Method and system for inputting automobile loan image

Abstract

The invention relates to a method and a system for inputting an automobile loan image. The method comprises the following steps of: uploading all image data; training an image recognition model based on OpenCV and OCR, and automatically recognizing the image to obtain an image category; acquiring image information, and classifying image categories; and judging whether the types of the required images are all aligned or not, and generating an uploading result. According to the method, the uploaded images are processed through OpenCV, the images are automatically classified in real time and efficiently in combination with the OCR recognition technology, and the manual auditing cost is reduced. According to the method, the uploaded image is segmented and sliced to obtain the to-be-recognized area, so that accurate recognition is realized, and the recognition efficiency is improved. According to the invention, the user is supported to upload all loan image data at one time, and the uploading experience of the user is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to a method and system for recording automobile loan images. Background Art

[0002] In recent years, with the rapid development of society and the rapid growth of economy, various financial needs have emerged. Auto finance is a major form of consumption in modern car purchases. It refers to the loan issued by the lender to the borrower for the purchase of cars (including second-hand cars). When applying for a car loan, you need to collect images such as marriage certificates, insurance policies, compulsory traffic insurance, and driving licenses. There are many types of image materials to collect, and the borrower needs to upload them one by one. If the uploaded folder is wrong, you need to return and re-upload the image materials.

[0003] At present, the method of uploading image materials commonly used by financial institutions is to display different folder entrances and guide borrowers to upload images to designated folders. The standard image recognition service adopts the standard material recognition privatization solution provided by AI companies such as Baidu and Megvii, and recognizes the image content of standard files through the standard image recognition service.

[0004] The method of distinguishing file types by folders can only guide customers to upload, but it cannot verify whether the uploaded images are correct. Whether the uploaded images are correct needs to be discovered during the manual review stage, and manual review is inefficient and has credit risks; manual review has time lag problems, and the borrower needs to re-upload after the credit reviewer returns the file. This method has poor user experience and low timeliness. The standard image recognition solution mainly recognizes file content, and does not support personalized material recognition. In addition, this method requires all information to be recognized before returning the recognition result. The recognition speed is slow and the results cannot be returned to the borrower in real time from end to end. Summary of the invention

[0005] To address the deficiencies in the prior art, the present invention processes the uploaded images through OpenCV, recognizes them through OCR technology, compares the recognition results with preset identifying texts, and automatically classifies the images according to the comparison results, thereby reducing manual review, achieving real-time and efficient feedback results, and improving approval efficiency.

[0006] To achieve the above objectives, the technical solutions adopted by the present invention include:

[0007] A method for recording automobile loan images, characterized by comprising:

[0008] S1. Upload all image data;

[0009] S2. Based on OpenCV and OCR, the image recognition model is trained to automatically identify the image and obtain the image category;

[0010] S3. Obtain image information and classify the image category;

[0011] S4. Determine whether all the required image categories have been collected and generate an upload result.

[0012] Furthermore, the image recognition model is trained based on OpenCV and OCR to automatically recognize the image and obtain the image category, including:

[0013] Process the image data through OpenCV to obtain the area to be identified;

[0014] Identify the area to be identified by using OCR recognition technology to obtain text recognition content;

[0015] The text recognition content is compared with the preset identifying text to obtain the image category.

[0016] Furthermore, the method for training the image recognition model includes:

[0017] Collect uploaded image data templates, where the image data templates are used to analyze feature information of the image data;

[0018] Obtaining the length and width of the image data template, calculating the length-to-width ratio of the image data template, and obtaining the color of the image data template;

[0019] Obtaining the identification text of the image data template and obtaining the position information of the identification text, wherein the identification text is used to determine the image category;

[0020] The image to be tested is converted into a binary image through OpenCV, and the binary image is segmented and sliced ​​to obtain the area to be identified, and the image data is traversed to calculate the color with the largest proportion to obtain the image color;

[0021] Determine the image category according to the aspect ratio and the image color, and obtain the identification area position according to the image category;

[0022] Recognize the area to be recognized by OCR to obtain text recognition content of the image to be tested;

[0023] The image to be tested is classified according to the identifying characters of the image data template and the text recognition content.

[0024] Furthermore, the image information includes an image ID and an image URL.

[0025] Furthermore, the determining whether the categories of the required images have all been collected and generating the upload result includes:

[0026] If all the required image categories have been collected, the upload result returned is successful upload;

[0027] If the categories of the required images are not all collected, a warning message is issued and a prompt is given to perform a second upload, wherein the warning message includes the type of image that has not been uploaded.

[0028] Furthermore, the method also includes: configuring the aspect ratio, color, and size of the area to be identified.

[0029] The present invention also relates to a system for recording automobile loan images, which is characterized by comprising:

[0030] Upload module, used to upload all image data;

[0031] The recognition module is used to train the image recognition model based on OpenCV and OCR, automatically recognize the image, and obtain the image category;

[0032] A classification module, used to obtain image information and classify the image categories;

[0033] The judgment module is used to judge whether the categories of required images have been collected and generate the upload results.

[0034] The present invention also relates to a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and the computer program implements the above method when executed by a processor.

[0035] The present invention also relates to an electronic device, characterized in that it comprises a processor and a memory;

[0036] The memory is used to store program instructions;

[0037] The processor is used to execute the above method by calling program instructions.

[0038] The present invention also relates to a computer program product, comprising a computer program and / or instructions, characterized in that the computer program and / or instructions implement the steps of the above method when executed by a processor.

[0039] The beneficial effects of the present invention are:

[0040] By using the method and system for auto loan image entry described in the present invention, the uploaded images are processed by OpenCV, and combined with OCR recognition technology, real-time and efficient automatic classification of images is achieved, reducing the cost of manual review. By segmenting and slicing the uploaded images, the area to be identified is obtained, accurate identification is achieved, and the identification efficiency is improved. The present invention supports users to upload all loan image materials at one time, greatly improving the user upload experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a method flow for recording automobile loan images according to the present invention;

[0042] Figure 2 A schematic diagram of the system structure of an automobile loan image recording system according to the present invention;

[0043] Figure 3 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. It should be understood that the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments, and these embodiments are only used to illustrate the present application and are not used to limit the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0045] like Figure 1 As shown, the embodiment of the present invention discloses a method for recording automobile loan images, the steps of which include:

[0046] Step S1. Upload all image data.

[0047] In this embodiment, the user can upload all the image data required for the loan to the image system at one time, such as: registration certificate, driving license, property certificate, bank flow, commercial insurance policy, compulsory traffic insurance, license plate certificate, GPS screenshot, etc., without the need for users to upload them one by one to the designated folder. Usually, after uploading to the designated folder, it is necessary to wait for the manual review stage to determine whether the uploaded image is correct. The manual review is inefficient and there is a credit risk. After uploading the image, it is not possible to verify whether the image is correct in real time. There is a time lag problem in manual review, and the user experience is poor. The present invention supports uploading all image data at one time, avoiding the situation where the image data required to be uploaded is inconsistent with the image data actually uploaded. There is no need to wait for manual response, and it can verify in real time whether the uploaded image is correct, which reduces labor costs and improves review efficiency.

[0048] Users can upload the required image data from the mobile phone album or by taking it with the camera.

[0049] Step S2: Based on OpenCV and OCR, the image recognition model is trained to automatically recognize the image and obtain the image category.

[0050] The imaging system processes the image, identifies the content of the designated area, and obtains the recognition result, i.e., the image category.

[0051] In this embodiment, the method for training the image recognition model includes:

[0052] Step S201: Collect uploaded image data templates, which are used to analyze feature information of image data.

[0053] Step S202: Obtain the length and width of the image data template, calculate the length-to-width ratio of the image data template, and obtain the color of the image data template.

[0054] In this embodiment, the image category can be preliminarily determined based on the aspect ratio and color.

[0055] Step S203: Obtain the identification text of the image data template and obtain the position information of the identification text, wherein the identification text is used to determine the image category.

[0056] Each loan image document has identifying text to reflect the category to which the loan image document belongs. For example, the title of a commercial insurance policy is "Motor Vehicle Commercial Insurance Policy", but other loan image documents such as the registration certificate do not have this text content. In this case, "Motor Vehicle Commercial Insurance Policy" can be used as the identifying text, and the position of the identifying text is recorded at the same time. The identifying text content and the identifying text position can be used as the image category features for preliminary judgment of the image category.

[0057] Step S204: Obtain a binary image from the image to be tested through OpenCV, and segment and slice the binary image to obtain the area to be identified, traverse the image data to calculate the color with the largest proportion, and obtain the image color.

[0058] In this embodiment, the image picture taken by the user with the camera may be affected by the background, light, pixels, angle, etc. Specifically, a binary image can be obtained by grayscale conversion, sharpening, image edge extraction, etc. through OpenCV, and the four points of the upper, lower, left, and right edges of the image contour can be located to determine the segmentation range and correct the image.

[0059] In this embodiment, color can also be used as a basis for image classification. A color recognition threshold is set to determine the color range, and the color with the largest proportion is calculated by traversing the image through OpenCV, and the color with the largest proportion is recorded as the image color.

[0060] Preferably, the present invention, through preprocessing and segmentation of the image, only needs to recognize the text content in the key area, thereby improving the recognition speed.

[0061] Step S205: Determine the image category according to the aspect ratio and the image color, and obtain the identification area position according to the image category.

[0062] The image is initially classified based on the aspect ratio and image color. The text content position can also be selected to determine the image type, and the text content position of the image data template is compared with the text content position of the image to be tested.

[0063] Step S206: Identify the area to be identified by OCR to obtain text recognition content of the image to be tested.

[0064] Step S207: Classify the image to be tested according to the identifying characters of the image data template and the text recognition content.

[0065] In the embodiment of the present invention, the identification text of the image template obtained in step S203 and the text recognition content of the image to be tested obtained in step S206 are compared to further determine the image category to determine whether the image to be tested is the image category that needs to be collected by the current loan node.

[0066] In actual business scenarios, a loan application can be divided into multiple nodes such as credit approval image, credit use image, and consumption voucher image, and each node collects different types of images. According to actual needs, each node first sets the image category to be entered, and for the images uploaded by the user, steps S204 to S207 are executed cyclically to perform identification and classification.

[0067] This image recognition model improves recognition speed and user experience by acquiring the template features of the image data and configuring the aspect ratio, color classification, and size of the area to be recognized. Users do not need to wait for manual review, and recognition results are generated in real time.

[0068] Step S3: Obtain image information and classify the image categories.

[0069] Specifically, the image information includes an image ID and an image URL.

[0070] In this embodiment, the image system returns the image information and image categories to the credit platform, and the lending platform classifies and stores the images.

[0071] Step S4: Determine whether all the required image categories have been collected and generate an upload result.

[0072] If all the required image categories have been collected, the upload result is returned as successful upload and the result is returned to the user;

[0073] If the required image categories are not all collected, an alarm message will be issued, and the user will be prompted with unrecognized images. The user will confirm whether to re-upload. The user will submit the loan application again, and the unrecognized images can be manually reviewed and classified.

[0074] In the embodiment of the present invention, the aspect ratio, image color threshold, size of the area to be identified, etc. can be configured to adjust the parameters to further improve the recognition efficiency and accuracy of the model.

[0075] like Figure 2 As shown, the embodiment of the present invention discloses a system for recording automobile loan images, the structure of which includes:

[0076] Upload module, used to upload all image data;

[0077] The recognition module is used to train the image recognition model based on OpenCV and OCR, automatically recognize the image, and obtain the image category;

[0078] A classification module, used to obtain image information and classify the image categories;

[0079] The judgment module is used to judge whether the categories of required images have been collected and generate the upload results.

[0080] By using this system, the above-mentioned operation processing method can be executed and the corresponding technical effects can be achieved.

[0081] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the method in the above embodiment, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps in the method in the above embodiment are implemented.

[0082] The embodiment of the present invention further provides an electronic device for executing the above method, as a device for implementing the method, and its structure is as follows: Figure 3 As shown, the electronic device 300 includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one. It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the specific structure of the electronic device 300. In other embodiments of the present application, the electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. Optionally, the electronic device may also include a display screen 305 for displaying images, or receiving user operation instructions when necessary.

[0083] The processor 301 is used in the embodiment of the present application to implement a method for recording a car loan image as shown in the above embodiment. The transceiver 304 may include a receiver and a transmitter. The transceiver 304 is used in the embodiment of the present application to implement the function of the electronic device of the embodiment of the present application communicating with other devices when executed.

[0084] Processor 301 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0085] The processor 301 may also include one or more processing units, for example, the processor 301 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processor (NPU). Among them, different processing units may be independent devices or integrated into one or more processors. Among them, the controller may be the nerve center and command center of the electronic device 300. The controller may generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. A memory may also be set in the processor 301 for storing instructions and data. In some embodiments, the memory in the processor 301 is a high-speed cache memory. The memory may store instructions or data that the processor 301 has just used or circulated. If the processor 301 needs to use the instruction or data again, it may be directly called from the memory. Repeated access is avoided, the waiting time of the processor 301 is reduced, and the efficiency of the system is improved.

[0086] The processor 301 can run the auto loan image entry method provided in the embodiment of the present application to reduce the user's operation complexity, improve the intelligence of the terminal device, and enhance the user experience. The processor 301 can include different devices. For example, when the CPU and GPU are integrated, the CPU and GPU can cooperate to execute the auto loan image entry method provided in the embodiment of the present application. For example, during the auto loan image entry process, part of the algorithm is executed by the CPU, and the other part of the algorithm is executed by the GPU to obtain faster processing efficiency.

[0087] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0088] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or may include a high-speed random access memory, or may include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0089] Optionally, the memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the method in any embodiment of the present application.

[0090] The memory 303 can be used to store computer executable program codes, which include instructions. The processor 301 executes various functional applications and data processing of the electronic device 300 by running the instructions stored in the memory 303. The memory 303 may include a program storage area and a data storage area. Among them, the program storage area may store the code of the operating system, application program, etc. The data storage area may store data created during the use of the electronic device 300 (such as images, videos, etc. collected by the camera application), etc.

[0091] The memory 303 may also store one or more computer programs corresponding to the automobile loan image entry method provided in the embodiment of the present application. The one or more computer programs are stored in the memory 303 and configured to be executed by the one or more processors 301. The one or more computer programs include instructions, which can be used to execute each step in the above corresponding embodiment.

[0092] Of course, the code of the automobile loan image entry method provided in the embodiment of the present application can also be stored in an external memory. In this case, the processor 301 can run the code stored in the external memory through the external memory interface, and the processor 301 can control the operation of the automobile loan image entry process.

[0093] The display screen 305 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 300 may include 1 or N display screens 305, where N is a positive integer greater than 1. The display screen 305 can be used to display information input by a user or information provided to a user and various graphical user interfaces (GUI). For example, the display screen 305 can display photos, videos, web pages, or files.

[0094] The electronic device provided in the embodiment of the present application is applicable to any embodiment of the above method. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0097] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0099] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for recording automobile loan images, characterized in that: include: S1. Upload all image data; S2. Based on OpenCV and OCR, the image recognition model is trained to automatically identify the image and obtain the image category; S3. Obtain image information and classify the image category; S4. Determine whether all the required image categories have been collected and generate an upload result.

2. The method according to claim 1, characterized in that The image recognition model is trained based on OpenCV and OCR to automatically recognize the image and obtain the image category, including: Process the image data through OpenCV to obtain the area to be identified; Identify the area to be identified by using OCR recognition technology to obtain text recognition content; The text recognition content is compared with the preset identifying text to obtain the image category.

3. The method according to claim 1-2, characterized in that: The method for training an image recognition model comprises: Collect uploaded image data templates, where the image data templates are used to analyze feature information of the image data; Obtaining the length and width of the image data template, calculating the length-to-width ratio of the image data template, and obtaining the color of the image data template; Obtaining the identification text of the image data template and obtaining the position information of the identification text, wherein the identification text is used to determine the image category; The image to be tested is converted into a binary image through OpenCV, and the binary image is segmented and sliced ​​to obtain the area to be identified, and the image data is traversed to calculate the color with the largest proportion to obtain the image color; Determine the image category according to the aspect ratio and the image color, and obtain the identification area position according to the image category; Recognize the area to be recognized by OCR to obtain text recognition content of the image to be tested; The image to be tested is classified according to the identifying characters of the image data template and the text recognition content.

4. The method according to claim 1, characterized in that The image information includes an image ID and an image URL.

5. The method according to claim 1, characterized in that The step of determining whether all the required image categories have been collected and generating an upload result includes: If all the required image categories have been collected, the upload result returned is successful upload; If the categories of the required images are not all collected, a warning message is issued and a prompt is given to perform a second upload, wherein the warning message includes the type of image that has not been uploaded.

6. The method according to claims 1 to 5, characterized in that The method further includes: configuring the aspect ratio, color, and size of the area to be identified.

7. A system for recording automobile loan images, characterized in that: include: Upload module, used to upload all image data; The recognition module is used to train the image recognition model based on OpenCV and OCR, automatically recognize the image, and obtain the image category; A classification module, used to obtain image information and classify the image categories; The judgment module is used to judge whether the categories of required images have been collected and generate the upload results.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store program instructions; The processor is configured to execute the method according to any one of claims 1 to 6 by calling program instructions.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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