CN116580390A - Price tag content acquisition method, price tag content acquisition device, storage medium and computer equipment - Google Patents
Price tag content acquisition method, price tag content acquisition device, storage medium and computer equipment Download PDFInfo
- Publication number
- CN116580390A CN116580390A CN202310563499.6A CN202310563499A CN116580390A CN 116580390 A CN116580390 A CN 116580390A CN 202310563499 A CN202310563499 A CN 202310563499A CN 116580390 A CN116580390 A CN 116580390A
- Authority
- CN
- China
- Prior art keywords
- data
- commodity
- matching
- price tag
- price
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000001514 detection method Methods 0.000 claims abstract description 31
- 238000004590 computer program Methods 0.000 claims description 17
- 238000012544 monitoring process Methods 0.000 abstract description 6
- 230000009286 beneficial effect Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 208000001840 Dandruff Diseases 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 239000002453 shampoo Substances 0.000 description 3
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
- G06V30/153—Segmentation of character regions using recognition of characters or words
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/19—Recognition using electronic means
- G06V30/19007—Matching; Proximity measures
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a price tag content acquisition method, a price tag content acquisition device, a storage medium and computer equipment, wherein the method comprises the following steps: acquiring a price tag picture of a target price tag; detecting the name area of the bidding label picture and commodity price through a target detection algorithm; performing content recognition on the name area through an OCR algorithm to obtain a commodity name; matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data; matching price data of a plurality of first candidate commodity data of the matching result with commodity price to obtain matching data; and determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag. The application can accurately acquire the price tag content on the electronic price tag and the non-electronic price tag, and is beneficial to realizing real-time monitoring and management of commodities on a goods shelf.
Description
Technical Field
The application relates to the technical field of detection and identification of price tag content, in particular to a price tag content acquisition method, a price tag content acquisition device, a storage medium and computer equipment.
Background
The price tag is a prop for marking commodity information, and with the generation of the electronic price tag, a merchant can broadcast commodity information and large-size special characters on the electronic price tag in turn, so that a purchaser can know commodity names and prices through the commodity information on the electronic price tag conveniently, the merchant can identify the special characters by utilizing an algorithm, obtain commodity information pointed by the special characters, and therefore real-time monitoring and management of commodities on a goods shelf are achieved, and efficiency and accuracy of goods shelf management are improved. However, the conventional non-electronic price tag cannot display commodity information and large-size special characters at the same time due to size limitation, and cannot broadcast commodity information and large-size special characters in turn, so that real-time monitoring and management of commodities with the non-electronic price tag on a shelf are difficult to realize.
Disclosure of Invention
The application aims to overcome the defects and shortcomings in the prior art and provides a price tag content acquisition method, a price tag content acquisition device, a storage medium and computer equipment, which can accurately acquire price tag contents on electronic price tags and non-electronic price tags and are beneficial to realizing real-time monitoring and management on commodities on a goods shelf.
One embodiment of the present application provides a price tag content acquiring method, including:
acquiring a price tag picture of a target price tag;
detecting a name area and commodity price of the price tag picture through a target detection algorithm;
performing content recognition on the name area through an OCR algorithm to obtain a commodity name;
matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data;
matching price data of a plurality of first candidate commodity data of the matching result with the commodity price to obtain matching data;
and determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag.
Further, the step of detecting the name area of the price tag picture and the commodity price through a target detection algorithm includes:
detecting a name area and a price area of the price tag picture through the target detection algorithm;
and identifying the price number and the price symbol of the price area through the target detection algorithm to obtain the commodity price.
Further, matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data, and the matching result comprises the following steps:
obtaining the similarity between the commodity name and each commodity information data stored in the commodity information database;
and determining the first plurality of commodity information data with the highest similarity as the first candidate commodity data.
Further, the matching data includes matching values of the respective first candidate commodity data and the commodity price;
the step of determining target commodity data from the first candidate commodity data of the matching result according to the matching data and determining the target commodity data as the price tag content of the target price tag comprises the following steps:
and if the matching values of the matching data are smaller than a preset matching threshold, determining the first candidate commodity data with highest similarity as the target commodity data from the matching result.
Further, the step of determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag includes:
and if one matching value in the matching data is greater than or equal to a preset matching threshold value, determining the corresponding first candidate commodity data as the target commodity data.
Further, the step of determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag includes:
if at least two matching values in the matching data are larger than or equal to a preset matching threshold, determining the corresponding first candidate commodity data as second candidate commodity data;
and obtaining the similarity between each piece of second candidate commodity data and the commodity information according to the matching result, and determining the second candidate commodity data with the highest similarity as the target commodity data.
Further, the step of determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag includes:
and if a plurality of second candidate commodity data with the highest similarity exist, randomly selecting one to be determined as the target commodity data from the plurality of second candidate commodity data with the highest similarity.
One embodiment of the present application also provides a price tag content acquiring apparatus, including:
the image acquisition module is used for acquiring a price tag image of the target price tag;
the detection module is used for detecting the name area and commodity price of the price tag picture through a target detection algorithm;
the identification module is used for carrying out content identification on the name area through an OCR algorithm to obtain a commodity name;
the first matching module is used for matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data;
the second matching module is used for matching the price data of the plurality of first candidate commodity data of the matching result with the commodity price to obtain matching data;
and the price tag content acquisition module is used for determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as price tag content of the target price tag.
An embodiment of the present application also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the price tag content acquisition method described above.
An embodiment of the present application also provides a computer apparatus including a memory, a processor, and a computer program stored in the memory and executable by the processor, the processor implementing the steps of the price tag content acquisition method as described above when executing the computer program.
Compared with the related art, the method and the device detect the name area and commodity price of the bidding price picture through the target detection algorithm, match the commodity name obtained from the name area through the OCR algorithm with the pre-built commodity information database, acquire the price data of a plurality of first candidate commodity data of the matching result and the matching data of the commodity price, determine the target commodity data from the plurality of first candidate commodity data of the matching result according to the matching data, and accordingly obtain the price content of the target price, whether the price corresponding to the price picture is an electronic price or a traditional non-electronic price, the method and the device can accurately acquire the price content on the price, and are beneficial to real-time monitoring and management of commodities on a shelf.
In order that the application may be more clearly understood, specific embodiments thereof will be described below with reference to the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a price tag content acquiring method according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for acquiring content of a price tag according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a price tag picture of a price tag content acquiring method according to an embodiment of the present application.
Fig. 4 is a schematic block diagram of a price tag content acquiring apparatus according to an embodiment of the present application.
1. A picture acquisition module; 2. a detection module; 3. an identification module; 4. a first matching module; 5. a second matching module; 6. and the price tag content acquisition module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the following detailed description of the embodiments of the present application will be given with reference to the accompanying drawings.
It should be understood that the described embodiments are merely some, but not all embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the application, are intended to be within the scope of the embodiments of the present application.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. In the description of the present application, it should be understood that the terms "first," "second," "third," and the like are used merely to distinguish between similar objects and are not necessarily used to describe a particular order or sequence, nor should they be construed to indicate or imply relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The word "if"/"if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination".
Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
Referring to fig. 1-2, a price tag content acquiring method according to a first embodiment of the present application includes:
s1: and acquiring a price tag picture of the target price tag.
The price tag picture can be obtained through shooting equipment, for example, price tags of various commodities on the goods shelf are shot through shooting equipment corresponding to the goods shelf, and the price tag picture is obtained. Alternatively, the photographing apparatus may be an AI camera or an AI robot.
S2: and detecting the name area of the price tag picture and the commodity price through a target detection algorithm.
The target detection algorithm is based on deep learning, and is trained by a price tag picture training sample marked with a name area, a price area and commodity price on the price area, so that the target detection algorithm for detecting the name area and commodity price of the price tag picture can be obtained.
S3: and carrying out content recognition on the name area through an OCR algorithm to obtain the commodity name.
The OCR algorithm, namely a character recognition algorithm, is an effective image processing algorithm specially aiming at character recognition and detection, has the advantages of high accuracy and high stability, and can recognize tens of characters such as Chinese, english, japanese, korean, arabic, italian and the like.
S4: matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data.
Matching the commodity name with the pre-constructed commodity information database is realized based on a database text matching algorithm of a Transformer, and the obtained matching result greatly reduces the number of first candidate commodity data.
S5: and matching the price data of the first candidate commodity data of the matching result with the commodity price to obtain matching data.
The matching data comprises matching conditions of price data of a plurality of first candidate commodity data of the matching result and commodity price, and the matching conditions can be used for indicating the first candidate commodity data which is most in line with the price picture.
S6: and determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag.
Compared with the related art, the method and the device detect the name area and commodity price of the bidding price picture through the target detection algorithm, match the commodity name obtained from the name area through the OCR algorithm with the pre-built commodity information database, acquire the price data of a plurality of first candidate commodity data of the matching result and the matching data of the commodity price, determine the target commodity data from the plurality of first candidate commodity data of the matching result according to the matching data, and accordingly obtain the price content of the target price, whether the price corresponding to the price picture is an electronic price or a traditional non-electronic price, the method and the device can accurately acquire the price content on the price, and are beneficial to real-time monitoring and management of commodities on a shelf. In addition, the accuracy of the obtained price tag content can be further improved in a two-time data matching mode.
In one possible embodiment, the step S2: detecting the name area of the price tag picture and the commodity price through a target detection algorithm, wherein the method comprises the following steps of:
s21: and detecting the name area and the price area of the price tag picture through the target detection algorithm.
S22: and identifying the price number and the price symbol of the price area through the target detection algorithm to obtain the commodity price.
In this embodiment, since the commodity price is composed of simple numbers and number numbers, the target detection algorithm based on deep learning can also detect and classify the numbers and the number symbols, so as to obtain the commodity price of the commodity according to the classification result output by the algorithm.
For example, as shown in fig. 3, the target detection algorithm may detect an "X soft long-acting anti-dandruff shampoo X500ml" region corresponding to a product name and a "9", "9", etc. region corresponding to a commodity price, where "X" represents a blurry chinese character. The target detection algorithm based on the deep learning not only can give specific position coordinates of the target area, but also can classify the target area.
In one possible embodiment, the step S4: matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data, and the matching result comprises the following steps:
s41: and obtaining the similarity between the commodity name and each commodity information data stored in the commodity information database.
S42: and determining the first plurality of commodity information data with the highest similarity as the first candidate commodity data.
In this embodiment, considering whether the content of the target price tag is blurred, whether the price tag picture is blocked, and the like, the first plurality of commodity information data with the highest similarity are determined as first candidate commodity data, for example, the first three commodity information data with the highest similarity are determined as first candidate commodity data, and then the first candidate commodity data is used for secondary matching, so that the accuracy of obtaining the price tag content can be further improved.
In a possible embodiment, the matching data includes a matching value of each of the first candidate commodity data and the commodity price;
the S6: determining target commodity data from a plurality of first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag, wherein the step comprises the following steps:
s61: and if the matching values of the matching data are smaller than a preset matching threshold, determining the first candidate commodity data with highest similarity as the target commodity data from the matching result.
The matching threshold is a proportion value preset by a user, and can be used as a condition parameter for judging whether the matching value of the matching data meets the requirement of the user. For example, the matching threshold may be set to 100%, 98%, 90%, etc.
In one possible embodiment, the step S6: determining target commodity data from a plurality of first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag, wherein the step comprises the following steps:
s62: and if one matching value in the matching data is greater than or equal to a preset matching threshold value, determining the corresponding first candidate commodity data as the target commodity data.
And when the matching value is larger than or equal to a preset matching threshold value, the price data of the corresponding first candidate commodity data is matched with the commodity price.
In one possible embodiment, the step S6: determining target commodity data from a plurality of first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag, wherein the step comprises the following steps:
s631: if at least two matching values in the matching data are larger than or equal to a preset matching threshold, determining the corresponding first candidate commodity data as second candidate commodity data;
s632: and obtaining the similarity between each piece of second candidate commodity data and the commodity information according to the matching result, and determining the second candidate commodity data with the highest similarity as the target commodity data.
In one possible embodiment, the step S6: determining target commodity data from a plurality of first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag, wherein the step comprises the following steps:
s633: and if a plurality of second candidate commodity data with the highest similarity exist, randomly selecting one to be determined as the target commodity data from the plurality of second candidate commodity data with the highest similarity.
In this embodiment, by matching the two times of data, the target commodity data that best matches the price tag picture can be used as the price tag content.
For example, as shown in fig. 3, since the contents of the commodity name region are text types, the contents cannot be classified and identified by the object detection algorithm, and therefore only specific position coordinates output by the algorithm are used. The commodity price is a simple combination of numbers and symbols (such as: ") and each number and symbol can be easily detected and classified by the target detection algorithm, so that the classification result output by the algorithm can be directly used as commodity price information. And for the commodity name area, performing content identification by using an OCR algorithm to obtain a preliminary commodity name; because the price tag is smaller in size and the size of commodity information displayed in the price tag is smaller and is limited by the imaging quality of a camera, commodity information displayed on the price tag cannot be displayed clearly sometimes, as shown in fig. 3, chinese characters in front of a soft character and Chinese characters behind a sending character cannot be displayed clearly because the Chinese characters are complex or the camera is not clear in photographing, and in this case, the Chinese characters in front of the soft character and the Chinese characters behind the sending character cannot be identified by using a single OCR algorithm, namely commodity name information cannot be accurately identified. At this time, three first candidate commodity data such as "the long-acting anti-dandruff shampoo with softness", "the long-acting anti-dandruff shampoo with softness" and "500 ml" may be obtained by matching the commodity name with the pre-constructed commodity information database, and at this time, the nearest one of the three first candidate commodity data may be selected as the price tag content of the target price tag through steps S5-S6 and S61-S633, thereby greatly improving the accuracy of identification.
Referring to fig. 4, a second embodiment of the present application provides a price tag content acquiring apparatus, including:
the image acquisition module 1 is used for acquiring price tag images of target price tags;
the detection module 2 is used for detecting the name area and commodity price of the price tag picture through a target detection algorithm;
the recognition module 3 is used for carrying out content recognition on the name area through an OCR algorithm to obtain a commodity name;
the first matching module 4 is used for matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data;
the second matching module 5 is used for matching the price data of the first candidate commodity data of the matching result with the commodity price to obtain matching data;
and the price tag content acquisition module 6 is used for determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as price tag content of the target price tag.
It should be noted that, when executing the method for acquiring the content of the price tag, the price tag content acquiring device provided by the second embodiment of the present application is only exemplified by the division of the above functional modules, in practical application, the above functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the price tag content acquiring apparatus provided by the second embodiment of the present application belongs to the same concept as the price tag content acquiring method of the first embodiment of the present application, which represents an implementation process detailed method embodiment, and the above-described device embodiment is not repeated here, and is merely illustrative, where the components described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present application. Those of ordinary skill in the art will understand and implement the present application without undue burden.
An embodiment of the present application also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the price tag content acquisition method described above.
An embodiment of the present application also provides a computer apparatus including a memory, a processor, and a computer program stored in the memory and executable by the processor, the processor implementing the steps of the price tag content acquisition method as described above when executing the computer program.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flashRAM). Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transshipment) such as modulated data signals and carrier waves.
It should also be noted that 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. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A method of acquiring content of a price tag, comprising:
acquiring a price tag picture of a target price tag;
detecting a name area and commodity price of the price tag picture through a target detection algorithm;
performing content recognition on the name area through an OCR algorithm to obtain a commodity name;
matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data;
matching price data of a plurality of first candidate commodity data of the matching result with the commodity price to obtain matching data;
and determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag.
2. The method according to claim 1, wherein the step of detecting the name area of the price tag picture and the commodity price by a target detection algorithm comprises:
detecting a name area and a price area of the price tag picture through the target detection algorithm;
and identifying the price number and the price symbol of the price area through the target detection algorithm to obtain the commodity price.
3. The method for acquiring price tag content according to claim 1, wherein the matching is performed between the commodity name and a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data, and the matching result comprises the following steps:
obtaining the similarity between the commodity name and each commodity information data stored in the commodity information database;
and determining the first plurality of commodity information data with the highest similarity as the first candidate commodity data.
4. A price tag content retrieval method according to claim 3, wherein the matching data comprises a matching value for each of the first candidate commodity data and the commodity price;
the step of determining target commodity data from the first candidate commodity data of the matching result according to the matching data and determining the target commodity data as the price tag content of the target price tag comprises the following steps:
and if the matching values of the matching data are smaller than a preset matching threshold, determining the first candidate commodity data with highest similarity as the target commodity data from the matching result.
5. A price tag content acquiring method according to claim 3, wherein the step of determining target commodity data from a plurality of first candidate commodity data of the matching result based on the matching data, and determining the target commodity data as the price tag content of the target price tag comprises:
and if one matching value in the matching data is greater than or equal to a preset matching threshold value, determining the corresponding first candidate commodity data as the target commodity data.
6. A price tag content acquiring method according to claim 3, wherein the step of determining target commodity data from a plurality of first candidate commodity data of the matching result based on the matching data, and determining the target commodity data as the price tag content of the target price tag comprises:
if at least two matching values in the matching data are larger than or equal to a preset matching threshold, determining the corresponding first candidate commodity data as second candidate commodity data;
and obtaining the similarity between each piece of second candidate commodity data and the commodity information according to the matching result, and determining the second candidate commodity data with the highest similarity as the target commodity data.
7. The method of claim 6, wherein the step of determining target commodity data from a plurality of first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as the price tag content of the target price tag, comprises:
and if a plurality of second candidate commodity data with the highest similarity exist, randomly selecting one to be determined as the target commodity data from the plurality of second candidate commodity data with the highest similarity.
8. A price tag content acquiring apparatus, comprising:
the image acquisition module is used for acquiring a price tag image of the target price tag;
the detection module is used for detecting the name area and commodity price of the price tag picture through a target detection algorithm;
the identification module is used for carrying out content identification on the name area through an OCR algorithm to obtain a commodity name;
the first matching module is used for matching the commodity name with a pre-constructed commodity information database to obtain a matching result; the matching result comprises a plurality of first candidate commodity data;
the second matching module is used for matching the price data of the plurality of first candidate commodity data of the matching result with the commodity price to obtain matching data;
and the price tag content acquisition module is used for determining target commodity data from the first candidate commodity data of the matching result according to the matching data, and determining the target commodity data as price tag content of the target price tag.
9. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implements the steps of the price tag content retrieval method of any of claims 1 to 7.
10. A computer device, characterized by: comprising a memory, a processor and a computer program stored in the memory and executable by the processor, the processor implementing the steps of the price tag content acquisition method according to any of claims 1 to 7 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310563499.6A CN116580390A (en) | 2023-05-18 | 2023-05-18 | Price tag content acquisition method, price tag content acquisition device, storage medium and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310563499.6A CN116580390A (en) | 2023-05-18 | 2023-05-18 | Price tag content acquisition method, price tag content acquisition device, storage medium and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116580390A true CN116580390A (en) | 2023-08-11 |
Family
ID=87539277
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310563499.6A Pending CN116580390A (en) | 2023-05-18 | 2023-05-18 | Price tag content acquisition method, price tag content acquisition device, storage medium and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116580390A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117275011A (en) * | 2023-10-11 | 2023-12-22 | 广州市玄武无线科技股份有限公司 | Commodity identification and commodity price tag matching method, system, terminal and medium |
-
2023
- 2023-05-18 CN CN202310563499.6A patent/CN116580390A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117275011A (en) * | 2023-10-11 | 2023-12-22 | 广州市玄武无线科技股份有限公司 | Commodity identification and commodity price tag matching method, system, terminal and medium |
| CN117275011B (en) * | 2023-10-11 | 2024-06-14 | 广州市玄武无线科技股份有限公司 | Commodity identification and commodity price tag matching method, system, terminal and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110827247B (en) | Label identification method and device | |
| US11080839B2 (en) | System and method for training a damage identification model | |
| CN111078908A (en) | Data annotation detection method and device | |
| CN110175590A (en) | A kind of commodity recognition method and device | |
| CN109977824B (en) | Article taking and placing identification method, device and equipment | |
| US9811537B2 (en) | Product identification via image analysis | |
| CN110348392B (en) | Vehicle matching method and device | |
| CN112580707A (en) | Image recognition method, device, equipment and storage medium | |
| US11715292B2 (en) | Methods and apparatus to perform image analyses in a computing environment | |
| CN111428743B (en) | Commodity identification method, commodity processing device and electronic equipment | |
| CN116580390A (en) | Price tag content acquisition method, price tag content acquisition device, storage medium and computer equipment | |
| CN106997350B (en) | Data processing method and device | |
| CN115115825B (en) | Method, device, computer equipment and storage medium for detecting object in image | |
| CN115063084A (en) | Inventory checking method and system for cigarette retail merchants | |
| CN112884054B (en) | Target labeling method and target labeling device | |
| WO2017069741A1 (en) | Digitized document classification | |
| US20210166028A1 (en) | Automated product recognition, analysis and management | |
| CN112364702A (en) | Article verification method and device | |
| US20230196558A1 (en) | Medicine image recognition method, electronic device and readable storage medium | |
| CN113627415A (en) | Method and device for determining target object placement information | |
| CN114417965A (en) | Training method of image processing model, target detection method and related device | |
| CN111222377B (en) | Commodity information determining method and device and electronic equipment | |
| CN109034067B (en) | Method, system, equipment and storage medium for commodity image reproduction detection | |
| CN115953635B (en) | Sorting method, AR (augmented reality) glasses and system for multi-class targets | |
| CN117115839B (en) | Invoice field identification method and device based on self-circulation neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |