CA3052846A1 - Character recognition method, device, electronic device and storage medium - Google Patents
Character recognition method, device, electronic device and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 48
- 239000013598 vector Substances 0.000 claims description 43
- 238000000605 extraction Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 239000000284 extract Substances 0.000 claims description 4
- 238000013500 data storage Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 21
- 238000012549 training Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 8
- 238000013527 convolutional neural network Methods 0.000 description 7
- 210000002569 neuron Anatomy 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
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- 239000004973 liquid crystal related substance Substances 0.000 description 1
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- 238000012015 optical character recognition Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- 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
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- 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
Abstract
The embodiment of the invention provides a character recognition method, a device, an electronic device and a storage medium, which relate to the technical field of image processing. The method comprises: acquiring a character image corresponding to the unique identifier and a standard result of the character image based on the unique identifier of the client;
extracting features of the character image and generating a sample set based on the features of the character image and the standard result; the sample set being pushed to the client based on the unique identification so that the client recognizes an image of a character to be recognized through the sample set. The technical proposal of the embodiment of the invention can avoid the problem of low identification rate caused by different payment environments of different clients.
extracting features of the character image and generating a sample set based on the features of the character image and the standard result; the sample set being pushed to the client based on the unique identification so that the client recognizes an image of a character to be recognized through the sample set. The technical proposal of the embodiment of the invention can avoid the problem of low identification rate caused by different payment environments of different clients.
Description
Character recognition method, device, electronic device and storage medium Technical Field [0001] The present invention relates to the field of image processing technologies, and in particular, to a character recognition method, a character recognition apparatus, an electronic equipment, and a computer readable storage medium.
Background Technology
Background Technology
[0002] With the development of Internet technology, payment methods are also evolving, and traditional payment methods need to be modified to meet people's convenience for payment.
[0003] Currently, in a technical solution, in order to be compatible with the original payment client, the Windows application program interface is called on the original payment client to intercept a part of the payment interface, such as a payment amount area, and OCR (Optical Character) is adopted. Recognition, optical character recognition) technology identifies the content of the payment amount area. In this technical solution, because the original cash register software, operating system version, display resolution and the like of different payment clients are different, it is difficult to achieve the better recognition rate by using the same sample library for identification.
[0004] It is to be understood that the information disclosed in the Background section above is only used to enhance the understanding of the background of the invention, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Summary of the Invention
Summary of the Invention
[0005] An object of embodiments of the present invention is to provide a character recognition method, a character recognition apparatus, an electronic equipment, and a computer readable storage medium, thereby at least partially offset one or more due to limitations and defects of the related art. Questions.
[0006] According to a first aspect of the present invention, a character recognition method includes: acquiring a character image corresponding to the unique identifier and a standard result of the character image based on a unique identifier of a client; and extracting the a feature of the character image, generating a sample set based on the characteristics of the character image and the standard result; pushing the sample set to the client based on the unique identifier to cause the client to be identified by the sample set Character images are recognized.
[0007] In some embodiments of the present invention, the obtaining, according to the foregoing solution, the character image corresponding to the unique identifier based on the unique identifier of the client, includes: acquiring a character recognition rate of the client based on the unique identifier of the client; determining whether the character recognition rate is less than a predetermined threshold; if the determination is less than the predetermined threshold, acquiring a corresponding character image based on the unique identifier of the client.
[0008] In some embodiments of the present invention, the character recognition method further includes: acquiring a recognition result of a character image corresponding to the unique identifier based on the unique identifier of the client, based on the foregoing solution; And the standard result determines the character recognition rate of the client.
[0009] In some embodiments of the present invention, extracting features of the character image based on the foregoing scheme, generating a sample set based on features of the character image and the standard result, including: extracting the character by a feature extraction model a feature of each character in the image; determining a target character corresponding to the respective character in the standard result; using the target character as a feature of the character of each character; based on characteristics and features of each character in the character image The tag generates the sample set.
[0010] In some embodiments of the present invention, the character recognition method further includes: receiving the character image sent by the client, the recognition result of the character image, and a standard result; The storage area stores the character image; the recognition result of the character image and the standard result are stored in the second storage area.
[0011] In some embodiments of the present invention, based on the foregoing aspect, the first storage area is an image storage unit of a target server, and the second storage area is a relational data storage unit of the target server.
[0012] According to a second aspect of the embodiments of the present invention, there is provided another character recognition method, comprising: receiving a sample set pushed by a target server by a unique identifier of a client, the sample set being a character sent based on the client And a feature set generated by the image and the standard result of the character image;
acquiring a character image to be recognized, and extracting a feature vector of each character in the character image to be recognized; performing feature vectors of each character and feature vectors in the sample set Matching; identifying characters in the character image to be recognized based on the matching result.
acquiring a character image to be recognized, and extracting a feature vector of each character in the character image to be recognized; performing feature vectors of each character and feature vectors in the sample set Matching; identifying characters in the character image to be recognized based on the matching result.
[0013] In some embodiments of the present invention, the character recognition method further includes: receiving a standard result of the character image to be recognized input by a user; and the character image to be recognized, the to-be-identified The recognition result of the character image and the standard result are transmitted to the target server.
[0014] According to a third aspect of the embodiments of the present invention, there is provided a character recognition apparatus, comprising: an obtaining unit, configured to acquire a character image corresponding to the unique identifier and a standard of the character image based on a unique identifier of a client a result; a sample generating unit, configured to extract a feature of the character image, generate a sample set based on the feature of the character image and the standard result; and a sample pushing unit, configured to push the client to the client based on the unique identifier The sample set is described to enable the client to identify the character image to be recognized by the sample set.
[0015] According to a fourth aspect of the present invention, a character recognition apparatus is provided, including: a sample receiving unit, configured to receive a sample set pushed by a target server by a unique identifier of a client, where the sample set is based on the a character set generated by the client and a feature set generated by the standard result of the character image; a feature extracting unit configured to acquire a character image to be recognized, and extract a feature vector of each character in the character image to be recognized; And matching the feature vector of each character with the feature vector in the sample set; the identifying unit is configured to identify the character in the character image to be recognized based on the matching result.
[0016] According to a fifth aspect of the embodiments of the present invention, there is provided an electronic equipment comprising: a processor; and a memory having stored thereon computer readable instructions, the computer readable instructions being The character recognition method as described in the first aspect above is implemented at the time of execution.
[0017] According to a sixth aspect of the embodiments of the present invention, there is provided a computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement character recognition as described in the first aspect above method.
[0018] In a technical solution provided by some embodiments of the present invention, on the one hand, generating a sample set of the client based on characteristics of a character image of a client and a standard result, since a corresponding sample set is generated for each client, thereby It can avoid the problem that the recognition rate is low due to different payment environments of different clients; on the other hand, the deployment cost can be reduced because there is no need to upgrade the original payment system.
[0019] The above general description and the following detailed description are intended to be illustrative and not restrictive.
Brief Description
Brief Description
[0020] Figures herein are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention, and together with the description serve to explain the principles of the invention. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can obtain other drawings according to the drawings without any creative work. In the drawing:
[0021] Figure 1 shows a flow diagram of a character recognition method in accordance with some embodiments of the present invention;
[0022] Figure 2 shows a schematic diagram of an application of a character recognition method in accordance with some embodiments of the present invention;
[0023] Figure 3 shows a schematic diagram of setting a screenshot area in accordance with some embodiments of the present invention;
[0024] Figure 4 shows a flow diagram of automatically uploading a captured image, in accordance with some embodiments of the present invention;
[0025] Figure 5 illustrates a flow diagram of an automated training sample in accordance with some embodiments of the present invention;
[0026] Figure 6 shows a schematic diagram of a feature extraction model in accordance with some embodiments of the present invention;
[0027] Figure 7 shows a flow chart of a character recognition method according to further embodiments of the present invention;
[0028] Figure 8 shows a schematic block diagram of a character recognition apparatus in accordance with some embodiments of the present invention;
[0029] Figure 9 shows a schematic block diagram of a character recognition apparatus according to further embodiments of the present invention;
[0030] Figure 10 shows a block diagram of a computer system suitable for use in implementing an electronic equipment in accordance with an embodiment of the present invention.
Description of the Preferred Examples
Description of the Preferred Examples
[0031] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in a variety of forms and should not be construed as being limited to the embodiments set forth herein. To those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and the repeated description thereof will be omitted.
[0032] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are set forth However, one skilled in the art will appreciate that the technical solution of the present invention may be practiced without one or more of the specific details, or other methods, components, apparatus, steps, etc. may be employed. In other instances, well-known methods, apparatus, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.
[0033] The block diagrams shown in the figures are merely functional entities and do not necessarily have to correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
[0034] The flowcharts shown in the figures are merely illustrative, and not all of the contents and operations/steps are necessarily included, and are not necessarily performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially merged, so the actual execution order may vary depending on the actual situation.
[0035] Figure 1 shows a flow diagram of a character recognition method in accordance with some embodiments of the present invention. In the exemplary embodiment of the present invention, although the character recognition method is applied to the server end of the cash register system as an example, it should be understood that the character recognition method can also be applied to the server side of the license plate recognition system. It can be applied to the server side of other suitable character recognition systems, and the present invention is not particularly limited.
[0036] Referring to Figure 1, in step S110, a character image corresponding to the unique identifier and a standard result of the character image are acquired based on a unique identifier of the client.
[0037] In an exemplary embodiment, the client may be a cash register system of a shopping mall or a supermarket, such as a cash register computer, and the unique identifier of the client is a serial number of each client set on the server side, by which the client can be uniquely identified. The standard result of the character image is the standard result input by the client. If the client accurately recognizes the character image, the recognition result is taken as the standard result of the character image; if the client recognizes the character image incorrectly, the character is input by the user. The standard result of the image.
[0038] The server side stores the standard result of the character image and the character image sent by the client in the storage area based on the unique identifier of the client, for example, storing the standard result of the character image and the character image sent by the client with the unique identifier of the client as the primary key.
[0039] If the character image is small, the standard result of the character image and the character image may be directly stored in the database; if the character image is large, the character image may be stored on the cloud server, and the character image is stored in the cloud in the database. The storage path of the server and the standard results of the character image. In an exemplary embodiment, obtaining a character image corresponding to the unique identifier and a standard result of the character image from the server end based on the unique identifier of the client, for example, when the character image is stored on the cloud server, may be from the database according to the unique identifier of the client. Obtaining a storage result of the character image and a standard result of the character image, and acquiring a corresponding character image from the cloud server based on the acquired storage path.
[0040] In step S120, a feature of the character image is extracted, and a sample set is generated based on the feature of the character image and the standard result.
[0041] The image with the character image as the payment amount is taken as an example. The character image may be normalized first, and the normalized character image is divided according to the pixel distribution to obtain a plurality of single characters, that is, 0-9. The numeric character extracts the segmented single-character pixel feature to obtain the characteristic vector of each single character.
[0042] Further, a character corresponding to the divided single character in the standard result of the character image is used as the label of the single character, and the sample set is formed based on the feature vector of each single character and the corresponding label. For example, if the extracted single-character feature vector is x, and the single-character label is y, and n is the number of characters in the character image, the sample set may be {(xi, yi), (x2, y2),...,(xn, yn)}.
The sample set is automatically generated by the characteristics of the character image and the standard result, which improves the efficiency of generating the sample set and improves the data processing efficiency.
The sample set is automatically generated by the characteristics of the character image and the standard result, which improves the efficiency of generating the sample set and improves the data processing efficiency.
[0043] In step S130, the sample set is pushed to the client based on the unique identifier, so that the client identifies the character image to be recognized through the sample set.
[0044] In some embodiments, the client's network address, such as an IP
address, is obtained based on the unique identifier of the client, and the sample set generated in step S120 is pushed to the client based on the acquired network address. By generating a corresponding sample set for each client, it is possible to avoid the problem that the recognition rate is low due to different payment environments of different clients.
address, is obtained based on the unique identifier of the client, and the sample set generated in step S120 is pushed to the client based on the acquired network address. By generating a corresponding sample set for each client, it is possible to avoid the problem that the recognition rate is low due to different payment environments of different clients.
[0045] In addition, the client matches and matches the feature of the extracted character image by the sample set. Since the sample set is generated based on the character image of the client and the standard result, the character recognition rate of the client can be improved.
[0046] Figure 2 shows a schematic diagram of an application of a character recognition method in accordance with some embodiments of the present invention.
[0047] Referring to Figure 2, in step S21, the client 210, 212 uploads its unique identifier, the captured character image, the recognition result of the character image, and the standard result to the background server 220.
[0048] In step S22, the server 220 stores the unique identifier of the client, the recognition result of the character image, and the standard result on the relation database 230, and stores the unique identifier of the client and the intercepted character image on the cloud server 240.
[0049] In step S23, according to the unique identification of the client, the recognition result and standard result of the character image sent by the client are obtained from the relational database 230. The character recognition rate of the client is calculated according to the recognition result and standard result of the character image.
[0050] In step S24, it is determined whether the character recognition rate of the client is lower than a predetermined recognition rate, and if it is lower than the predetermined recognition rate, the character sent by the client lower than the predetermined recognition rate is downloaded from the cloud server 240 according to the unique identifier of the client. The image is sent to the sample training server 250.
[0051] In step S25, the feature vector of each character is extracted from the character image corresponding to the unique identifier by a feature extraction model such as a neural network model, and each character in the standard result of the character image is used as a label of the extracted feature vector. A new sample set is generated based on the feature vectors of the characters and the tags.
[0052] In step S26, the new sample set generated by the sample training server 250 is pushed to the background server 220 based on the unique identifier of the client.
[0053] In step S27, the new sample set generated by the sample training server 250 is pushed to the corresponding client based on the unique identifier of the client.
[0054] Figure 3 shows a schematic diagram of setting a screenshot area in accordance with some embodiments of the present invention. Referring to Figure 3, after the payment interface of the cash register software is opened, the area of the receivable amount is automatically identified by the OCR method, and when the area of the receivable amount is recognized, the area is distinctively displayed on the payment interface, and the area is received.
After the user's confirmation message, the area is used as the screenshot area. Since the payment interface does not change after each startup of the cash register software, only the screenshot area needs to be set once.
After the user's confirmation message, the area is used as the screenshot area. Since the payment interface does not change after each startup of the cash register software, only the screenshot area needs to be set once.
[0055] Figure 4 illustrates a flow diagram of automatically uploading a captured image, in accordance with some embodiments of the present invention.
[0056] Referring to Figure 4, a screenshot area is configured at the client 210, and a character image of the screenshot area is intercepted when the user pays, and the character image is identified by a sample set pushed by the sample training server, for example, the character image is extracted. The feature vector of each character in the character matches the extracted feature vector with the feature vector in the sample set, and determines the content of each character according to the matching result. After the character image of the screenshot area is identified, the clipped character image, the recognition result of the character image, and the standard result are transmitted to the background server 220, which is input by the user when the error is recognized.
[0057] At the background server 220, the unique identifier of the client, the recognition result of the character image, and the standard result are stored on the relation database 230, and the unique identifier of the client and the intercepted character image are stored on the cloud server 240. After the storage is completed, the stored result is returned to the client 210.
[0058] Figure 5 shows a flow diagram of an automated training sample in accordance with some embodiments of the present invention.
[0059] Referring to Figure 5, before the sample training is performed, the character recognition rate of the client is obtained from the relational database 230 according to the unique identifier of the client, and the request for obtaining the character recognition rate is low according to the unique identifier of the client. In response to the image acquisition request, the cloud server 240 searches for a character image corresponding to the unique identifier of the client below the predetermined recognition rate based on the unique identifier of the client in response to the image acquisition request, and returns the found character image to the sample training server 250.
[0060] In the sample training server 250, the feature vector of each character is extracted from the character image corresponding to the unique identifier by a feature extraction model such as a neural network model, and each character in the standard result of the character image is taken as the extracted feature vector. The tag generates a new sample set based on the feature vectors of the characters and the tags. The generated new training set is then pushed to the client 210 via the background server 220, which uses the sample set for image recognition.
[0061] Figure 6 shows a schematic diagram of a feature extraction model in accordance with some embodiments of the present invention.
[0062] Referring to Figure 6, the feature extraction model is a Convolutional Neural Network (CNN) model. The CNN model may include an input layer, a convolutional layer Cl, a sampling layer S2, a convolutional layer C3, a sampling layer S4, and an output layer.
[0063] In Figure 6, the character image corresponding to the unique identifier is downloaded from the cloud server according to the unique identifier of the client, and the downloaded image is subjected to binarization and image cutting processing to obtain an image of a single character, and a single character is obtained. The image is input into the CNN model for sample training.
[0064] Referring to Figure 6, a single character image, that is, a feature map of 28 *28 is input at the input layer, and the feature map of 28*28 is convoluted by the convolution layer Cl to form six 24*24 images. Feature map; six 24*24 feature maps are sampled by sampling layer S2 to form six 12*12 feature maps; six 12*12 feature maps are convoluted by convolution layer C3 12 12*8 feature maps are generated; then 12 8*8 feature maps are processed through the sampling layer S4 to generate 12 4*4 feature maps; then, 12 1*1 are generated through the output layer. A feature map of 1 generates feature vectors of the input single character images based on the 12 1*1 feature maps.
[0065] The CNN model adopts the technical features of Local Connection and Weight Sharing, which can significantly increase the number of parameters of image processing and improve image processing efficiency. By using local connections, each neuron is only connected to a local area of the upper layer, which reduces the parameters that need to be processed. The spatial size of the local area of the connection is called the receptive field of the neuron. By using weight sharing, the current layer uses the same weight and bias for each channel's neurons in the depth direction, which can reduce the number of parameters. For example, in a local connection, each neuron corresponds to 100 parameters, a total of 1000000. For each neuron, if the 100 parameters of the 1,000,000 neurons are equal, then the number of parameters becomes 100. The use of local joins and weight sharing in the CNN model reduces the amount of parameters, greatly reduces training complexity and reduces the risk of overfitting.
[0066] It should be noted that, in the exemplary embodiment, although the feature extraction model is described by taking the CNN model as an example, the present invention is not limited thereto. For example, the feature extraction model may also be a support vector machine model, a template matching model, or the like. It is also within the scope of the invention.
[0067] Figure 7 is a flow chart showing a character recognition method applied to a cash register system of a client such as a shopping mall or a supermarket, according to further embodiments of the present invention.
[0068] Referring to Figure 7, in step S710, a sample set pushed by a target server through a unique identifier of a client is generated, and the sample set is generated based on a character image sent by the client and a standard result of the character image. Feature set.
[0069] The target server may be the sample training server 250 or the background server 220 described above, and the sample set is a set of feature vectors of each character of the character image and a label of the feature vector, and the label is a standard result of the character image. A
character corresponding to a single character of a character image.
character corresponding to a single character of a character image.
[0070] In step S720, the character image to be recognized is acquired, and the feature vector of each character in the character image to be recognized is extracted.
[0071] In an exemplary embodiment, the character image is first normalized, and the normalized character image is divided according to the pixel distribution to obtain a plurality of single characters, that is, 0 to 9 numeric characters, and the divided characters are extracted. A
single-character pixel feature yields a characteristic vector for each single character.
single-character pixel feature yields a characteristic vector for each single character.
[0072] In step S730, the feature vectors of the respective characters are matched with the feature vectors in the sample set.
[0073] In an example embodiment, the distance between the feature vector of each character and the feature vector in the sample set may be calculated, and the feature vector in the sample set closest to the character distance in the character image is taken as the matched feature vector.
The distance between the feature vectors may be a Hamming distance, an Euclidean distance, or a cosine distance, but the distance in the exemplary embodiment of the present invention is not limited thereto, and for example, the distance may also be a Mahalanobis distance, a Manhattan distance, or the like.
The distance between the feature vectors may be a Hamming distance, an Euclidean distance, or a cosine distance, but the distance in the exemplary embodiment of the present invention is not limited thereto, and for example, the distance may also be a Mahalanobis distance, a Manhattan distance, or the like.
[0074] In step S740, characters in the character image to be recognized are identified based on the matching result.
[0075] After obtaining the feature vector in the sample set that matches the character in the character image, the character in the character image to be recognized is determined based on the tag with the feature vector. For example, the matching feature vector is xi, and the feature vector has a label of yi, and the character of the character image to be recognized is yi.
[0076] Further, in some embodiments, after the character image to be recognized is recognized as an error, a standard result of the character image to be recognized input by the user may be received; a character image to be recognized, a recognition result of the character image to be recognized, and Standard results are sent to the target server.
[0077] Further, in still other embodiments of the present invention, a character recognition apparatus is also provided. Referring to Figure 8, the character recognition apparatus 800 may include an acquisition unit 810, a sample generation unit 820, and a sample push unit 830. The obtaining unit 810 is configured to acquire a character image corresponding to the unique identifier and a standard result of the character image based on the unique identifier of the client;
the sample generating unit 820 is configured to extract a feature of the character image, based on the feature of the character image And the standard result generating sample set; the sample pushing unit 830 is configured to push the sample set to the client based on the unique identifier, so that the client identifies the character image to be recognized through the sample set.
the sample generating unit 820 is configured to extract a feature of the character image, based on the feature of the character image And the standard result generating sample set; the sample pushing unit 830 is configured to push the sample set to the client based on the unique identifier, so that the client identifies the character image to be recognized through the sample set.
[0078] In some embodiments of the present invention, based on the foregoing solution, the obtaining unit 810 includes: a character recognition rate obtaining unit, configured to acquire a character recognition rate of the client based on the unique identifier of the client; and a determining unit, configured to: Determining whether the character recognition rate is less than a predetermined threshold; and the image obtaining unit is configured to acquire a corresponding character image based on the unique identifier of the client if the determination is less than the predetermined threshold.
[0079] In some embodiments of the present invention, the character recognition apparatus 800 further includes: a recognition result acquisition unit, configured to acquire a character image corresponding to the unique identifier based on the unique identifier of the client, a recognition result; a recognition rate determining unit configured to determine the character recognition rate of the client based on the recognition result and the standard result.
[0080] In some embodiments of the present invention, based on the foregoing scheme, the sample generation unit 820 includes: an extraction unit, configured to extract features of each character in the character image by a feature extraction model; and a character determination unit, configured to determine a target character corresponding to the respective characters in the standard result; a label generating unit, configured to use the target character as a label of the feature of each of the characters; and a sample set generating unit configured to calculate each character in the character image The feature and the tag of the feature generate the sample set.
[0081] In some embodiments of the present invention, the character recognition apparatus 800 further includes: a receiving unit, configured to receive the character image sent by the client, the recognition result of the character image, and a standard result; the first storage unit is configured to store the character image in the first storage area; and the second storage unit is configured to store the recognition result of the character image and the standard result in the second storage area.
[0082] In some embodiments of the present invention, based on the foregoing aspect, the first storage area is an image storage unit of a target server, and the second storage area is a relation data storage unit of the target server.
[0083] Since the respective functional modules of the character recognition apparatus 800 of the exemplary embodiment of the present invention correspond to the steps of the exemplary embodiment of the character recognition method illustrated in Figure 1 described above, details are not described herein again.
[0084] Further, in still other embodiments of the present invention, a character recognition apparatus is also provided. Referring to Figure 9, the character recognition apparatus may include a sample receiving unit 910, a feature extracting unit 920, a matching unit 930, and an identifying unit 940. The sample receiving unit 910 is configured to receive a sample set pushed by the target server by using a unique identifier of the client, where the sample set is a feature set generated based on a character image sent by the client and a standard result of the character image; The extracting unit 920 is configured to acquire a character image to be recognized, and extract a feature vector of each character in the character image to be recognized; the matching unit 930 is configured to match the feature vector of each character with the feature vector in the sample set; 940 is configured to identify characters in the character image to be recognized based on the matching result.
[0085] In some embodiments of the present invention, the character recognition apparatus 900 further includes: a standard result receiving unit, configured to receive a standard result of the character image to be recognized input by a user; and a sending unit, And sending the character image to be recognized, the recognition result of the character image to be recognized, and the standard result to the target server.
[0086] Since the respective functional modules of the character recognition apparatus 900 of the exemplary embodiment of the present invention correspond to the steps of the exemplary embodiment of the character recognition method of Figure 7, the details are not described herein.
[0087] In an exemplary embodiment of the present invention, there is also provided an electronic equipment capable of implementing the above method.
[0088] Referring now to Figure 10, a block diagram of a computer system 1000 suitable for use in implementing an electronic equipment in accordance with an embodiment of the present invention is shown. The computer system 1000 of the electronic equipment shown in Figure 10 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present invention.
[0089] As shown in Figure 10, the computer system 1000 includes a central processing unit (CPU) 1001 that can be loaded into a random access memory (RAM) 1003 according to a program stored in a read only memory (ROM) 1002 or from a storage portion 1008. The program in the middle performs various appropriate actions and processes. In the RAM
1003, various programs and data required for system operation are also stored. The CPU 1001 ROM 1002 and the RAM 1003 are connected to each other through a bus 1004. An input/output (I/O) interface 1005 is also coupled to bus 1004.
1003, various programs and data required for system operation are also stored. The CPU 1001 ROM 1002 and the RAM 1003 are connected to each other through a bus 1004. An input/output (I/O) interface 1005 is also coupled to bus 1004.
[0090] The following components are connected to the I/O interface 1005: an input portion 1006 including a keyboard, a mouse, etc.; an output portion 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, etc.; The storage portion 1008;
and a communication portion 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the Internet. Driver 1010 is also coupled to I/0 interface 1005 as needed. The removable medium 1011, such as disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the driver 1010 as required so that the computer program read from it can be installed into the storage section 1008 as required.
and a communication portion 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the Internet. Driver 1010 is also coupled to I/0 interface 1005 as needed. The removable medium 1011, such as disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the driver 1010 as required so that the computer program read from it can be installed into the storage section 1008 as required.
[0091] In particular, the processes described above with reference to the flowcharts may be implemented as a computer software program, in accordance with an embodiment of the present invention. For example, an embodiment of the invention includes a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for executing the method illustrated in the flowchart.
In such an embodiment, the computer program can be downloaded and installed from the network via the communication portion 1009, and/or installed from the removable medium 1011.
When the computer program is executed by the central processing unit (CPU) 1001, the above-described functions defined in the system of the present application are executed.
In such an embodiment, the computer program can be downloaded and installed from the network via the communication portion 1009, and/or installed from the removable medium 1011.
When the computer program is executed by the central processing unit (CPU) 1001, the above-described functions defined in the system of the present application are executed.
[0092] It should be noted that the computer readable medium illustrated by the present invention may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage apparatus, magnetic storage apparatus, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in connection with an instruction execution system, apparatus or device. In the present invention, a computer readable signal medium may include a data signal that is propagated in the baseband or as part of a carrier, in which computer readable program code is carried. Such propagated data signals can take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing.
The computer readable signal medium can also be any computer readable medium other than a computer readable storage medium, which can transmit, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device. Program code embodied on a computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The computer readable signal medium can also be any computer readable medium other than a computer readable storage medium, which can transmit, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device. Program code embodied on a computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
[0093] The flowchart and block diagram in the drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in a flowchart or block diagram may represent a module, program segment, or part of a code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams or flowcharts, and combinations of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be used A combination of dedicated hardware and computer instructions is implemented.
[0094] The units involved in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described unit may also be disposed in the processor. The names of these units do not in any way constitute a limitation on the unit itself.
[0095] In another aspect, the present application further provides a computer readable medium, which may be included in the electronic equipment described in the above embodiments; or may exist separately but not assembled In the electronic equipment. The computer-readable medium bears one or more programs that, when executed by one or more of the above-mentioned programs, enable the electronic device to implement a character recognition method as described in the above-mentioned embodiments.
[0096] For example, the electronic equipment may implement as shown in Figure 1: acquiring a character image corresponding to the unique identifier and a standard result of the character image based on a unique identifier of the client; extracting features of the character image Generating a sample set based on the characteristics of the character image and the standard result; pushing the sample set to the client based on the unique identifier to cause the client to identify a character image to be recognized by the sample set.
[0097] It should be noted that although several modules or units of apparatus or apparatus for action execution are mentioned in the detailed description above, such division is not mandatory.
In fact, the features and functions of the two or more modules or units described above may be embodied in one module or unit in accordance with the embodiments of the invention.
Conversely, the features and functions of one of the modules or units described above may be further divided into multiple modules or units.
In fact, the features and functions of the two or more modules or units described above may be embodied in one module or unit in accordance with the embodiments of the invention.
Conversely, the features and functions of one of the modules or units described above may be further divided into multiple modules or units.
[0098] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein may be implemented by software, or may be implemented by software in combination with necessary hardware.
Therefore, the technical solution according to the embodiment of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network. A
number of instructions are included to cause a computing apparatus (which may be a personal computer, server, touch terminal, or network apparatus, etc.) to perform a method in accordance with an embodiment of the present invention.
Therefore, the technical solution according to the embodiment of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network. A
number of instructions are included to cause a computing apparatus (which may be a personal computer, server, touch terminal, or network apparatus, etc.) to perform a method in accordance with an embodiment of the present invention.
[0099] Those skilled in the art upon consideration of the specification and practice of the invention disclosed herein, will readily appreciate other embodiments of the present invention.
The present application is intended to cover any variations, uses, or adaptations of the present invention, which are in accordance with the general principles of the present invention and include common general knowledge or conventional technical means in the art that are not disclosed in the present invention. The specification and examples are to be considered as illustrative only.
The present application is intended to cover any variations, uses, or adaptations of the present invention, which are in accordance with the general principles of the present invention and include common general knowledge or conventional technical means in the art that are not disclosed in the present invention. The specification and examples are to be considered as illustrative only.
[0100] It should be understood that the present invention is not limited to the above has been described and illustrated in the drawings precise structure, and may be carried out without departing from the scope of the various modifications and changes. The scope of the invention is limited only by the appended claims.
Claims (12)
1. A character recognition method, comprising:
Obtaining a character image corresponding to the unique identifier and a standard result of the character image based on a unique identifier of the client;
Extracting features of the character image, generating a sample set based on characteristics of the character image and the standard result;
Pushing the sample set to the client based on the unique identifier to cause the client to identify a character image to be recognized by the sample set.
Obtaining a character image corresponding to the unique identifier and a standard result of the character image based on a unique identifier of the client;
Extracting features of the character image, generating a sample set based on characteristics of the character image and the standard result;
Pushing the sample set to the client based on the unique identifier to cause the client to identify a character image to be recognized by the sample set.
2. The character recognition method according to Claim 1, wherein the obtaining a character image corresponding to the unique identifier based on the unique identifier of the client comprises:
Obtaining a character recognition rate of the client based on the unique identifier of the client;
Determining whether the character recognition rate is less than a predetermined threshold;
If the determination is less than the predetermined threshold, the corresponding character image is obtained based on the unique identifier of the client.
Obtaining a character recognition rate of the client based on the unique identifier of the client;
Determining whether the character recognition rate is less than a predetermined threshold;
If the determination is less than the predetermined threshold, the corresponding character image is obtained based on the unique identifier of the client.
3. The character recognition method according to Claim 2, wherein the character recognition method further comprises:
Acquiring a recognition result of the character image corresponding to the unique identifier based on the unique identifier of the client;
The character recognition rate of the client is determined based on the recognition result and the standard result.
Acquiring a recognition result of the character image corresponding to the unique identifier based on the unique identifier of the client;
The character recognition rate of the client is determined based on the recognition result and the standard result.
4. The character recognition method according to Claim 1, wherein extracting a feature of the character image, generating a sample set based on the feature of the character image and the standard result, comprises:
Extracting features of each character in the character image by a feature extraction model;
Determining a target character corresponding to each of the characters in the standard result;
Using the target character as a label of the feature of the respective character;
The sample set is generated based on a feature of each character in the character image and a tag of the feature.
Extracting features of each character in the character image by a feature extraction model;
Determining a target character corresponding to each of the characters in the standard result;
Using the target character as a label of the feature of the respective character;
The sample set is generated based on a feature of each character in the character image and a tag of the feature.
5. The character recognition method according to Claim 1, wherein the character recognition method further comprises:
Receiving the character image sent by the client, the recognition result of the character image, and a standard result;
Storing the character image in a first storage area;
The recognition result of the character image and the standard result are stored in the second storage area.
Receiving the character image sent by the client, the recognition result of the character image, and a standard result;
Storing the character image in a first storage area;
The recognition result of the character image and the standard result are stored in the second storage area.
6. The character recognition method according to Claim 5, wherein the first storage area is an image storage unit of a target server, and the second storage area is a relation data storage unit of the target server.
7. A character recognition method, comprising:
Receiving, by the target server, a sample set pushed by the unique identifier of the client, where the sample set is a feature set generated based on a character image sent by the client and a standard result of the character image;
Obtaining a character image to be recognized, and extracting a feature vector of each character in the character image to be recognized;
Matching feature vectors of individual characters with feature vectors in the sample set;
Characters in the character image to be recognized are identified based on the matching result.
Receiving, by the target server, a sample set pushed by the unique identifier of the client, where the sample set is a feature set generated based on a character image sent by the client and a standard result of the character image;
Obtaining a character image to be recognized, and extracting a feature vector of each character in the character image to be recognized;
Matching feature vectors of individual characters with feature vectors in the sample set;
Characters in the character image to be recognized are identified based on the matching result.
8. The character recognition method according to Claim 7, wherein the character recognition method further comprises:
Receiving a standard result of the character image to be recognized input by a user;
Sending the character image to be recognized, the recognition result of the character image to be recognized, and the standard result to the target server.
Receiving a standard result of the character image to be recognized input by a user;
Sending the character image to be recognized, the recognition result of the character image to be recognized, and the standard result to the target server.
9. A character recognition apparatus, comprising:
An obtaining unit, configured to acquire, according to a unique identifier of the client, a character image corresponding to the unique identifier and a standard result of the character image;
A sample generating unit, configured to extract a feature of the character image, and generate a sample set based on the feature of the character image and the standard result;
And a sample pushing unit, configured to push the sample set to the client based on the unique identifier, so that the client identifies the character image to be recognized through the sample set.
An obtaining unit, configured to acquire, according to a unique identifier of the client, a character image corresponding to the unique identifier and a standard result of the character image;
A sample generating unit, configured to extract a feature of the character image, and generate a sample set based on the feature of the character image and the standard result;
And a sample pushing unit, configured to push the sample set to the client based on the unique identifier, so that the client identifies the character image to be recognized through the sample set.
10. A character recognition apparatus, comprising:
A sample receiving unit, configured to receive a sample set pushed by the target server by using a unique identifier of the client, where the sample set is a feature set generated based on a character image sent by the client and a standard result of the character image;
A feature extraction unit, configured to acquire a character image to be recognized, and extract a feature vector of each character in the character image to be recognized;
A matching unit, configured to match a feature vector of each character with a feature vector in the sample set;
And an identifying unit, configured to identify a character in the character image to be recognized based on the matching result.
A sample receiving unit, configured to receive a sample set pushed by the target server by using a unique identifier of the client, where the sample set is a feature set generated based on a character image sent by the client and a standard result of the character image;
A feature extraction unit, configured to acquire a character image to be recognized, and extract a feature vector of each character in the character image to be recognized;
A matching unit, configured to match a feature vector of each character with a feature vector in the sample set;
And an identifying unit, configured to identify a character in the character image to be recognized based on the matching result.
11. An electronic equipment, comprising:
A processor; and A memory having computer readable instructions stored thereon, the computer readable instructions being executed by the processor to implement the character recognition method of any one of Claims 1 to 8.
A processor; and A memory having computer readable instructions stored thereon, the computer readable instructions being executed by the processor to implement the character recognition method of any one of Claims 1 to 8.
12. A computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the character recognition method according to any one of Claims 1 to 8.
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| CN201810967423.9 | 2018-08-23 | ||
| CN201810967423.9A CN109086834B (en) | 2018-08-23 | 2018-08-23 | Character recognition method, character recognition device, electronic equipment and storage medium |
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|---|---|---|---|---|
| CN111639648A (en) * | 2020-05-26 | 2020-09-08 | 浙江大华技术股份有限公司 | Certificate identification method and device, computing equipment and storage medium |
| CN112508003A (en) * | 2020-12-18 | 2021-03-16 | 北京百度网讯科技有限公司 | Character recognition processing method and device |
| CN112529004A (en) * | 2020-12-08 | 2021-03-19 | 平安科技（深圳）有限公司 | Intelligent image recognition method and device, computer equipment and storage medium |
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| CN110399912B (en) * | 2019-07-12 | 2023-04-07 | 广东浪潮大数据研究有限公司 | Character recognition method, system, equipment and computer readable storage medium |
| CN110956133A (en) * | 2019-11-29 | 2020-04-03 | 上海眼控科技股份有限公司 | Training method of single character text normalization model, text recognition method and device |
| CN111325196A (en) * | 2020-03-05 | 2020-06-23 | 上海眼控科技股份有限公司 | Vehicle frame number detection method and device, computer equipment and storage medium |
| CN111507355B (en) * | 2020-04-17 | 2023-08-22 | 北京百度网讯科技有限公司 | Character recognition method, device, equipment and storage medium |
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| US7269737B2 (en) * | 2001-09-21 | 2007-09-11 | Pay By Touch Checking Resources, Inc. | System and method for biometric authorization for financial transactions |
| CN105184084B (en) * | 2015-09-14 | 2018-05-01 | 深圳供电局有限公司 | A kind of automatic power-measuring terminal fault type prediction method and system |
| CN105530265B (en) * | 2016-01-28 | 2019-01-18 | 李青山 | A kind of mobile Internet malicious application detection method based on frequent item set description |
| CN107025567A (en) * | 2016-02-01 | 2017-08-08 | 秒针信息技术有限公司 | A kind of data processing method and device |
| CN107861957B (en) * | 2016-09-22 | 2020-10-02 | 杭州海康威视数字技术股份有限公司 | Data analysis method and device |
| CN107368827B (en) * | 2017-04-01 | 2020-09-15 | 阿里巴巴集团控股有限公司 | Character recognition method and device, user equipment and server |
| CN106954207B (en) * | 2017-04-25 | 2018-06-05 | 腾讯科技（深圳）有限公司 | A kind of method and device for the account attributes value for obtaining target terminal |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639648A (en) * | 2020-05-26 | 2020-09-08 | 浙江大华技术股份有限公司 | Certificate identification method and device, computing equipment and storage medium |
| CN111639648B (en) * | 2020-05-26 | 2023-09-19 | 浙江大华技术股份有限公司 | Certificate identification method, device, computing equipment and storage medium |
| CN112529004A (en) * | 2020-12-08 | 2021-03-19 | 平安科技（深圳）有限公司 | Intelligent image recognition method and device, computer equipment and storage medium |
| CN112508003A (en) * | 2020-12-18 | 2021-03-16 | 北京百度网讯科技有限公司 | Character recognition processing method and device |
| CN112508003B (en) * | 2020-12-18 | 2023-10-13 | 北京百度网讯科技有限公司 | Character recognition processing method and device |
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| CN109086834A (en) | 2018-12-25 |
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