolutional neural network model, and the insurance number identification device further includes:

An acquiring module, configured to obtain a preset number of insurance ticket sample pictures, use an insurance policy sample picture including an insurance policy number as a first picture set, and use an insurance policy sample picture that does not include an insurance policy number as a second picture set;

a second extraction module, configured to separately extract a first preset ratio of the insurance ticket sample image from the first image set and the second image set as a sample image to be trained, and focus the first image set and the second image set The remaining insurance policy sample image is used as the sample image to be verified;

a first training module, configured to perform model training by using each sample image to be trained, to generate the convolutional neural network model, and verify the generated convolutional neural network model by using each sample image to be verified;

The first processing module is configured to: if the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the number of the insurance ticket sample pictures is increased to perform training and verification again.

Obtain a preset number of insurance policy sample images, for example, obtain 100,000 insurance policy sample images, where the insurance policy sample image including the insurance policy number is used as the first photo collection, and the insurance policy sample image not including the insurance policy number As a second collection of pictures. The first picture set and the second picture set respectively extract the first preset proportion of the insurance order sample picture as the sample picture to be trained, for example, the first picture set and the second picture set respectively extract 80% of the insurance sample picture as the to-be-trained Sample map The slice, the first picture set and the remaining insurance order sample pictures in the second picture set are used as sample pictures to be verified.

When the convolutional neural network model is trained for the first time, the parameters of the convolutional neural network model are trained by default parameters, and the parameters are continuously adjusted during the training process. After the training of the convolutional neural network model is generated, each of the to-be-verified models is used. The sample picture is used to verify the generated convolutional neural network model. If the verification pass rate is greater than or equal to a preset threshold, for example, the pass rate is greater than or equal to 98%, the training ends, and the convolutional neural network model obtained by the training is used to identify the target. A model of a line character area; if the verification pass rate is less than a preset threshold, for example, less than 98%, the number of insurance ticket sample pictures is increased until the verification pass rate is greater than or equal to a preset threshold.

In a preferred embodiment, based on the foregoing embodiment, the identification device of the insurance policy number further includes:

The third extraction module is configured to obtain a preset number of insurance ticket number sample images, extract a second preset ratio insurance ticket number sample image as a training set, and replace the remaining insurance policy in the preset number of insurance policy number sample images The number sample picture is used as a test set;

a second training module, configured to input the insurance ticket number sample picture in the training set to a time recurrent neural network model for model training, and use the insurance ticket number sample picture in the test set to train the time every preset time The recurrent neural network model is tested to evaluate the recognition effect of the trained time recurrent neural network model;

a second processing module, configured to calculate a recognition error of the trained time recurrent neural network model after each test, if the recognition error converges, the training is completed, otherwise the model parameters of the time recurrent neural network model are adjusted, To re-train and test.

Specifically, after each test, the recognition error of the trained time recurrent neural network model is calculated, and the identification error is an edit of the identified insurance policy number and the insurance policy number used for naming the insurance ticket number sample picture. Distance, if the recognition error converges, the training is completed, and the trained time recurrent neural network model is used as a model for identifying characters in the target line character region; if the recognition error is diverged, the model parameters of the time recurrent neural network model are adjusted until identification The error converges.

In a preferred embodiment, as shown in FIG. 8, on the basis of the foregoing embodiment of FIG. 6, the identification device for the insurance policy number further includes:

The search module is configured to: after receiving the search request for carrying the insurance policy number issued by the terminal, search for an insurance policy picture associated with the insurance policy number, and send the found insurance policy picture to the terminal.

Referring to FIG. 9, a block diagram of a hardware structure of an apparatus for identifying an insurance policy number according to the present invention is shown.

In this embodiment, the device for identifying the insurance policy number may be a PC (Personal Computer), or may be a portable terminal device having a display function such as a smart phone, a tablet computer, an e-book reader, or a portable computer.

The device for identifying the policy number includes a memory 11, a processor 12, a communication bus 13, and a network interface 14.

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the device that identifies the insurance policy number, such as the hard disk of the device that identifies the insurance policy number. The memory 11 may also be an external storage device of the device that identifies the insurance policy number in other embodiments, such as a plug-in hard disk equipped on a device that identifies the insurance policy number, a smart memory card (SMC), a secure digital number. (Secure Digital, SD) card, flash card, etc. Further, the memory 11 may also include both an internal storage unit of the device that identifies the insurance policy number and an external storage device. The memory 11 can be used not only for storing application software and various types of data installed in a device for identifying an insurance policy number, such as a code for a program for identifying an insurance policy number, but also for temporarily storing data that has been output or is to be output.

The processor 12 may be a central processing unit (Central Processing Unit, in some embodiments). A CPU, controller, microcontroller, microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as executing a program for identifying an insurance policy number.

Communication bus 13 is used to implement connection communication between these components.

The network interface 14 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the device and other electronic devices.

Figure 9 shows only the device for identifying the policy number with the components 11-14 and the program identifying the policy number, but it should be understood that not all of the illustrated components are required to be implemented, alternative implementations may be more or more Less components.

Optionally, the device may further include a user interface, the user interface may include a display, an input unit such as a keyboard, and the optional user interface may further include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like. The display may also be referred to as a display or display unit, as appropriate, for displaying information processed in the device identifying the insurance policy number and a user interface for displaying the visualization.

In the apparatus for identifying an insurance policy number shown in FIG. 9, a program for identifying an insurance policy number is stored in the memory 11, the network interface 14 is mainly used to connect to a server, and performs data communication with the server; the processor 12 executes the storage in the memory 11. The procedure for identifying the policy number is as follows:

Further, the step S2 can be replaced by:

S0. After receiving the insurance policy picture, the second recognition model generated by the pre-training is invoked to identify the target line character area in which the insurance policy number is located in the insurance policy picture.

Further, the second identification model is a convolutional neural network model, and the processor is further configured to execute the program for identifying the insurance policy number, so as to implement the following steps before step S0:

S01: Obtain a preset number of insurance policy sample pictures, use an insurance policy sample picture including an insurance policy number as a first picture set, and use an insurance policy sample picture that does not include an insurance policy number as a second picture set;

S02, extracting, from the first picture set and the second picture set, a first preset proportion of insurance policy sample pictures as sample pictures to be trained, and collecting remaining insurance policy samples in the first picture set and the second picture set The picture serves as a sample picture to be verified;

S03: performing model training by using each sample image to be trained to generate the convolutional neural network model, and verifying the generated convolutional neural network model by using each sample image to be verified;

S04. If the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the protection is increased. The number of insurance sample images to re-train and verify.

Further, the first recognition model is a time recurrent neural network model, and the processor is further configured to execute the program for identifying the insurance policy number, so as to implement the following steps before step S2:

Further, the processor is further configured to execute the program for identifying the insurance policy number, so after step S2, further implementing the following steps:

The present invention also provides a computer readable storage medium having stored thereon a program for identifying an insurance policy number, the program for identifying the insurance policy number being executed by the processor to:

Further, the step S2 can be replaced by:

Further, when the program for identifying the insurance policy number is executed by the processor, the following steps are further implemented before step S0:

S03, performing model training by using each sample image to be trained to generate the convolutional neural network Network model, and use the sample images to be verified to verify the generated convolutional neural network model;

S04. If the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the number of the insurance ticket sample pictures is increased to perform training and verification again.

Further, when the program for identifying the insurance policy number is executed by the processor, the following steps are further implemented before step S2:

Further, when the program for identifying the insurance policy number is executed by the processor, the following steps are further implemented after step S2:

The specific embodiment of the computer readable storage medium of the present invention is substantially the same as the method embodiment for identifying the insurance policy number, and is not described herein.

The serial numbers of the embodiments of the present invention are merely for the description, and do not represent the advantages and disadvantages of the embodiments. Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present invention, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a cell phone, a computer, a server, or a network device, etc.) to perform the methods described in various embodiments of the present invention.

The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalents, improvements, etc., which are within the spirit and scope of the present invention, should be included in the protection of the present invention. Within the scope.

Claims

A method for identifying an insurance policy number, wherein the method for identifying an insurance policy number includes:

S1. After receiving the insurance policy picture, identify the insurance type corresponding to the insurance policy picture, and extract the insurance policy number based on the predetermined insurance type and the positional relationship of the insurance policy number in the insurance policy picture. The corresponding target line character area in a single picture;

S2, calling a first recognition model generated by the pre-training to perform character recognition on the target line character region to identify an insurance policy number included in the target line character region, and identify the insurance policy number and the insurance policy The image is stored in association.
The method of identifying an insurance policy number according to claim 1, wherein said step S1 is replaced by:

S0. After receiving the insurance policy picture, the second recognition model generated by the pre-training is invoked to identify the target line character area in which the insurance policy number is located in the insurance policy picture.
The method for identifying an insurance policy number according to claim 2, wherein the second recognition model is a convolutional neural network model, and the step S0 further comprises:

S01: Obtain a preset number of insurance policy sample pictures, use an insurance policy sample picture including an insurance policy number as a first picture set, and use an insurance policy sample picture that does not include an insurance policy number as a second picture set;

S02, extracting, from the first picture set and the second picture set, a first preset proportion of insurance policy sample pictures as sample pictures to be trained, and collecting remaining insurance policy samples in the first picture set and the second picture set The picture serves as a sample picture to be verified;

S03: performing model training by using each sample image to be trained to generate the convolutional neural network model, and verifying the generated convolutional neural network model by using each sample image to be verified;

S04. If the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the number of the insurance ticket sample pictures is increased to perform training and verification again.
The method for identifying an insurance policy number according to any one of claims 1 to 3, wherein the first recognition model is a time recurrent neural network model, and the step S2 further comprises:

S21: Obtain a preset number of insurance ticket number sample images, extract a second preset proportion of insurance policy number sample images as a training set, and test the remaining insurance policy number sample images in the preset number of insurance policy number sample images as a test set;

S22: Input the insurance ticket number sample image in the training set to the time recurrent neural network model for model training, and use the insurance ticket number sample image in the test set to perform the trained time recurrent neural network model every preset time. Test to assess the time of recurrent nerves trained The recognition effect of the network model;

S23. After each test, calculate the recognition error of the trained time recurrent neural network model. If the recognition error converges, the training is completed, otherwise the model parameters of the time recurrent neural network model are adjusted to re-train and test.
The method for identifying an insurance policy number according to claim 4, wherein the step S2 further comprises:

S3. After receiving the search request for carrying the insurance policy number issued by the terminal, search for the insurance policy picture associated with the insurance policy number, and send the found insurance policy picture to the terminal.
An apparatus for identifying an insurance policy number, wherein the apparatus for identifying an insurance policy number comprises:

a first extraction module, configured to identify an insurance type corresponding to the insurance policy picture after receiving the insurance policy picture, and extract the insurance policy based on a predetermined insurance type and a positional relationship of the insurance policy number in the insurance policy picture The number is in the corresponding target line character area in the insurance policy picture;

a first identification module, configured to invoke a first recognition model generated by the pre-training to perform character recognition on the target line character region, to identify an insurance policy number included in the target line character region, and identify the insurance policy number Associated with the insurance policy picture for storage.
The apparatus for identifying an insurance policy according to claim 6, wherein the first extraction module is replaced by: a second identification module, configured to invoke a second identification generated by the pre-training after receiving the insurance policy picture The model identifies the target line character area in which the insurance policy number is located in the insurance policy picture.
The apparatus for identifying an insurance policy number according to claim 7, wherein the second identification model is a convolutional neural network model, and the identification device of the insurance policy number further comprises:

An acquiring module, configured to obtain a preset number of insurance ticket sample pictures, use an insurance policy sample picture including an insurance policy number as a first picture set, and use an insurance policy sample picture that does not include an insurance policy number as a second picture set;

a second extraction module, configured to separately extract a first preset ratio of the insurance ticket sample image from the first image set and the second image set as a sample image to be trained, and focus the first image set and the second image set The remaining insurance policy sample image is used as the sample image to be verified;

a first training module, configured to perform model training by using each sample image to be trained, to generate the convolutional neural network model, and verify the generated convolutional neural network model by using each sample image to be verified;

The first processing module is configured to: if the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the number of the insurance ticket sample pictures is increased to perform training and verification again.
The identification device for an insurance policy according to any one of claims 6 to 8, characterized in that The first recognition model is a time recurrent neural network model, and the insurance number identification device further includes:

The third extraction module is configured to obtain a preset number of insurance ticket number sample images, extract a second preset ratio insurance ticket number sample image as a training set, and replace the remaining insurance policy in the preset number of insurance policy number sample images The number sample picture is used as a test set;

a second training module, configured to input the insurance ticket number sample picture in the training set to a time recurrent neural network model for model training, and use the insurance ticket number sample picture in the test set to train the time every preset time The recurrent neural network model is tested to evaluate the recognition effect of the trained time recurrent neural network model;

a second processing module, configured to calculate a recognition error of the trained time recurrent neural network model after each test, if the recognition error converges, the training is completed, otherwise the model parameters of the time recurrent neural network model are adjusted, To re-train and test.
The apparatus for identifying an insurance policy number according to claim 9, wherein the identification device of the insurance policy number further comprises:

The search module is configured to: after receiving the search request for carrying the insurance policy number issued by the terminal, search for an insurance policy picture associated with the insurance policy number, and send the found insurance policy picture to the terminal.
An apparatus for identifying an insurance policy number, the apparatus comprising the memory, a processor, and a program for identifying an insurance policy number stored on the memory and operable on the processor, the identifying When the program of the insurance policy number is executed by the processor, the following steps are implemented:

S1. After receiving the insurance policy picture, identify the insurance type corresponding to the insurance policy picture, and extract the insurance policy number based on the predetermined insurance type and the positional relationship of the insurance policy number in the insurance policy picture. The corresponding target line character area in a single picture;

S2, calling a first recognition model generated by the pre-training to perform character recognition on the target line character region to identify an insurance policy number included in the target line character region, and identify the insurance policy number and the insurance policy The image is stored in association.
The device for identifying an insurance policy number according to claim 11, wherein said step S2 can be replaced by:

S0. After receiving the insurance policy picture, the second recognition model generated by the pre-training is invoked to identify the target line character area in which the insurance policy number is located in the insurance policy picture.
The apparatus for identifying an insurance policy number according to claim 12, wherein said second recognition model is a convolutional neural network model, and said processor is further configured to execute said program for identifying an insurance policy number to Before step S0, the following steps are also implemented:

S01, obtaining a preset number of insurance policy sample pictures, using the insurance policy sample picture including the insurance policy number as the first picture set, and using the insurance policy sample picture not including the insurance policy number as the second Photo album;

S02, extracting, from the first picture set and the second picture set, a first preset proportion of insurance policy sample pictures as sample pictures to be trained, and collecting remaining insurance policy samples in the first picture set and the second picture set The picture serves as a sample picture to be verified;

S03: performing model training by using each sample image to be trained to generate the convolutional neural network model, and verifying the generated convolutional neural network model by using each sample image to be verified;

S04. If the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the number of the insurance ticket sample pictures is increased to perform training and verification again.
The apparatus for identifying an insurance policy number according to any one of claims 11 to 13, wherein the first recognition model is a time recurrent neural network model, and the processor is further configured to execute the identification insurance policy The number program, in order to perform the following steps before step S2:

S21: Obtain a preset number of insurance ticket number sample images, extract a second preset proportion of insurance policy number sample images as a training set, and test the remaining insurance policy number sample images in the preset number of insurance policy number sample images as a test set;

S22: Input the insurance ticket number sample image in the training set to the time recurrent neural network model for model training, and use the insurance ticket number sample image in the test set to perform the trained time recurrent neural network model every preset time. Testing to assess the recognition effect of the trained time recurrent neural network model;

S23. After each test, calculate the recognition error of the trained time recurrent neural network model. If the recognition error converges, the training is completed, otherwise the model parameters of the time recurrent neural network model are adjusted to re-train and test.
The apparatus for identifying an insurance policy number according to any one of claims 11 to 13, wherein the processor is further configured to execute the program for identifying the insurance policy number, to further implement the following after step S2 step:

S3. After receiving the search request for carrying the insurance policy number issued by the terminal, search for the insurance policy picture associated with the insurance policy number, and send the found insurance policy picture to the terminal.
A computer readable storage medium, wherein the computer readable storage medium stores a program for identifying an insurance policy number, and the program for identifying the insurance policy number is executed by the processor to implement the following steps:

S1. After receiving the insurance policy picture, identify the insurance type corresponding to the insurance policy picture, and extract the insurance policy number based on the predetermined insurance type and the positional relationship of the insurance policy number in the insurance policy picture. The corresponding target line character area in a single picture;

S2, calling a first recognition model generated by the pre-training to perform character recognition on the target line character region to identify an insurance policy number included in the target line character region, and identify the insurance policy number and the insurance policy The image is stored in association.
The computer readable storage medium according to claim 16, wherein said step S2 is replaceable by:

S0. After receiving the insurance policy picture, the second recognition model generated by the pre-training is invoked to identify the target line character area in which the insurance policy number is located in the insurance policy picture.
The computer readable storage medium according to claim 17, wherein when said program for identifying the policy number is executed by the processor, the following steps are further implemented before step S0:

S01: Obtain a preset number of insurance policy sample pictures, use an insurance policy sample picture including an insurance policy number as a first picture set, and use an insurance policy sample picture that does not include an insurance policy number as a second picture set;

S02, extracting, from the first picture set and the second picture set, a first preset proportion of insurance policy sample pictures as sample pictures to be trained, and collecting remaining insurance policy samples in the first picture set and the second picture set The picture serves as a sample picture to be verified;

S03: performing model training by using each sample image to be trained to generate the convolutional neural network model, and verifying the generated convolutional neural network model by using each sample image to be verified;

S04. If the verification pass rate is greater than or equal to the preset threshold, the training is completed, otherwise the number of the insurance ticket sample pictures is increased to perform training and verification again.
The computer readable storage medium according to any one of claims 16 to 18, wherein when the program for identifying the insurance policy number is executed by the processor, the following steps are further implemented before step S2:

S21: Obtain a preset number of insurance ticket number sample images, extract a second preset proportion of insurance policy number sample images as a training set, and test the remaining insurance policy number sample images in the preset number of insurance policy number sample images as a test set;

S22: Input the insurance ticket number sample image in the training set to the time recurrent neural network model for model training, and use the insurance ticket number sample image in the test set to perform the trained time recurrent neural network model every preset time. Testing to assess the recognition effect of the trained time recurrent neural network model;

S23. After each test, calculate the recognition error of the trained time recurrent neural network model. If the recognition error converges, the training is completed, otherwise the model parameters of the time recurrent neural network model are adjusted to re-train and test.
The computer readable storage medium according to claim 16, wherein when the program for identifying the policy number is executed by the processor, the following steps are further implemented after step S2:

S3. After receiving the search request for carrying the insurance policy number issued by the terminal, search for the insurance policy picture associated with the insurance policy number, and send the found insurance policy picture to the terminal.