CN110245594A - A kind of commodity recognition method for cash register system - Google Patents
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Abstract
This application involves a kind of commodity recognition methods for cash register system, this method comprises: being identified using trained image recognition neural network to commodity image to be detected, obtain image recognition result, commodity image to be detected is identified using trained text identification neural network, obtain text identification result, in conjunction with image recognition result and text identification as a result, obtaining final goods recognition result.The accuracy rate of identification can be improved by this method.The application further relates to a kind of device of commodity for identification.
Description
Technical field
This application involves image identification technical fields, such as are related to a kind of commodity identification side for cash register system
Method.
Background technique
Currently, each retail convenience shop and chain-supermarket are already provided with many self-service cash registers.Self-service cash register is disappearing
When the person of expense settles accounts, whole process is not necessarily to cashier's cash register, and such as the self-service cash register that everybody happy Xi'an shops releases, customer is being bought goods
Barcode scanning completes payment afterwards, and whole process does not all need the participation of cashier；Wal-Mart's major part sales field shops and supermarket, community " favour
Choosing " is additionally arranged self-service cash register, quick cash register " barcode scanning purchase " etc., wherein correctly identification commodity are crucial.In recent years, with
The fast development of deep learning carries out commodity identification using based on the image-recognizing method of deep learning, becomes a big research
Hot spot.
During realizing the embodiment of the present disclosure, at least there are the following problems in the related technology for discovery: knowing according to image
The accuracy rate when identifying commodity of other technology is low.
Summary of the invention
In order to which some aspects of the embodiment to disclosure have basic understanding, simple summary is shown below.It is described general
Including is not extensive overview, nor to determine key/critical component or describe the protection scope of these embodiments, but is made
For the preamble of following detailed description.
The embodiment of the present disclosure provides a kind of commodity recognition method for cash register system, to solve according to image recognition skill
The low technical problem of the accuracy rate when identifying commodity of art.
A kind of commodity recognition method for cash register system characterized by comprising
Commodity image to be detected is identified using trained image recognition neural network, obtains image recognition knot
Fruit；
The detection commodity image is treated using trained text identification neural network to be identified, text identification is obtained
As a result；
In conjunction with described image recognition result and the text identification as a result, obtaining final goods recognition result.
It is preferably, described to treat the detection commodity image using trained text identification neural network and identified,
Obtain text identification result, comprising:
Extract the low-level feature figure and high-level semantics features figure of the detection commodity image；
The low-level feature figure and the high-level semantics features figure are merged, the fusion feature of whole image is obtained
Figure；
The fusion feature figure is converted to the horizontal properties figure of fixed height；
The water product characteristic pattern is encoded by CNN and RNN, decodes to obtain the text knowledge by CTC decoder
Other result.
Preferably, after the fusion feature figure for obtaining whole image, further includes:
The fusion feature figure is exported to the text position of single pass Text Score characteristic pattern and multichannel by convolutional layer
Set characteristic pattern；
The all of the detection commodity image are obtained according to the Text Score characteristic pattern and the text position characteristic pattern
It is text filed.
It preferably, include position mark and content mark in the training set of the training text identification neural network.
Preferably, the position is labeled as using the coordinate on any one vertex in four text filed vertex as starting point
It is labeled.
Preferably, the position is labeled as tactic according to setting.
Preferably, the setting sequence includes clock-wise order and sequence counter-clockwise.
Preferably, the combination described image recognition result and the text identification are as a result, obtain final goods identification knot
Fruit, comprising:
Under the first commodity recognition result and the second commodity recognition result unanimous circumstances, with first quotient
Product recognition result or the second commodity recognition result are as the final goods recognition result.
Preferably, described image identification neural network is the neural network based on Resnet-34.
The method for the commodity for identification that the embodiment of the present disclosure provides, may be implemented following technical effect:
The graphical information of commodity is not only utilized, also while the text information on commodity is utilized, two kinds of information combine,
Available higher commodity recognition result.
Above general description and it is discussed below be only it is exemplary and explanatory, be not used in limitation the application.
Detailed description of the invention
One or more embodiments are illustrated by corresponding attached drawing, these exemplary illustrations and
Attached drawing does not constitute the restriction to embodiment, and the element with same reference numbers label is shown as similar element in attached drawing, attached
Composition does not limit figure, and wherein:
Fig. 1 is a kind of commodity recognition method flow diagram for cash register system that the embodiment of the present disclosure provides；
Fig. 2 is a kind of commodity recognition method flow diagram for cash register system that the embodiment of the present disclosure provides；
Fig. 3 is a kind of commodity recognition method flow diagram for cash register system that the embodiment of the present disclosure provides；
Fig. 4 is the block diagram of the device for the commodity for identification that the embodiment of the present disclosure provides；
Fig. 5 is the block diagram of the electronic equipment for the commodity for identification that the embodiment of the present disclosure provides.
Specific embodiment
The characteristics of in order to more fully hereinafter understand the embodiment of the present disclosure and technology contents, with reference to the accompanying drawing to this public affairs
The realization for opening embodiment is described in detail, appended attached drawing purposes of discussion only for reference, is not used to limit the embodiment of the present disclosure.
In technical description below, for convenience of explanation for the sake of, disclosed embodiment is fully understood with providing by multiple details.
However, one or more embodiments still can be implemented in the case where without these details.In other cases, it is
Simplify attached drawing, well known construction and device can simplify displaying.
The embodiment of the present disclosure provides a kind of commodity recognition method for cash register system.
As shown in Figure 1, in some embodiments, the method for commodity includes: for identification
S101, commodity image to be detected is identified using trained image recognition neural network, obtains image and knows
Other result.
When training image identifies neural network, need commodity image being divided into training set and test set.Optionally, training
Integrate and the ratio of test set is 9:1.
Optionally, commodity image to be detected is one and one or more that multi-angled shooting acquisition is carried out to commodity outer packing
Image.
S102, commodity image to be detected is identified using trained text identification neural network, obtains text and knows
Other result.
It optionally, include position mark and content mark in the training set of training text identification neural network.Optionally, position
It sets and is labeled as being labeled using the coordinate on any one vertex in four text filed vertex as starting point.Optionally, position
Set be labeled as according to set it is tactic.Optionally, setting sequence includes clock-wise order and sequence counter-clockwise.
Optionally, text identification neural network is FOTS (Fast Oriented Text Spotting with a
Unified Network, Unified frame slewing text detection network) neural network.
S103, in conjunction with image recognition result and text identification as a result, obtain final goods recognition result.
The graphical information of commodity is not only utilized, also while the text information on commodity is utilized, two kinds of information combine,
Available higher commodity recognition result.This method can be applied to cash register system, to improve the accuracy rate of identification commodity.
In addition, using image recognition neural network end to end and text identification neural network end to end, both minds
It is all very fast through processing of the network for commodity image to be detected, enhance the real-time of this method.
As shown in Fig. 2, in some embodiments, S102 is using trained text identification neural network to commodity to be detected
Image is identified, text identification result is obtained, comprising:
S201, the low-level feature figure and high-level semantics features figure for extracting detection commodity image；
S202, low-level feature figure and high-level semantics features figure are merged, obtains the fusion feature figure of whole image.
Optionally, fusion feature figure size is the 1/4 of low-level feature figure or high-level semantics features figure size.
S203, the horizontal properties figure that fusion feature figure is converted to fixed height；
S204, pass through CNN (Convolutional Neural Networks, convolutional neural networks) and RNN
(Recurrent Neural Network, Recognition with Recurrent Neural Network) encodes water product characteristic pattern, passes through CTC
(Connectionist Temporal Classification, connectionism chronological classification) decoder decodes to obtain text knowledge
Other result.
As shown in figure 3, in some embodiments, after the fusion feature figure that S202 obtains whole image, further includes:
S301, the text position that fusion feature figure is exported to single pass Text Score characteristic pattern and multichannel by convolutional layer
Set characteristic pattern；
S302, all text areas of detection commodity image are obtained according to Text Score characteristic pattern and text position characteristic pattern
Domain.
In some embodiments, S103 combination image recognition result and text identification are as a result, obtain final goods identification knot
Fruit, comprising:
Under the first commodity recognition result and the second commodity recognition result unanimous circumstances, with the first commodity recognition result or
Second commodity recognition result is as final goods recognition result.
In some embodiments, image recognition neural network is the neural network based on Resnet-34.
The specific identification process of the neural network is to extract commodity picture abstract characteristics first with depth residual error network, with
Full articulamentum classifies to commodity using the feature extracted afterwards, is finally every kind of article using the output of softmax function
Probability value.
The network configuration of Resnet-34 is as shown in table 1.
1 Resnet-34 network configuration table of table
The embodiment of the present disclosure provides a kind of device of commodity for identification,
As shown in figure 4, in some embodiments, the device of commodity includes: for identification
Picture recognition module 41, be configured as using trained image recognition neural network to commodity image to be detected into
Row identification, obtains image recognition result；
Text identification module 42, be configured as using trained text identification neural network to commodity image to be detected into
Row identification, obtains text identification result；
Binding modules 43 are configured as combining image recognition result and text identification as a result, obtaining final goods identification knot
Fruit.
In some embodiments, text identification module includes:
Extraction unit is configured as extracting the low-level feature figure and high-level semantics features figure of detection commodity image；
Integrated unit is configured as merging in low-level feature figure and high-level semantics features figure, obtains whole image
Fusion feature figure；
Rotary unit is configured as being converted to fusion feature figure into the horizontal properties figure of fixed height；
Recognition unit is configured as encoding water product characteristic pattern by CNN and RNN, be decoded by CTC decoder
To text identification result.
In some embodiments, text identification module further include:
Analytical unit is configured as fusion feature figure exporting single pass Text Score characteristic pattern and more by convolutional layer
The text position characteristic pattern in channel；
Text detection unit is configured as obtaining detection commodity figure according to Text Score characteristic pattern and text position characteristic pattern
Picture it is all text filed.
It in some embodiments, include position mark and content mark in the training set of training text identification neural network.
In some embodiments, position be labeled as be with the coordinate on any one vertex in four text filed vertex
What starting point was labeled.
In some embodiments, position is labeled as tactic according to setting.
In some embodiments, setting sequence includes clock-wise order and sequence counter-clockwise.
In some embodiments, binding modules are configured as:
Under the first commodity recognition result and the second commodity recognition result unanimous circumstances, with the first commodity recognition result or
Second commodity recognition result is as final goods recognition result.
The embodiment of the present disclosure provides a kind of computer readable storage medium, is stored with computer executable instructions, described
The method that computer executable instructions are arranged to carry out above-mentioned commodity for identification.
The embodiment of the present disclosure provides a kind of computer program product, and the computer program product includes being stored in calculating
Computer program on machine readable storage medium storing program for executing, the computer program include program instruction, when described program instruction is calculated
When machine executes, the method that makes the computer execute above-mentioned commodity for identification.
Above-mentioned computer readable storage medium can be transitory computer readable storage medium, be also possible to non-transient meter
Calculation machine readable storage medium storing program for executing.
The embodiment of the present disclosure provides a kind of electronic equipment, and structure is as shown in figure 5, the electronic equipment includes:
In at least one processor (processor) 50, Fig. 5 by taking a processor 50 as an example；With memory (memory)
51, it can also include communication interface (Communication Interface) 52 and bus 53.Wherein, processor 50, communication
Interface 52, memory 51 can complete mutual communication by bus 53.Communication interface 52 can be used for information transmission.Processing
Device 50 can call the logical order in memory 51, the method to execute the commodity for identification of above-described embodiment.
In addition, the logical order in above-mentioned memory 51 can be realized and as only by way of SFU software functional unit
Vertical product when selling or using, can store in a computer readable storage medium.
Memory 51 is used as a kind of computer readable storage medium, can be used for storing software program, journey can be performed in computer
Sequence, such as the corresponding program instruction/module of the method in the embodiment of the present disclosure.Processor 50 is stored in memory 51 by operation
Software program, instruction and module, thereby executing functional application and data processing, i.e., in realization above method embodiment
Method.
Memory 51 may include storing program area and storage data area, wherein storing program area can storage program area, extremely
Application program needed for a few function；Storage data area, which can be stored, uses created data etc. according to terminal device.This
Outside, memory 51 may include high-speed random access memory, can also include nonvolatile memory.
The technical solution of the embodiment of the present disclosure can be embodied in the form of software products, which deposits
It stores up in one storage medium, including one or more instructions are used so that a computer equipment (can be personal meter
Calculation machine, server or network equipment etc.) execute embodiment of the present disclosure the method all or part of the steps.And it is above-mentioned
Storage medium can be non-transient storage media, comprising: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are a variety of can store journey
The medium of sequence code, is also possible to transitory memory medium.
Above description and attached drawing sufficiently illustrate embodiment of the disclosure, to enable those skilled in the art to practice
They.Other embodiments may include structure, logic, it is electrical, process and other change.Embodiment only represents
Possible variation.Unless explicitly requested, otherwise individual components and functionality is optional, and the sequence operated can change.
The part of some embodiments and feature can be included in or replace part and the feature of other embodiments.The embodiment of the present disclosure
Range includes the entire scope of claims and all obtainable equivalents of claims.When for the application
When middle, although term " first ", " second " etc. may be used in this application to describe each element, these elements should not be by
To the limitation of these terms.These terms are only used to differentiate an element with another element.For example, not changing description
Meaning in the case where, first element can be called second element, and similarly, and second element can be called first element,
As long as " first element " that is occurred unanimously renames and " second element " occurred unanimously renames.First
Element and second element are all elements, but can not be identical element.Moreover, word used herein is only used for describing
Embodiment and it is not used in limitation claim.As used in the description in embodiment and claim, unless context
It clearly illustrates, otherwise "one" (a) of singular, "one" (an) and " described " (the) is intended to equally include plural shape
Formula.Similarly, term "and/or" refers to and associated lists comprising one or more as used in this specification
Any and all possible combination.In addition, when in the application, term " includes " (comprise) and its modification " packet
Include " (comprises) and/or feature, entirety, step, operation, element including the statement such as (comprising) fingers, and/or
The presence of component, but be not excluded for one or more other features, entirety, step, operation, element, component and/or these
The presence or addition of grouping.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except there is also other identical elements in the process, method or equipment for including the element.Herein, each embodiment weight
Point explanation can be the difference from other embodiments, and the same or similar parts in each embodiment can refer to each other.
For method, product disclosed in the embodiment etc., if it is corresponding with method part disclosed in embodiment, correlation
Place may refer to the description of method part.
It will be appreciated by those of skill in the art that unit described in conjunction with the examples disclosed in the embodiments of the present disclosure and
Algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually with hard
Part or software mode execute, and can depend on the specific application and design constraint of technical solution.The technical staff
Described function can be realized using distinct methods to each specific application, but this realization is it is not considered that exceed
The range of the embodiment of the present disclosure.The technical staff can be understood that, for convenience and simplicity of description, foregoing description
The specific work process of system, device and unit, can refer to corresponding processes in the foregoing method embodiment, no longer superfluous herein
It states.
In embodiments disclosed herein, disclosed method, product (including but not limited to device, equipment etc.) can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
Divide, can be only a kind of logical function partition, there may be another division manner in actual implementation, for example, multiple units or
Component can be combined or can be integrated into another system, or some features can be ignored or not executed.In addition, shown
Or the mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, device or unit it is indirect
Coupling or communication connection can be electrical property, mechanical or other forms.The unit as illustrated by the separation member can be or
Person, which may not be, to be physically separated, and component shown as a unit may or may not be physical unit
With in one place, or may be distributed over multiple network units.Portion therein can be selected according to the actual needs
Point or whole unit realize the present embodiment.In addition, each functional unit in the embodiments of the present disclosure can integrate at one
In processing unit, it is also possible to each unit and physically exists alone, a list can also be integrated in two or more units
In member.
The flow chart and block diagram in the drawings show system, the method and computer program products according to the embodiment of the present disclosure
Architecture, function and operation in the cards.In this regard, each box in flowchart or block diagram can represent one
A part of a part of module, section or code, the module, section or code is used for comprising one or more
The executable instruction of logic function as defined in realizing.In some implementations as replacements, function marked in the box can also
To occur in a different order than that indicated in the drawings.For example, two continuous boxes can actually be basically executed in parallel,
They can also be executed in the opposite order sometimes, this can be depended on the functions involved.It is every in block diagram and or flow chart
The combination of box in a box and block diagram and or flow chart can use the dedicated base for executing defined function or movement
It realizes, or can realize using a combination of dedicated hardware and computer instructions in the system of hardware.
Claims (9)
1. a kind of commodity recognition method for cash register system characterized by comprising
Commodity image to be detected is identified using trained image recognition neural network, obtains image recognition result；
The detection commodity image is treated using trained text identification neural network to be identified, text identification knot is obtained
Fruit；
In conjunction with described image recognition result and the text identification as a result, obtaining final goods recognition result.
2. a kind of commodity recognition method for cash register system according to claim 1, which is characterized in that described to utilize instruction
The text identification neural network perfected is treated the detection commodity image and is identified, text identification result is obtained, comprising:
Extract the low-level feature figure and high-level semantics features figure of the detection commodity image；
The low-level feature figure and the high-level semantics features figure are merged, the fusion feature figure of whole image is obtained；
The fusion feature figure is converted to the horizontal properties figure of fixed height；
The water product characteristic pattern is encoded by CNN and RNN, decodes to obtain the text identification knot by CTC decoder
Fruit.
3. a kind of commodity recognition method for cash register system according to claim 2, which is characterized in that it is described obtain it is whole
After the fusion feature figure for opening image, further includes:
The text position that the fusion feature figure is exported single pass Text Score characteristic pattern and multichannel by convolutional layer is special
Sign figure；
All texts of the detection commodity image are obtained according to the Text Score characteristic pattern and the text position characteristic pattern
Region.
4. a kind of commodity recognition method for cash register system according to any one of claim 1 to 3, feature exist
In,
It include position mark and content mark in the training set of the training text identification neural network.
5. a kind of commodity recognition method for cash register system according to claim 4, which is characterized in that
What the position was labeled as being labeled using the coordinate on any one vertex in four text filed vertex as starting point.
6. a kind of commodity recognition method for cash register system according to claim 4, which is characterized in that
The position is labeled as tactic according to setting.
7. a kind of commodity recognition method for cash register system according to claim 6, which is characterized in that
The setting sequence includes clock-wise order and sequence counter-clockwise.
8. a kind of commodity recognition method for cash register system according to claim 1, which is characterized in that the combination institute
Image recognition result and the text identification are stated as a result, obtaining final goods recognition result, comprising:
Under the first commodity recognition result and the second commodity recognition result unanimous circumstances, with first commodity knowledge
Other result or the second commodity recognition result are as the final goods recognition result.
9. a kind of commodity recognition method for cash register system according to claim 1, which is characterized in that
Described image identifies that neural network is the neural network based on Resnet-34.
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| CN112686220A (en) * | 2021-03-10 | 2021-04-20 | 浙江口碑网络技术有限公司 | Commodity identification method and device, computing equipment and computer storage medium |
| CN117952507A (en) * | 2024-03-26 | 2024-04-30 | 南京亿猫信息技术有限公司 | Intelligent shopping cart commodity returning identification method and system |
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