CN110991522A - Character recognition method and system and industrial intelligent gateway - Google Patents
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Abstract
The present disclosure discloses a character recognition method, a system and an industrial intelligent gateway, including: an image acquisition step: acquiring an image to be identified; an image caching step: caching the acquired image to be identified; an image processing step: preprocessing the cached image to be identified; cutting the preprocessed image to be recognized, and performing character segmentation on the cut image to be recognized; scaling the image after the character segmentation, and extracting the features of the image after scaling; matching the extracted features with character features of a pre-constructed template library, and outputting a character recognition result; an image display step: displaying the character recognition result; in the image processing step, the ARM reads the cache image, the cache image is transmitted to the FPGA after being obtained, the FPGA processes the image, the FPGA identifies characters, the character identification result is transmitted to the ARM, and finally the character identification result is displayed.
Description
Technical Field
The present disclosure relates to the field of visual inspection technologies, and in particular, to a character recognition method, a character recognition system, and an industrial intelligent gateway.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The machine vision detection is characterized by improving the production efficiency and the automation degree. In some dangerous working environments which are not suitable for manual operation or occasions which are difficult for manual vision to meet the requirements, machine vision is commonly used to replace the manual vision; meanwhile, in the process of mass industrial production, the efficiency of checking the product quality by using manual vision is low, the precision is not high, and the production efficiency and the automation degree of production can be greatly improved by using a machine vision detection method. And the machine vision is easy to realize information integration, and is a basic technology for realizing computer integrated manufacturing. Visual inspection is to use a robot to replace human eyes for measurement and judgment.
In the course of implementing the present disclosure, the inventors found that the following technical problems exist in the prior art:
visual inspection has huge market value, important in a visual inspection system are not only algorithm steps, but also hardware equipment, especially in the embedded field, but also in the character recognition field, the use cost of most technical schemes in the market is high, and meanwhile, the algorithm steps are complex, so that the operations of ordinary technicians such as parameter changing and the like are not facilitated.
Disclosure of Invention
In order to solve the defects of the prior art, the present disclosure provides a character recognition method, a system and an industrial intelligent gateway; the problems of poor real-time performance and low efficiency caused by technical reasons in the traditional character recognition system and the problem of resource waste caused by large volume of the character recognition system are solved, and the requirements of real-time performance and accuracy in character detection and recognition can be met.
In a first aspect, the present disclosure provides a character recognition method;
a character recognition method, comprising:
an image acquisition step: acquiring an image to be identified;
an image caching step: caching the acquired image to be identified;
an image processing step: preprocessing the cached image to be identified; cutting the preprocessed image to be recognized, and performing character segmentation on the cut image to be recognized; scaling the image after the character segmentation, and extracting the features of the image after scaling; matching the extracted features with character features of a pre-constructed template library, and outputting a character recognition result;
an image display step: displaying the character recognition result;
in the image processing step, the ARM reads the cache image, the cache image is transmitted to the FPGA after being obtained, the FPGA processes the image and recognizes characters, the FPGA transmits the character recognition result to the ARM, and finally the character recognition result is displayed.
In a second aspect, the present disclosure also provides a character recognition system;
a character recognition system comprising:
an image acquisition module: acquiring an image to be identified;
an image caching module: caching the acquired image to be identified;
an image processing module: preprocessing the cached image to be identified; cutting the preprocessed image to be recognized, and performing character segmentation on the cut image to be recognized; scaling the image after the character segmentation, and extracting the features of the image after scaling; matching the extracted features with character features of a pre-constructed template library, and outputting a character recognition result;
an image display module: displaying the character recognition result;
in the image processing module, the ARM reads the cache image, the cache image is transmitted to the FPGA after being obtained, the FPGA processes the image, the FPGA identifies characters, the FPGA transmits character identification results to the ARM, and finally the character identification results are displayed.
In a third aspect, the present disclosure provides an industrial intelligent gateway;
an industrial intelligent gateway, comprising: a central processor including an ARM (Advanced RISC Machine) and an FPGA (Field programmable gate array) connected to each other through an AXI-HP high-speed bus;
the ARM is used for storing a first computer instruction, and the first computer instruction is executed by the ARM to complete the image acquisition step, the image caching step and the image display step of the first embodiment;
the FPGA is used for storing a second computer instruction, and the second computer instruction is used for completing the image processing step of the first embodiment when the second computer instruction is executed by the FPGA;
the ARM reads the cache image, the cache image is transmitted to the FPGA after the cache image is obtained, the FPGA preprocesses the image, the FPGA recognizes characters according to a character recognition algorithm, the FPGA transmits a character recognition result to the ARM, and finally, the ARM displays the character recognition result.
Compared with the prior art, the beneficial effect of this disclosure is:
1, a central processing unit consisting of an ARM and an FPGA is utilized to make up the real-time effect which cannot be achieved by the ARM;
2, data acquisition is carried out by adopting a character recognition algorithm, so that the robustness is good, the accuracy is high, and the real-time checking and comparison can be realized;
3 the monitoring system has small equipment and does not need excessive manual intervention and control.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flowchart of a method according to a first embodiment of the disclosure;
fig. 2 is a schematic structural diagram of an industrial intelligent gateway according to a first embodiment of the present disclosure;
fig. 3 is a schematic view of an application scenario of character recognition of an industrial intelligent gateway according to a first embodiment of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In a first embodiment, the present embodiment provides a character recognition method;
as shown in fig. 1, a character recognition method includes:
s1: an image acquisition step: acquiring an image to be identified;
s2: an image caching step: caching the acquired image to be identified;
s3: an image processing step: preprocessing the cached image to be identified; cutting the preprocessed image to be recognized, and performing character segmentation on the cut image to be recognized; scaling the image after the character segmentation, and extracting the features of the image after scaling; matching the extracted features with character features of a pre-constructed template library, and outputting a character recognition result;
s4: an image display step: displaying the character recognition result;
in the image processing step, the ARM reads the cache image, the cache image is transmitted to the FPGA after being obtained, the FPGA processes the image and recognizes characters, the FPGA transmits the character recognition result to the ARM, and finally the character recognition result is displayed.
As one or more embodiments, the image to be identified is pre-processed; the method comprises the following specific steps:
and (3) carrying out chrominance space conversion on the image, namely converting the RGB image into a gray level image, carrying out image enhancement on the gray level image, and finally carrying out binarization processing on the image.
As one or more embodiments, the pre-processed image to be recognized is cut; the method comprises the following specific steps:
and determining the area to be identified according to the selection instruction of the user for the preprocessed image to be identified.
As one or more embodiments, the character segmentation is performed on the cut image to be recognized; the method comprises the following specific steps:
and performing character segmentation on the area to be recognized, and segmenting the character string in the image into a plurality of characters.
As one or more embodiments, the scaling is performed on the image after the character segmentation; the method comprises the following specific steps:
all characters are scaled to a uniform size.
As one or more embodiments, the scaling of the image is performed by feature extraction; the method comprises the following specific steps: extracting the texture features of each character.
As one or more embodiments, the matching of the extracted features with character features of a pre-constructed template library; the method comprises the following specific steps: and calculating the Euclidean distance between the extracted features and the character features of the pre-constructed template library, wherein the character in the pre-constructed template library corresponding to the minimum Euclidean distance is the recognition result of the character to be recognized.
The pre-constructed template library comprises: a known character and character features corresponding to the known character.
It should be understood that the euclidean distance refers to:
wherein X, Y are two points in a two-dimensional space, XiAnd yiData in two points.
In the second embodiment, the present embodiment provides a character recognition system;
a character recognition system comprising:
an image acquisition module: acquiring an image to be identified;
an image caching module: caching the acquired image to be identified;
an image processing module: preprocessing the cached image to be identified; cutting the preprocessed image to be recognized, and performing character segmentation on the cut image to be recognized; scaling the image after the character segmentation, and extracting the features of the image after scaling; matching the extracted features with character features of a pre-constructed template library, and outputting a character recognition result;
an image display module: displaying the character recognition result;
in the image processing module, the ARM reads the cache image, the cache image is transmitted to the FPGA after being obtained, the FPGA processes the image, the FPGA identifies characters, the FPGA transmits character identification results to the ARM, and finally the character identification results are displayed.
The third embodiment provides an industrial intelligent gateway;
as shown in fig. 2, an industrial intelligent gateway includes: a central processor including an ARM (Advanced RISC Machine) and an FPGA (field programmable Gate array) connected to each other through an AXI-HP high-speed bus;
the ARM is used for storing a first computer instruction, and the first computer instruction is executed by the ARM to complete the image acquisition step, the image caching step and the image display step of the first embodiment;
the FPGA is used for storing a second computer instruction, and the second computer instruction is used for completing the image processing step of the first embodiment when the second computer instruction is executed by the FPGA;
the ARM reads the cache image, the cache image is transmitted to the FPGA after the cache image is obtained, the FPGA preprocesses the image, the FPGA recognizes characters according to a character recognition algorithm, the FPGA transmits a character recognition result to the ARM, and finally, the ARM displays the character recognition result.
Furthermore, the reading of the cached image by the ARM means that the ARM reads the cached image from the image storage module. The image in the image storage module is stored in the image storage module by the image acquisition module.
Furthermore, the FPGA in the central processing unit sends the recognized characters to the ARM through a high-speed bus, and the ARM transmits the recognized characters to the MES through the Ethernet module or the wireless communication module according to the requirements of a communication protocol.
Further, the central processing unit displays the character recognition result through the image display module. The image display module is a display interface manufactured by QT and displays the incoming image and the recognized characters on the display, a user can freely select a character area to be recognized, at most 20 positions can be selected, and after the characters are recognized by a character recognition algorithm, the recognized characters can be displayed in the image in the display interface and can be contrasted and viewed by the user.
Furthermore, the central processing unit is also connected with a user terminal butt joint module, the user terminal butt joint module is also connected with a user authentication module, and the user authentication module is also connected with the user terminal.
The user terminal butt-joint module is a mobile network. The user terminal is connected with a mobile network by using an APP (Application), then the connection between the industrial intelligent gateway and the APP is established, the user terminal logs in the APP through an account number and a password, a face or a fingerprint to be authenticated with the industrial intelligent gateway, if the user terminal passes the authentication, the communication connection between the industrial intelligent gateway and the user terminal is established, if the user terminal does not pass the authentication, a message of authentication failure is sent to the user terminal, and the specifically adopted authentication protocol and the key are not limited here.
The user authentication module authenticates the user terminal according to a user account number and a password, a face or a fingerprint carried by the authentication request; and if the user terminal passes the verification, establishing communication connection between the industrial intelligent gateway and the user terminal, and if the user terminal does not pass the verification, sending an authentication failure message to the user terminal.
Furthermore, the central processing unit is further connected with an equipment docking module, and the equipment docking module is used for being connected with third-party equipment. The central processing unit collects data of the third-party equipment and uploads the identified characters to an MES (Manufacturing Execution System) through the wireless communication module and the Ethernet module. The third-party equipment comprises an industrial personal computer, a numerical control system and the like.
Furthermore, the central processing unit is also connected with a power supply module; the power supply module provides a stable working power supply for system work.
Furthermore, the central processing unit is also connected with the wireless communication module and the Ethernet module;
furthermore, the Ethernet module can realize the remote transmission of data through an RJ45 interface, and improves the adaptability of the industrial intelligent gateway through the combination of an RJ45 interface and a USB interface, thereby solving the problems of quick plug-in connection between the gateway and third-party equipment, parallel acquisition and remote transmission between the industrial intelligent gateway and third-party equipment and between the industrial intelligent gateway and an MES (manufacturing execution system);
the Wireless communication module, WiFi (Wireless Fidelity) Wireless network card, adopts a high-speed network card which meets the IEEE802.11a/b/g/n and above Wireless transmission standards, provides a stable WiFi Wireless network, can reach 300Mbps at the fastest theoretical speed, and provides network support for the whole network;
the high-speed network card meeting the wireless transmission standard of IEEE802.11a/b/g/n and above is connected with the central processing unit through a USB (Universal Serial Bus) interface circuit;
further, the central processing unit is also connected with an equipment protocol library; the equipment protocol library comprises a third-party communication protocol and a host communication protocol; and data transmission and information communication between the central processing unit and the third-party equipment are realized.
The host communication protocol comprises a TCP/IP protocol, an IPX/SPX protocol, a NetBEUI protocol and the like; the third-party communication protocol comprises a CAN protocol, a Profibus protocol, a LonWork protocol, an industrial Ethernet protocol and the like.
Furthermore, the central processing unit is also connected with a monitoring alarm module;
and the monitoring alarm module sends out an alarm to inform workers of timely processing when an error occurs in the process of data acquisition and uploading to the MES.
Monitoring alarm module still is used for monitoring industry intelligent gateway's operating condition, if industry intelligent gateway breaks down, then monitoring alarm module sends fault information first time for the host computer through wireless communication module, and the host computer sends for staff's mobile terminal, and then guarantees that the staff can know industry intelligent gateway's operating condition the very first time, and the maintenance team of specialty is sent to the very first time goes the maintenance before.
Furthermore, the ARM and the FPGA are connected through an AXI-HP high-speed bus.
Further, the ARM is used for designing an embedded Linux system and a display interface and monitoring image data acquisition;
furthermore, the FPGA breaks through a sequential execution mode by utilizing the advantage of hardware parallelism, realizes the parallel processing of data and reduces the channel processing time;
FIG. 3 is a schematic diagram of an application scenario of character recognition of an industrial intelligent gateway provided by the present invention, including a third-party device, an industrial intelligent gateway, a communication network, and a user terminal; and the embedded industrial intelligent gateway is respectively connected with the third-party equipment and the user terminal.
The user terminal comprises a mobile terminal (such as a smart phone), a touch screen (such as a tablet personal computer), a Personal Computer (PC) and the like, remote monitoring and control of third-party equipment are achieved, the acquisition and transmission conditions of equipment data are checked in real time, and when the acquisition and transmission of the equipment data are in trouble, the user terminal sends change information and configuration instructions to the third-party equipment through the industrial intelligent gateway or sends alarm information to the monitoring alarm module to inform a worker of timely processing.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A character recognition method is characterized by comprising the following steps:
an image acquisition step: acquiring an image to be identified;
an image caching step: caching the acquired image to be identified;
an image processing step: preprocessing the cached image to be identified; cutting the preprocessed image to be recognized, and performing character segmentation on the cut image to be recognized; scaling the image after the character segmentation, and extracting the features of the image after scaling; matching the extracted features with character features of a pre-constructed template library, and outputting a character recognition result;
an image display step: displaying the character recognition result;
in the image processing step, the ARM reads the cache image, the cache image is transmitted to the FPGA after being obtained, the FPGA processes the image and recognizes characters, the FPGA transmits the character recognition result to the ARM, and finally the character recognition result is displayed.
2. The method according to claim 1, characterized in that the image to be recognized is preprocessed; the method comprises the following specific steps:
carrying out chrominance space conversion on the image, namely converting an RGB image into a gray image, then carrying out image enhancement on the gray image, and finally carrying out binarization processing on the image;
alternatively, the first and second electrodes may be,
cutting the preprocessed image to be recognized; the method comprises the following specific steps:
determining a region to be identified according to a selection instruction of a user for the preprocessed image to be identified;
alternatively, the first and second electrodes may be,
performing character segmentation on the cut image to be recognized; the method comprises the following specific steps:
carrying out character segmentation on the area to be recognized, and segmenting character strings in the image into a plurality of characters;
alternatively, the first and second electrodes may be,
carrying out scale scaling on the image obtained after the character segmentation; the method comprises the following specific steps:
scaling all characters to a uniform size;
alternatively, the first and second electrodes may be,
carrying out feature extraction on the image after the scaling; the method comprises the following specific steps: extracting the texture features of each character;
alternatively, the first and second electrodes may be,
matching the extracted features with character features of a pre-constructed template library; the method comprises the following specific steps: and calculating the Euclidean distance between the extracted features and the character features of the pre-constructed template library, wherein the character in the pre-constructed template library corresponding to the minimum Euclidean distance is the recognition result of the character to be recognized.
3. A character recognition system, comprising:
an image acquisition module: acquiring an image to be identified;
an image caching module: caching the acquired image to be identified;
an image processing module: preprocessing the cached image to be identified; cutting the preprocessed image to be recognized, and performing character segmentation on the cut image to be recognized; scaling the image after the character segmentation, and extracting the features of the image after scaling; matching the extracted features with character features of a pre-constructed template library, and outputting a character recognition result;
an image display module: displaying the character recognition result;
in the image processing module, the ARM reads the cache image, the cache image is transmitted to the FPGA after being obtained, the FPGA processes the image, the FPGA identifies characters, the FPGA transmits character identification results to the ARM, and finally the character identification results are displayed.
4. An industrial intelligent gateway, characterized by, includes: a central processor including an ARM and an FPGA connected to each other by an AXI-HP high speed bus;
wherein the ARM is configured to store a first computer instruction, and the first computer instruction when executed by the ARM performs the image capturing step, the image caching step, and the image displaying step of the method of claim 1;
the FPGA for storing second computer instructions which, when executed by the FPGA, perform the image processing steps of the method of claim 1;
the ARM reads the cache image, the cache image is transmitted to the FPGA after the cache image is obtained, the FPGA preprocesses the image, the FPGA recognizes characters according to a character recognition algorithm, the FPGA transmits a character recognition result to the ARM, and finally, the ARM displays the character recognition result.
5. The gateway of claim 4, wherein the FPGA in the CPU feeds back the recognized characters to the ARM through a high-speed bus, and the ARM transmits the recognized characters to the MES through the Ethernet module or the wireless communication module according to the requirements of the communication protocol.
6. The gateway of claim 4, wherein said central processor is further coupled to a user terminal docking module, said user terminal docking module being further coupled to a user authentication module, said user authentication module being further coupled to a user terminal.
7. The gateway of claim 6, wherein the user terminal interfacing module is a mobile network; the user terminal uses the APP to be connected with a mobile network, then the connection between the industrial intelligent gateway and the APP is established, the user terminal logs in the APP through an account number and a password, a human face or a fingerprint to be authenticated with the industrial intelligent gateway, if the user terminal passes the authentication, the communication connection between the industrial intelligent gateway and the user terminal is established, if the user terminal does not pass the authentication, a message of authentication failure is sent to the user terminal, and the authentication protocol and the secret key which are specifically adopted are not limited here.
8. The gateway of claim 6, wherein the user authentication module authenticates the user terminal according to a user account and a password, a face or a fingerprint carried by the authentication request; and if the user terminal passes the verification, establishing communication connection between the industrial intelligent gateway and the user terminal, and if the user terminal does not pass the verification, sending an authentication failure message to the user terminal.
9. The gateway of claim 4, wherein said central processor is further connected to a device docking module for connection to a third party device; and the central processing unit is used for acquiring data of the third-party equipment and uploading the identified characters to a Manufacturing Execution System (MES) through the wireless communication module and the Ethernet module.
10. The gateway of claim 4, wherein said central processor is further connected to a monitoring alarm module; the monitoring alarm module sends out an alarm to inform workers of timely processing when an error occurs in the process of data acquisition and MES uploading;
the monitoring and alarming module is further used for monitoring the working state of the industrial intelligent gateway, if the industrial intelligent gateway breaks down, the monitoring and alarming module sends the fault information to the upper computer at a first time through the wireless communication module, and the upper computer sends the fault information to the mobile terminal of the worker.
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| CN115147840A (en) * | 2021-03-31 | 2022-10-04 | 广东高云半导体科技股份有限公司 | Artificial intelligence system and method for character recognition |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104243931A (en) * | 2014-09-29 | 2014-12-24 | 唐子贤 | ARM (random access memory) camera interface based video collection displaying system |
| CN204155449U (en) * | 2014-09-04 | 2015-02-11 | 上海智达商投资管理合伙企业(有限合伙) | A kind of Car license recognition comparison high-definition camera and supervisory system |
| CN105182275A (en) * | 2015-04-01 | 2015-12-23 | 无锡桑尼安科技有限公司 | Outdoor automatic electric meter fault detection method |
| CN106250939A (en) * | 2016-07-30 | 2016-12-21 | 复旦大学 | System for Handwritten Character Recognition method based on FPGA+ARM multilamellar convolutional neural networks |
| CN106899498A (en) * | 2017-04-13 | 2017-06-27 | 山东万腾电子科技有限公司 | Embedded industry intelligent gateway and its real-time data acquisition method based on SoC |
| CN107065668A (en) * | 2017-04-13 | 2017-08-18 | 山东万腾电子科技有限公司 | Industrial gateway remote data monitoring system and method based on intelligent sensor device |
| CN108615058A (en) * | 2018-05-10 | 2018-10-02 | 苏州大学 | A kind of method, apparatus of character recognition, equipment and readable storage medium storing program for executing |
| CN108986144A (en) * | 2018-08-27 | 2018-12-11 | 广州烽火众智数字技术有限公司 | A kind of vehicle identification method for tracing, system and device based on ARM and FPGA |
| CN109409272A (en) * | 2018-10-17 | 2019-03-01 | 北京空间技术研制试验中心 | Cable Acceptance Test System and method based on machine vision |
| CN109541475A (en) * | 2018-12-18 | 2019-03-29 | 武汉精能电子技术有限公司 | The method that automatic identification is carried out to multiple functions module simultaneously |
-
2019
- 2019-11-29 CN CN201911201586.7A patent/CN110991522A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN204155449U (en) * | 2014-09-04 | 2015-02-11 | 上海智达商投资管理合伙企业(有限合伙) | A kind of Car license recognition comparison high-definition camera and supervisory system |
| CN104243931A (en) * | 2014-09-29 | 2014-12-24 | 唐子贤 | ARM (random access memory) camera interface based video collection displaying system |
| CN105182275A (en) * | 2015-04-01 | 2015-12-23 | 无锡桑尼安科技有限公司 | Outdoor automatic electric meter fault detection method |
| CN106250939A (en) * | 2016-07-30 | 2016-12-21 | 复旦大学 | System for Handwritten Character Recognition method based on FPGA+ARM multilamellar convolutional neural networks |
| CN106899498A (en) * | 2017-04-13 | 2017-06-27 | 山东万腾电子科技有限公司 | Embedded industry intelligent gateway and its real-time data acquisition method based on SoC |
| CN107065668A (en) * | 2017-04-13 | 2017-08-18 | 山东万腾电子科技有限公司 | Industrial gateway remote data monitoring system and method based on intelligent sensor device |
| CN108615058A (en) * | 2018-05-10 | 2018-10-02 | 苏州大学 | A kind of method, apparatus of character recognition, equipment and readable storage medium storing program for executing |
| CN108986144A (en) * | 2018-08-27 | 2018-12-11 | 广州烽火众智数字技术有限公司 | A kind of vehicle identification method for tracing, system and device based on ARM and FPGA |
| CN109409272A (en) * | 2018-10-17 | 2019-03-01 | 北京空间技术研制试验中心 | Cable Acceptance Test System and method based on machine vision |
| CN109541475A (en) * | 2018-12-18 | 2019-03-29 | 武汉精能电子技术有限公司 | The method that automatic identification is carried out to multiple functions module simultaneously |
Non-Patent Citations (2)
| Title |
|---|
| 周立波;: "基于FPGA的高速字符识别***", 中国新技术新产品, no. 22, pages 16 - 17 * |
| 陆建 等: "《家庭电脑学校 基础篇》", 30 April 2003, 上海科学技术出版社, pages: 159 - 160 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115147840A (en) * | 2021-03-31 | 2022-10-04 | 广东高云半导体科技股份有限公司 | Artificial intelligence system and method for character recognition |
| CN115147840B (en) * | 2021-03-31 | 2024-05-17 | 广东高云半导体科技股份有限公司 | Artificial intelligence system and method for character recognition |
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