CN112434619A - Case information extraction method, case information extraction device, case information extraction equipment and computer readable medium - Google Patents

Case information extraction method, case information extraction device, case information extraction equipment and computer readable medium Download PDF

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Publication number
CN112434619A
CN112434619A CN202011357576.5A CN202011357576A CN112434619A CN 112434619 A CN112434619 A CN 112434619A CN 202011357576 A CN202011357576 A CN 202011357576A CN 112434619 A CN112434619 A CN 112434619A
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Prior art keywords
case information
information extraction
model
parameters
training
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CN202011357576.5A
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CN112434619B (en
Inventor
赵蕾
黄信
宋英豪
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Xinao Xinzhi Technology Co ltd
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Ennew Digital Technology Co Ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40Document-oriented image-based pattern recognition
    • G06V30/41Analysis of document content
    • G06V30/414Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/94Hardware or software architectures specially adapted for image or video understanding
    • G06V10/95Hardware or software architectures specially adapted for image or video understanding structured as a network, e.g. client-server architectures

Abstract

The embodiment of the invention discloses a case information extraction method, a case information extraction device, case information extraction equipment and a computer readable medium. The method comprises the following steps: preprocessing the acquired input image to obtain a processed input image; extracting text regions from the processed input image to obtain a text region set; and inputting the text region set into a case information extraction model trained in advance, and outputting case information, wherein the case information extraction model is obtained through joint learning training. The embodiment realizes the detection, identification and extraction of the case information of the image, and meets the requirement of a user on the extraction of the case information. In addition, by selecting part of equipment for training, data transmission of large data volume is avoided, communication cost is reduced, safety is indirectly improved, and the utilization rate of data on edge equipment is also improved.

Description

Case information extraction method, case information extraction device, case information extraction equipment and computer readable medium
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a case information extraction method, a case information extraction device, case information extraction equipment and a computer readable medium.
Background
At present, the character recognition technology is widely applied to the medical industry, but the safety of the current character recognition technology is low, and case data cannot be well analyzed. Thereby seriously influencing the application and development of the character recognition technology in the medical industry.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary of the disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the invention provides a case information extraction method, a case information extraction device, case information extraction equipment and a computer readable medium, which are used for solving the technical problems mentioned in the background technology part.
In a first aspect, an embodiment of the present disclosure provides a case information extraction method, including: preprocessing the acquired input image to obtain a processed input image; extracting text regions from the processed input image to obtain a text region set; inputting the text region set into a case information extraction model trained in advance, and outputting case information, wherein the case information extraction model is obtained through joint learning training, and the training of the case information extraction model comprises the following steps: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; and generating the case information extraction model based on the parameters of the equipment model.
In a second aspect, an embodiment of the present disclosure provides a case information extraction device, including: a processing unit configured to pre-process the acquired input image to obtain a processed input image; an extraction unit configured to perform text region extraction on the processed input image to obtain a text region set; a generation unit configured to input the text region set to a case information extraction model trained in advance, the case information extraction model being obtained by joint learning training, and output case information, the training of the case information extraction model including: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; and generating the case information extraction model based on the parameters of the equipment model.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method as described in the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in the first aspect.
One of the above embodiments disclosed by the invention has the following beneficial effects: by preprocessing the input image, the input image which is convenient to detect and identify can be obtained. Then, the text region extraction is carried out on the processed input image, so that the workload of case information extraction can be greatly reduced, and the extraction efficiency is improved. And then, inputting the text region set into a case information extraction model trained in advance to obtain case information. The method and the device realize detection, identification and extraction of case information of the image and meet the requirement of a user on case information extraction. In addition, by selecting part of equipment for training, data transmission of large data volume is avoided, communication cost is reduced, safety is indirectly improved, and the utilization rate of data on edge equipment is also improved.
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The above and other features, advantages and aspects of the disclosed embodiments will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
Fig. 1 is a schematic diagram of an application scenario of a case information extraction method according to a disclosed embodiment of the invention;
FIG. 2 is a flow chart of an embodiment of a case information extraction method according to the present disclosure;
FIG. 3 is a flow diagram of some embodiments of a case information extraction method according to the present disclosure;
fig. 4 is a schematic structural diagram of an embodiment of a case information extraction apparatus according to the present disclosure;
FIG. 5 is a schematic block diagram of an electronic device suitable for use in implementing disclosed embodiments of the invention.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments disclosed in the present invention may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules, or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules, or units.
It is noted that references to "a", "an", and "the" modifications in the disclosure are exemplary rather than limiting, and that those skilled in the art will understand that "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the disclosed embodiments are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of a case information extraction method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may pre-process an acquired input image 102, as indicated by reference numeral 103, resulting in a processed input image 104. The computing device 101 may then perform text region extraction on the processed input image 104, resulting in a text region set 105. Finally, the computing device 101 may input the text region set 105 to a pre-trained case information extraction model 106, outputting case information 107.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of an embodiment of a case information extraction method according to the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The case information extraction method comprises the following steps:
step 201, preprocessing the acquired input image to obtain a processed input image.
In an embodiment, the executing subject of the case information extraction method (e.g., the computing device 101 shown in fig. 1) may pre-process the acquired input image, resulting in a processed input image. Here, the preprocessing may be image binarization processing on the input image. The input image may be an image to be recognized containing case-related text. As an example, the case-related word may be "quinsy".
In an alternative implementation manner of the embodiment, the execution main body may acquire the input image through a wired connection manner or a wireless connection manner.
It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
Step 202, performing text region extraction on the processed input image to obtain a text region set.
In an embodiment, the executing entity may perform text detection on the processed input image to determine at least one text region. Here, the text region may be a region containing the relevant text. Then, the execution body may crop each of the at least one text region to obtain a text region set.
And step 203, inputting the text region set to a case information extraction model trained in advance, and outputting case information. In an embodiment, the execution subject may input the text region set to a case information extraction model trained in advance, and output case information. Here, the case information extraction model described above may be an already trained deep neural network (e.g., a BiLSTM-CRF model) for extracting case-related characters in an image. The case information may be a set of case-related characters contained in the input image.
In an optional implementation manner of the embodiment, the training of the scene text recognition model includes: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; and generating the case information extraction model based on the parameters of the equipment model.
In an alternative implementation manner of the embodiment, the execution main body may transmit the case information to a target display device, and control the target display device to display the case information.
One of the above embodiments disclosed by the invention has the following beneficial effects: by preprocessing the input image, the input image which is convenient to detect and identify can be obtained. Then, the text region extraction is carried out on the processed input image, so that the workload of case information extraction can be greatly reduced, and the extraction efficiency is improved. And then, inputting the text region set into a case information extraction model trained in advance to obtain case information. The method and the device realize detection, identification and extraction of case information of the image and meet the requirement of a user on case information extraction. In addition, by selecting part of equipment for training, data transmission of large data volume is avoided, communication cost is reduced, safety is indirectly improved, and the utilization rate of data on edge equipment is also improved.
With continued reference to fig. 3, a flow 300 is shown in accordance with some embodiments of the case information extraction methods disclosed herein. The method may be performed by the computing device 101 of fig. 1. The training method comprises the following steps:
in response to receiving a training request from a target user, at least one device is selected as a target device, step 301.
In an embodiment, in response to receiving a training request of a target user, an executing agent of the scene text recognition method (e.g., computing device 101 shown in fig. 1) may select (e.g., randomly select) at least one device from a library of devices as the target device. The equipment library stores at least one equipment for participating in training.
Step 302, controlling the target device to start training.
In an embodiment, the executing entity may control the target device to start training by: firstly, the execution main body can obtain an initial model and model parameters of the initial model; secondly, the executing body can transmit the model parameters to the target device; and thirdly, the executing body can control the target device to start training based on the local data of the target device. Here, the initial model may be a model that is not trained or does not reach a preset condition after training. The initial model may be a model having a deep neural network structure. The storage location of the initial model is likewise not limiting in this disclosure.
Optionally, in response to determining that the end training condition is reached, the executive may complete training. Here, the end training condition may be to perform a preset number of training tasks. The training adopts a gradient descent algorithm to iteratively update the model.
Step 303, in response to determining that the training is completed, obtaining parameters of the trained device model.
In an embodiment, in response to determining that the training is complete, the performing agent may obtain parameters of the trained device model.
Step 304, generating the case information extraction model based on the parameters of the equipment model.
In an embodiment, the executing entity may generate the case information extraction model by:
first, the execution subject transmits the parameters of the equipment model to a central server.
In an alternative implementation manner of the embodiment, the execution body may encrypt the parameter of the device model based on a preset encryption method (e.g., a symmetric encryption method or an asymmetric encryption method). Then, in response to determining that the encryption is complete, the executing agent may transmit parameters of the encrypted device model to the central server.
And secondly, the execution main body can control the central server to aggregate the parameters of the equipment model to obtain aggregated parameters.
In an optional implementation manner of the embodiment, the execution subject may control the central server to average the parameters of the device model based on a preset aggregation algorithm (e.g., an average aggregation algorithm), and obtain an average result as the aggregation parameter.
Third, the execution body may generate the case information extraction model based on the aggregation parameter.
In an optional implementation manner of the embodiment, the execution subject may determine the aggregation parameter as a parameter of the initial model, thereby obtaining the case information extraction model.
As can be seen from fig. 3, compared with the description of the embodiment corresponding to fig. 2, the flow 300 of the case information extraction method in some embodiments corresponding to fig. 3 embodies the steps of expanding how to obtain the case information extraction model. Thus, the embodiments describe schemes that can be trained by selecting a target device, controlling the target device, and then obtaining the trained parameters. The acquired trained parameters generate a case information extraction model. In addition, the parameters of the equipment model are encrypted, so that information leakage can be prevented, and the safety is improved.
With further reference to fig. 4, as an implementation of the above method for the above figures, the present disclosure provides some embodiments of a case information extraction apparatus, which correspond to those of the method embodiments described above in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 4, the case information extraction apparatus 400 of the embodiment includes: a processing unit 401, an extraction unit 402 and a generation unit 403. The processing unit 401 is configured to perform preprocessing on the acquired input image to obtain a processed input image; an extracting unit 402, configured to perform text region extraction on the processed input image, resulting in a text region set; a generating unit 403 configured to input the text region set to a case information extraction model trained in advance, the case information extraction model being obtained by joint learning training, and output case information, the training of the case information extraction model including: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; and generating the case information extraction model based on the parameters of the equipment model.
In an alternative implementation of the embodiment, the extraction unit 402 of the case information extraction device 400 is further configured to: performing text detection on the processed input image, and determining at least one text area; and cutting each text region in the at least one text region to obtain a text region set.
In an optional implementation manner of the embodiment, the controlling the target device to start training includes: obtaining an initial model and model parameters of the initial model; transmitting the model parameters to the target device; controlling the target device to start training based on the local data of the target device.
In an optional implementation manner of the embodiment, the generating the case information extraction model based on the parameters of the device model includes: transmitting parameters of the equipment model to a central server; controlling the central server to aggregate the parameters of the equipment model to obtain aggregate parameters; and generating the case information extraction model based on the aggregation parameters.
In an optional implementation manner of the embodiment, the transmitting the parameters of the device model to the central server includes: encrypting the parameters of the equipment model based on a preset encryption method; in response to determining that the encryption is complete, transmitting parameters of the encrypted device model to the central server.
In an optional implementation manner of the embodiment, the controlling the central server to aggregate the parameters of the device model to obtain an aggregated parameter includes: and controlling the central server to aggregate the parameters of the equipment model based on a preset aggregation algorithm to obtain aggregation parameters.
In an alternative implementation of the embodiment, the case information extraction device 400 is further configured to: transmitting the case information to a target display apparatus, and controlling the target display apparatus to display the case information.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to FIG. 5, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)500 suitable for use in implementing some embodiments of the present disclosure is shown. The server shown in fig. 5 is only an example, and should not bring any limitation to the function and the scope of use of the disclosed embodiments of the present invention.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. Which when executed by the processing means 501 performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium mentioned above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: preprocessing the acquired input image to obtain a processed input image; extracting text regions from the processed input image to obtain a text region set; inputting the text region set into a case information extraction model trained in advance, and outputting case information, wherein the case information extraction model is obtained through joint learning training, and the training of the case information extraction model comprises the following steps: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; and generating the case information extraction model based on the parameters of the equipment model.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. 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 should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. 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.
The units described in some embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a processing unit, an extraction unit, and a generation unit. The names of these units do not in some cases form a limitation on the units themselves, and for example, the processing unit may also be described as a "unit that preprocesses the acquired input image to obtain a processed input image".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the present disclosure and is provided for the purpose of illustrating the general principles of the technology. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments disclosed in the present application is not limited to the embodiments with specific combinations of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims (10)

1. A case information extraction method, characterized by comprising:
preprocessing the acquired input image to obtain a processed input image;
extracting text regions from the processed input image to obtain a text region set;
inputting the text region set into a case information extraction model trained in advance, and outputting case information, wherein the case information extraction model is obtained through joint learning training, and the training of the case information extraction model comprises the following steps:
in response to receiving a training request of a target user, selecting at least one device as a target device;
controlling the target device to start training;
in response to determining that the training is complete, obtaining parameters of the trained device model;
and generating the case information extraction model based on the parameters of the equipment model.
2. The method according to claim 1, wherein extracting text regions from the processed input image to obtain a text region set comprises:
performing text detection on the processed input image, and determining at least one text area;
and cutting each text region in the at least one text region to obtain a text region set.
3. A case information extraction method according to claim 1, wherein the controlling the target apparatus to start training includes:
obtaining an initial model and model parameters of the initial model;
transmitting the model parameters to the target device;
controlling the target device to start training based on the local data of the target device.
4. A case information extraction method according to claim 3, wherein the generating the case information extraction model based on the parameters of the device model includes:
transmitting parameters of the equipment model to a central server;
controlling the central server to aggregate the parameters of the equipment model to obtain aggregate parameters;
and generating the case information extraction model based on the aggregation parameters.
5. The case information extraction method according to claim 4, wherein the transmitting the parameters of the device model to a central server includes:
encrypting the parameters of the equipment model based on a preset encryption method;
in response to determining that the encryption is complete, transmitting parameters of the encrypted device model to the central server.
6. The method as claimed in claim 4, wherein the controlling the central server to aggregate the parameters of the device model to obtain aggregated parameters includes:
and controlling the central server to aggregate the parameters of the equipment model based on a preset aggregation algorithm to obtain aggregation parameters.
7. The method for extracting case information according to any one of claims 1 to 6, further comprising:
transmitting the case information to a target display apparatus, and controlling the target display apparatus to display the case information.
8. A case information extraction device characterized by comprising:
a processing unit configured to pre-process the acquired input image to obtain a processed input image;
an extraction unit configured to perform text region extraction on the processed input image to obtain a text region set;
a generation unit configured to input the text region set to a case information extraction model trained in advance, the case information extraction model being obtained by joint learning training, and output case information, the training of the case information extraction model including:
in response to receiving a training request of a target user, selecting at least one device as a target device;
controlling the target device to start training;
in response to determining that the training is complete, obtaining parameters of the trained device model;
and generating the case information extraction model based on the parameters of the equipment model.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
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