CN110929614A - Template positioning method and device and computer equipment - Google Patents
Template positioning method and device and computer equipment Download PDFInfo
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- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
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Abstract
The application relates to a template positioning method, a template positioning device, computer equipment and a storage medium. The method comprises the following steps: acquiring a policy template picture; identifying a specific text region in the policy template picture, determining the position information of the specific text region, and determining the picture characteristics of the policy template picture through big data analysis; classifying the policy template pictures according to the picture characteristics; preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set; and training the data set according to the picture characteristics to obtain a template positioning model. By adopting the method, the problem that model training cannot be carried out due to the small quantity of the pictures of the policy and policy template can be effectively solved, the accuracy of policy classification is improved, and the operation efficiency is improved.
Description
Technical Field
The present application relates to the field of software development technologies, and in particular, to a template positioning method, apparatus, computer device, and storage medium.
Background
In the field of traditional policy identification, data filling is usually performed according to data manually input in a paper policy by a user, and with the progress of science and technology, an intelligent policy identification technology arises, for example, an OCR image identification technology which is rapidly developed at present, and is widely applied to identification of identity cards, bank cards and various policies.
At present, the establishment of a policy template is to perform feature extraction on each picture through machine learning and then perform vector machine classification on feature values, but the method needs to train a large amount of policy data, is slow in operation process and often has the problem of insufficient data sets, so that the template positioning of the policy OCR is very complex.
Disclosure of Invention
Therefore, it is necessary to provide a template positioning method, device, computer device and storage medium for the above technical problems, so as to effectively solve the problem that model training cannot be performed due to a small number of images of a policy and policy template, improve the accuracy of policy classification, and improve the operation efficiency.
A method of stencil positioning, the method comprising:
acquiring a policy template picture;
identifying a specific text region in the policy template picture, determining the position information of the specific text region, and determining the picture characteristics of the policy template picture through big data analysis;
classifying the policy template pictures according to the picture characteristics;
preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set;
training the data set according to the picture characteristics to obtain a template positioning model;
and the determining module is suitable for determining the policy picture to be processed based on the template positioning model.
In one embodiment, after acquiring the policy template picture, the method further includes:
and carrying out coarse classification processing on the policy template picture through machine learning.
In one embodiment, identifying a specific text region in the policy template picture, and determining the location information of the specific text region includes:
carrying out coordinate interception on the area corresponding to the specific text in each type of picture after the rough classification processing to obtain the coordinate information of the area;
and performing distance conversion on the coordinate information according to a preset algorithm to obtain the picture characteristics.
In one embodiment, the pre-processing comprises:
intercepting all policy template pictures under each classification according to a preset interception rule;
and rotating the intercepted picture according to different angles to generate a plurality of policy template pictures, and carrying out data coding and labeling.
In one embodiment, the picture characteristics include plate-type uniform characteristics.
In one embodiment, the method further comprises the following steps:
and training the data set through a convolutional neural network according to the plate-type uniform characteristics.
In one embodiment, before training the data set, the method further comprises:
and reducing the data set to a set size, carrying out zero-averaging processing, and converting and filling specific text region data into image data.
A reticle positioning apparatus, the apparatus comprising:
the acquisition module is suitable for acquiring a policy template picture;
the analysis module is suitable for identifying a specific text region in the policy template picture, determining the position information of the specific text region and determining the picture characteristics of the policy template picture through big data analysis;
the classification module is suitable for classifying the insurance policy template pictures according to the picture characteristics;
the preprocessing module is suitable for preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set;
the training module is suitable for training the data set according to the picture characteristics to obtain a template positioning model; and the determining module is suitable for determining the policy picture to be processed based on the template positioning model.
A computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the methods described above.
According to the template positioning method, the template positioning device, the computer equipment and the storage medium, the policy template picture is obtained, the specific text area in the policy template picture is identified, the position information of the specific text area is determined, the picture characteristic of the policy template picture is determined through big data analysis, then the policy template picture is classified according to the picture characteristic, one policy template picture under each classification is preprocessed, a plurality of additional policy template pictures related to the picture are obtained, a data set is generated, the data set is trained according to the picture characteristic, and a template positioning model is obtained, so that the problem of insufficient data sets is effectively solved, the accuracy of policy classification is improved, and the operation efficiency is improved.
Drawings
FIG. 1 is a flow diagram of a stencil positioning method in one embodiment;
FIG. 2 is a diagram illustrating determination of regions of picture specific text in one embodiment;
FIG. 3 is a flowchart illustrating step S102 according to an embodiment;
FIG. 4 is a schematic flow diagram of pretreatment in one embodiment;
FIG. 5 is a diagram illustrating an embodiment of a system for coding a policy;
FIG. 6 is a block diagram of a reticle positioning device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In this document, relational terms such as left and right, top and bottom, front and back, first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
In the present invention, the pictures may be any fields and any types of pictures, and for convenience of description, only the insurance policy (i.e. insurance policy) is described as an example of one picture, but it is understood that other types of pictures are also covered by the scope of the present invention.
In one embodiment, as shown in fig. 1, a template positioning method is provided, which may be implemented in a smart terminal, where the smart terminal may be a personal computer, a laptop, a smartphone, a tablet computer, and a portable wearable device, and may also be an independent server or a server cluster formed by multiple servers, and specifically, the method includes:
s101, acquiring a policy template picture;
specifically, one or more paper policy can be scanned by a scanning tool to obtain a policy template picture.
In a certain embodiment, after the policy template picture is obtained, the obtained policy template picture may be subjected to coarse classification by machine learning, specifically, the policy template picture may be subjected to coarse classification by unsupervised learning. The unsupervised learning means that the input data are not marked and have no determined result, and the model automatically induces the structure and the value of the data.
S102, identifying a specific text region in the policy template picture, determining position information of the specific text region, and determining picture characteristics of the policy template picture through big data analysis;
the specific text area may be an inherent text in the policy page except for the entered data, for example, as shown in fig. 2, the specific text may be a fixed text in the policy, such as "insurance unit", "contact name", "insured name", "contact phone" or "organization code", and the corresponding areas of the fixed text, i.e., the specific text area, are shown as rectangular boxes in fig. 2.
Further, as shown in fig. 3, S102 specifically includes:
s301, coordinate capture is carried out on the region corresponding to the specific text in each type of picture after the rough classification processing, and coordinate information of the region is obtained;
and coordinate interception can be carried out on the areas where the fixed texts are located through an interception coordinate position tool.
S302, performing distance conversion on the coordinate information according to a preset algorithm to obtain the picture characteristics.
The preset algorithm can be an Euclidean distance algorithm, the Euclidean distance algorithm is a distance measurement algorithm, and the measured quantity is moreThe absolute distance between two points in the dimensional space can also be understood as the real distance between two points in the m-dimensional space, or the natural length of the vector (i.e. the distance from the point to the origin), and the euclidean distance in the two-dimensional and three-dimensional spaces is the actual distance between two points, and can be specifically expressed by a formula
And performing distance conversion. After the distance conversion is carried out, corresponding picture characteristics are obtained through big data analysis, the field style of the picture is obtained, and the plate type of the policy picture is determined.
S103, classifying the policy template pictures according to the picture characteristics;
the picture characteristics can be plate characteristics, pictures with the same picture characteristics are classified into one class, and the insurance policies of different risk categories can share one template with Euclidean distance. The image layout is obtained by performing distance conversion on the coordinates of the specific text region of the policy, and the policy is classified according to the uniform plate characteristic, so that the policy can be classified without paying attention to the text content in the image and only paying attention to the image style during subsequent data training.
S104, preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set;
further, as shown in fig. 4, the preprocessing may specifically include:
s401, intercepting all policy template pictures under each classification according to a preset intercepting rule;
specifically, a policy in the same category may be reset according to a set width and height, for example, a policy a in category a, a policy B in category B, and a policy C in category C may be collectively configured as 900 × 1200 (width × height) pixels.
Then, randomly cutting the picture after the reset according to the first set pixel, and then randomly cutting the picture into a picture with a second set pixel. For example, the policy a is randomly intercepted according to N × N pixels to obtain a picture a1, N is an arbitrary value between 650 and 800, then a1 is randomly intercepted according to N × N pixels to obtain a2, N is 600, and the policies b and c are processed according to the same method.
S402, rotating the captured pictures according to different angles to generate a plurality of policy template pictures, and carrying out data coding and labeling.
For example, the picture a2 may be rotated according to different angles to generate a plurality of policy template pictures to expand the training data, and the positions of the captured pictures containing the fixed text regions may be transformed and recorded, and the policies b and c may be similarly processed according to the same method.
Further, further refinement and classification can be performed manually, the same picture format of the same company is defined as one template, otherwise, the labels and the corresponding parsing codes of each label are defined for different templates, for example, the intercepted picture is coded and labeled as shown in fig. 5.
And S105, training the data set according to the picture characteristics to obtain a template positioning model.
The method can be used for training based on 16 layers of convolutional neural networks (such as a 'residual error' network) according to plate-type unified characteristics, when the characteristics are adjusted and extracted, parameters are set to output key emphasis picture pattern characteristics, a 'similarity conversion' layer algorithm is added to a convolutional layer during the extraction of the 2 nd and 3 rd layer characteristics, and the characteristic value of each row of fixed regions is promoted by combining fixed region data, so that the text content in a policy image does not need to be concerned during training, only the policy image pattern needs to be concerned, the problems of insufficient training data and single prediction capability are solved, and meanwhile, the accurate positioning of templates of policy OCR is ensured.
And S106, determining a policy picture to be processed based on the template positioning model.
In other embodiments, before training the data set, further comprising: and reducing the data set to a set size, such as 224 × 224, performing zero-averaging processing, converting and filling specific text region data into image data, training the model, and obtaining a template positioning model of the policy-preserving OCR.
In one embodiment, as shown in fig. 6, there is provided a stencil positioning apparatus, which may be an intelligent terminal device with certain computing power, such as a mobile phone, a smart phone, a PDA or a tablet computer, or other electronic devices that can interact with the internet, such as cameras, wearable electronic devices, car navigation devices, electronic interactive terminals installed in public places such as stations or schools, etc., and may also be servers or clusters of servers with independent computing capabilities, the device is adapted to perform any of the above template positioning methods and can access the network via broadband, e.g. ADSL, VDSL, fiber, wireless, cable tv, satellite, etc., or via narrowband, such as telephone dial-up access, GPRS, 2G, 3G, etc. to the internet, or alternatively to telecommunications networks via CDMA, 2G, 3G, 4G, etc. technologies. Specifically, the apparatus includes:
the obtaining module 601 is suitable for obtaining a policy template picture;
an analysis module 602, adapted to identify a specific text region in the policy template picture, determine location information of the specific text region, and determine picture characteristics of the policy template picture through big data analysis;
a classification module 603 adapted to classify the policy template picture according to the picture characteristics;
a preprocessing module 604 adapted to preprocess one policy template picture under each classification, obtain a plurality of additional policy template pictures associated with the picture, and generate a data set;
a training module 605 adapted to train the data set according to the picture characteristics to obtain a template positioning model.
The various modules described above may be run integrated into a processor, which may be a central processing unit ("CPU") or a graphics processing unit ("GPU"), and in particular the processor may comprise one or more printed circuit boards or micro-processing module chips executing sequences of computer program instructions to perform the stencil positioning method mentioned above.
In a certain embodiment, the policy making system further comprises a rough classification module adapted to perform rough classification processing on the policy making template picture through machine learning after the policy making template picture is acquired.
In one embodiment, the analysis module 602 further comprises:
the first intercepting unit is suitable for intercepting the coordinates of the area corresponding to the specific text in each type of picture after the rough classification processing to obtain the coordinate information of the area;
and the distance conversion unit is suitable for performing distance conversion on the coordinate information according to a preset algorithm to obtain the picture characteristics.
In a certain embodiment, the preprocessing module 604 further comprises:
the second interception unit is suitable for intercepting all the policy template pictures under each classification according to a preset interception rule;
and the rotating unit is suitable for rotating the intercepted picture according to different angles to generate a plurality of policy template pictures and carry out data coding and marking.
In one embodiment, the picture characteristic comprises a plate-type uniform characteristic.
In one embodiment, the training module 605 further comprises: and training the data set through a convolutional neural network according to the plate-type uniform characteristics.
In one embodiment, the method further comprises:
and the data set processing module is suitable for reducing the data set to a set size before training the data set, carrying out zero-averaging processing, and converting and filling specific text region data into image data.
The specific definition of the reticle positioning device can be referred to the definition of the reticle positioning method in the above, and is not described herein again. The modules in the stencil positioning apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
s101, acquiring a policy template picture;
s102, identifying a specific text region in the policy template picture, determining position information of the specific text region, and determining picture characteristics of the policy template picture through big data analysis;
s103, classifying the policy template pictures according to the picture characteristics;
s104, preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set;
and S105, training the data set according to the picture characteristics to obtain a template positioning model.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
s101, acquiring a policy template picture;
s102, identifying a specific text region in the policy template picture, determining position information of the specific text region, and determining picture characteristics of the policy template picture through big data analysis;
s103, classifying the policy template pictures according to the picture characteristics;
s104, preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set;
and S105, training the data set according to the picture characteristics to obtain a template positioning model.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of stencil positioning, the method comprising:
acquiring a policy template picture;
identifying a specific text region in the policy template picture, determining the position information of the specific text region, and determining the picture characteristics of the policy template picture through big data analysis;
classifying the policy template pictures according to the picture characteristics;
preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set;
training the data set according to the picture characteristics to obtain a template positioning model;
and determining a policy picture to be processed based on the template positioning model.
2. The method of claim 1, after obtaining the policy template picture, further comprising:
and carrying out coarse classification processing on the policy template picture through machine learning.
3. The method of claim 2, wherein identifying a specific text region in the policy template picture, and wherein determining location information for the specific text region comprises:
carrying out coordinate interception on the area corresponding to the specific text in each type of picture after the rough classification processing to obtain the coordinate information of the area;
and performing distance conversion on the coordinate information according to a preset algorithm to obtain the picture characteristics.
4. The method of claim 1, wherein the pre-processing comprises:
intercepting all policy template pictures under each classification according to a preset interception rule;
and rotating the intercepted picture according to different angles to generate a plurality of policy template pictures, and carrying out data coding and labeling.
5. The method of any of claims 1-4, wherein the picture characteristics comprise board uniform characteristics.
6. The method of claim 5, further comprising:
and training the data set through a convolutional neural network according to the plate-type uniform characteristics.
7. The method of claim 1, further comprising, prior to training the data set:
and reducing the data set to a set size, carrying out zero-averaging processing, and converting and filling specific text region data into image data.
8. A reticle positioning apparatus, the apparatus comprising:
the acquisition module is suitable for acquiring a policy template picture;
the analysis module is suitable for identifying a specific text region in the policy template picture, determining the position information of the specific text region and determining the picture characteristics of the policy template picture through big data analysis;
the classification module is suitable for classifying the insurance policy template pictures according to the picture characteristics;
the preprocessing module is suitable for preprocessing one policy template picture under each classification to obtain a plurality of additional policy template pictures related to the picture and generate a data set;
the training module is suitable for training the data set according to the picture characteristics to obtain a template positioning model;
and the determining module is suitable for determining the policy picture to be processed based on the template positioning model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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