CN113256583A - Image quality detection method and apparatus, computer device, and medium - Google Patents
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Abstract
The disclosure provides an image quality detection method and device, computer equipment and a medium, relates to the field of artificial intelligence, in particular to computer vision and deep learning technology, and can be applied to OCR (optical character recognition) scenes. The implementation scheme is as follows: performing global quality detection on the image to be detected to obtain a global quality detection result; in response to determining that the global quality detection result meets a first preset condition, extracting at least one region to be detected from the image to be detected; aiming at each area to be detected in at least one area to be detected, performing area quality detection on the area to be detected to obtain a corresponding area quality detection result; and determining the quality of the image to be detected based on the respective corresponding region quality detection result of the at least one region to be detected.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and in particular, to computer vision and deep learning techniques, which can be applied in OCR scenarios, and in particular, to an image quality detection method, apparatus, computer-readable storage medium, and computer program product.
Background
Artificial intelligence is the subject of research that makes computers simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. The artificial intelligence hardware technology generally comprises technologies such as a sensor, a special artificial intelligence chip, cloud computing, distributed storage, big data processing and the like, and the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge graph technology and the like. In recent years, artificial intelligence techniques have been widely used in various fields, for example, the field of image detection.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.
Disclosure of Invention
The present disclosure provides an image quality detection method, apparatus, computer-readable storage medium, and computer program product.
According to an aspect of the present disclosure, there is provided an image quality detection method including: performing global quality detection on the image to be detected to obtain a global quality detection result; in response to determining that the global quality detection result meets a first preset condition, extracting at least one region to be detected from the image to be detected; aiming at each area to be detected in at least one area to be detected, performing area quality detection on the area to be detected to obtain a corresponding area quality detection result; and determining the quality of the image to be detected based on the respective corresponding region quality detection result of the at least one region to be detected.
According to another aspect of the present disclosure, there is provided a character recognition method including: acquiring an image to be identified; the image quality detection method is adopted to carry out quality detection on the image to be identified so as to obtain the image to be identified with qualified quality; and executing character recognition on the obtained image to be recognized with qualified quality.
According to another aspect of the present disclosure, there is provided an image quality detection apparatus including: the first detection unit is configured to execute global quality detection on the image to be detected to obtain a global quality detection result; an extraction unit configured to extract at least one region to be detected from an image to be detected in response to determining that the global quality detection result satisfies a first preset condition; the second detection unit is configured to perform area quality detection on each area to be detected in the at least one area to be detected to obtain a corresponding area quality detection result; and the determining unit is configured to determine the quality of the image to be detected based on the respective corresponding region quality detection result of the at least one region to be detected.
According to another aspect of the present disclosure, there is provided a character recognition apparatus including: an acquisition unit configured to acquire an image to be recognized; the fourth detection unit is configured to perform quality detection on the image to be identified by adopting the image quality detection method so as to obtain the image to be identified with qualified quality; and the identification unit is configured to perform character identification on the obtained image to be identified with qualified quality.
According to another aspect of the present disclosure, there is provided a computing device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method according to any one of the above.
According to another aspect of the disclosure, a computer program product is provided, comprising a computer program, wherein the computer program realizes the method of any of the above when executed by a processor
According to one or more embodiments of the present disclosure, automatic quality detection of a large number of images can be achieved, and satisfactory results in both detection effects and detection efficiency can be obtained at the same time.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.
FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;
FIG. 2 shows a flow diagram of a method of image quality detection according to an embodiment of the present disclosure;
FIG. 3 shows a flow diagram of another method of image quality detection according to an embodiment of the present disclosure;
FIG. 4 shows a flow diagram of a text recognition method according to an embodiment of the present disclosure;
fig. 5 shows a block diagram of the structure of an image quality detection apparatus according to an embodiment of the present disclosure;
FIG. 6 shows a block diagram of a text recognition device according to an embodiment of the present disclosure;
FIG. 7 illustrates a block diagram of an exemplary computer device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.
The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.
With the development of data acquisition technology, people increasingly adopt portable mobile devices such as mobile phones and the like to acquire images, and realize information input based on OCR recognition of the acquired images. However, the image input by the user often has uneven quality, and various conditions such as blurring, light shadow, shaking and the like exist, so that recognition errors often occur in the subsequent OCR recognition process.
In the related art, in order to improve the accuracy of OCR recognition, manual quality detection needs to be performed on an acquired image, so that great repetitive labor is caused, a large amount of labor cost and time cost are consumed, and the efficiency of input work is low.
Based on this, the present disclosure provides an image quality detection method, which first performs global quality detection on an image to be detected to obtain a global quality detection result, performs area quality detection on at least one area to be detected in the image to be detected to obtain a corresponding area quality detection result when the global quality detection result satisfies a first preset condition, and determines the quality of the image to be detected based on the respective area quality detection results of the at least one area to be detected. According to the image quality detection method and device, the coarse-fine quality detection of the global quality detection level and the regional quality detection level is successively performed on the image to be detected, the image with poor overall quality in the image to be detected can be efficiently screened out through the global quality detection, and the image with qualified overall quality is further subjected to regional quality detection. Therefore, automatic quality detection of a large number of images can be realized, and satisfactory effects on detection effects and detection efficiency can be achieved simultaneously.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. Client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.
In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable the image quality detection method to be performed.
In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of client devices 101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.
In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user operating a client device 101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.
The user may use client devices 101, 102, 103, 104, 105, and/or 106 to capture images. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.
The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.
The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.
In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.
In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS) service.
The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The data store 130 may reside in various locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 130 may be of different types. In certain embodiments, the data store used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.
In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.
The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.
Fig. 2 is a flowchart illustrating an image quality detection method according to an exemplary embodiment of the present disclosure, which may include, as shown in fig. 2: step S201, performing global quality detection on an image to be detected to obtain a global quality detection result; step S202, in response to the fact that the global quality detection result meets a first preset condition, extracting at least one to-be-detected region from the to-be-detected image; step S203, aiming at each area to be detected in at least one area to be detected, performing area quality detection on the area to be detected to obtain a corresponding area quality detection result; and S204, determining the quality of the image to be detected based on the respective corresponding region quality detection result of at least one region to be detected.
Based on the method, the detection of two levels of global quality detection and regional quality detection can be successively performed on the image to be detected, the image with poor overall quality in the image to be detected can be efficiently screened out through the global quality detection, and the image with qualified overall quality is further subjected to regional quality detection. Therefore, automatic quality detection of a large number of images can be realized, and satisfactory effects on detection effects and detection efficiency can be achieved simultaneously.
For step S201, the global quality detection is performed on the whole image to be detected, and the detected indexes may include one or more of the indexes of the whole image to be detected, such as definition, reflectivity, coverage degree, and wrinkle degree.
According to some embodiments, the global quality detection may include global sharpness detection, and the global sharpness detection result of the image to be detected may be determined based on a mean square error calculation of the image to be detected. The mean square error calculation operation speed is high, so that the whole image definition of the image to be detected can be conveniently detected.
For step S202, according to some embodiments, in a case that the global quality detection includes global sharpness detection, the global quality detection result includes a global sharpness detection result, wherein, in response to determining that the global quality detection result satisfies a first preset condition, extracting at least one to-be-detected region from the to-be-detected image may include: and in response to determining that the global definition detection result is higher than a preset threshold, extracting at least one region to be detected from the image to be detected.
From this, can be at the in-process to the automatic image quality detection of a large amount of images, detect through global definition, sieve out wherein the not good image of whole definition fast, do not wait to detect the extraction of regional to the not good image execution of whole definition, and only wait to detect the extraction of regional to the clear image execution that waits, promoted the detection efficiency that detects the image quality of a large amount of images effectively from this.
According to some embodiments, in response to determining that the global quality detection result does not satisfy the first preset condition, discarding the current image to be detected, and re-acquiring the image to be detected.
According to some embodiments, before extracting at least one region to be detected from an image to be detected, correction is performed on the image to be detected based on affine transformation. Therefore, the accuracy of extracting the region to be detected can be improved, and the specified region to be detected can be conveniently extracted from the image to be detected.
According to some embodiments, the four corners of the complete picture to be detected and the corresponding four corners in the preset template can be respectively subjected to corner alignment, a relevant affine matrix is obtained based on the corresponding relation of the four pairs of corners, and affine transformation is performed on the image to be detected based on the obtained relevant affine matrix, so that the corrected image to be detected is obtained.
For step S203, after at least one region to be detected is extracted, region quality detection may be performed on the region to be detected.
According to some embodiments, the detection accuracy of the regional quality detection is higher than the detection accuracy of the global quality detection.
In order to obtain an ideal recognition effect in a subsequent OCR recognition process, quality detection with high detection precision needs to be performed on a specific to-be-detected region of an image to be detected. By matching the detection areas with different sizes and different detection precisions, namely, the overall quality detection of the whole image to be detected adopts the quality detection with relatively low detection precision, and the appointed area to be detected of the image to be detected adopts the quality detection with relatively high detection precision, so that the detection efficiency of the image to be detected can be improved under the condition of ensuring the subsequent OCR identification quality requirement.
According to some embodiments, the region quality detection may be performed on the region to be detected by the first depth detection model. The first depth detection model may adopt one or more machine learning models such as a neural network, a decision tree, a classifier, and the like.
According to some embodiments, the region quality detection is detection performed on a specific region of the image to be detected, and the detected indexes may include one or more of indexes of definition, light reflection, covering degree, wrinkle degree and the like of the region to be detected.
With respect to step S204, according to some embodiments, determining the quality of the image to be detected based on the respective corresponding region quality detection result of the at least one region to be detected includes: for each to-be-detected area in at least one to-be-detected area, determining the to-be-detected area as a qualified area in response to determining that the area quality detection result corresponding to the to-be-detected area meets a second preset condition; and determining the quality of the image to be detected based on at least one of the total number or the total area of the regions to be detected which are determined to be qualified regions. Therefore, the quality of the whole image to be detected can be conveniently determined on the basis of the detection result of each region to be detected.
According to some embodiments, the quality of the image to be detected may be determined based on the total number of regions to be detected determined as qualified regions.
In one embodiment, the image to be detected is determined to be a quality-qualified image in response to the ratio of the total number of the regions to be detected determined to be qualified to the total number of the regions to be detected extracted from the image to be detected being greater than a preset threshold.
According to some embodiments, the quality of the image to be detected may be determined based on the total area of the region to be detected determined to be a qualified region.
In one embodiment, in response to the ratio of the total area of the regions to be detected determined to be qualified regions to the area of the image to be detected being greater than a preset threshold, the image to be detected is determined to be a qualified image.
According to some embodiments, the quality of the image to be detected may be determined based on both the total number and the total area of the regions to be detected determined to be qualified regions.
According to some embodiments, the image to be detected is a captured image of a card.
In order to realize the input process of card information through the collection of image information, the quality of the collected card image can be controlled based on the method for detecting the image quality disclosed by the invention, so that the manual checking workload can be effectively reduced, the labor cost is reduced, the input automation of the card information is realized, and the input efficiency is improved.
The card may include a driving card, a bank card, an identity card and other documents.
According to some embodiments, before extracting at least one region to be detected from an image to be detected, performing a stuck integrity detection on the image to be detected; and in response to the fact that the detection result obtained by the card integrity detection indicates that the card in the image to be detected is complete, extracting at least one region to be detected from the image to be detected. Therefore, the extraction of at least one region to be detected can be executed only aiming at the image to be detected with complete card, the processing efficiency of the image to be detected is improved, and the effectiveness of the extracted region to be detected is ensured.
According to some embodiments, in response to determining that the detection result obtained by the card integrity detection indicates that the card in the image to be detected is incomplete, discarding the current image to be detected, and re-acquiring the image to be detected.
According to some embodiments, the card integrity check may be performed by a second depth detection model. The second depth detection model may also adopt one or more machine learning models such as a neural network, a decision tree, a classifier, and the like.
In one embodiment, the corner points of the jams in the image to be detected can be identified through the second depth detection model, and the integrity of the jams in the image to be detected is determined in response to the fact that the corner points of the jams in the image to be detected are equal to four.
In another embodiment, the incomplete jam in the image to be detected is determined in response to the identified corner point of the jam in the image to be detected being less than four.
In one embodiment, the coordinates of each of the four corner points of the card in the image to be detected can be predicted through the second depth detection model, and the integrity of the card in the image to be detected is determined in response to the fact that the coordinates of each of the four corner points are within the range of the image to be detected.
In another embodiment, the incomplete jam in the image to be detected is determined in response to the coordinate of any one of the four corner points being outside the range of the image to be detected.
According to some embodiments, prior to extracting at least one region to be detected from an image to be detected, performing a card type identification on the image to be detected; and performing extraction of at least one region to be detected on the image to be detected based on the recognition result of the card type recognition. Therefore, the extraction of at least one region to be detected is executed on the image to be detected only when the identification result of the card type identification meets the condition, the processing efficiency of the image to be detected is improved, and the effectiveness of the extracted region to be detected is ensured.
According to some embodiments, in response to determining that the recognition result of the card-type recognition is the target type, extraction of at least one region to be detected is performed on the image to be detected.
According to some embodiments, in response to determining that the recognition result of the card type recognition is not the target type, discarding the current image to be detected, and re-acquiring the image to be detected.
Wherein the target type can be one or more card types specified.
According to some embodiments, the stuck target recognition may be performed by a third depth detection model. The third depth detection model can adopt one or more machine learning models such as a neural network, a decision tree and a classifier.
According to some embodiments, the integrity detection and the card type identification of the card may also be performed simultaneously by the second depth detection model. The integrity detection result of the card and the type identification result of the card can be respectively output through different branches of the second depth detection model. From this, can realize the detection of two kinds of differences simultaneously based on same degree of depth detection model, can effectively promote the efficiency that detects.
According to some embodiments, wherein, based on the recognition result of the card type recognition, the extracting of the at least one region to be detected from the image to be detected comprises: acquiring at least one key field position information corresponding to the identification result; and aiming at each key field position information in at least one key field position information, extracting a region to be detected corresponding to the key field position information from the image to be detected. Therefore, at least one region to be detected corresponding to the identification result can be extracted in a targeted manner based on different identification results of the cards.
According to some embodiments, obtaining at least one key field location information corresponding to the recognition result comprises: acquiring a card template corresponding to the card type identified by the identification result, wherein the card template comprises at least one key field position information of the card type; and performing extraction of at least one region to be detected based on the acquired position information of at least one key field included in the card template.
The position information may include one or more of a coordinate value of each key field in the card or a side length of an area where each key field is located.
For example, in the case that the card type identified by the identification result is a bank card, the card template corresponding to the bank card may be acquired. The card template of the bank card comprises at least one key field position information, such as the position information of the card number of the bank card, the position information of the name of the issuer of the bank card, or the position information of the effective date of the bank card. Based on the key field position information in the card template, a bank card number area, a bank card issuing bank name area, an effective date area and the like of the bank card in the image to be detected can be extracted from the image to be detected.
Fig. 3 is a flowchart illustrating an image quality detection method for a card image according to an exemplary embodiment of the present disclosure, and as shown in fig. 3, a specific detection process may be implemented by the following flows:
s301, acquiring a card image through an acquisition system, wherein the acquisition system can acquire an independent image as an image to be detected and can also acquire a video, and each frame in the acquired video is used as the image to be detected to execute subsequent image quality detection;
step S302, carrying out global quality detection on the collected image to be detected;
step S303, judging whether the overall quality detection result of the overall quality detection of the image to be detected is qualified, executing step S304 under the condition that the overall quality detection result is qualified, abandoning the current image to be detected under the condition that the overall quality detection result is unqualified, and executing step S301 again;
step S304, adopting a fourth depth detection model, and simultaneously executing card type identification and integrity detection of the image to be detected so as to respectively input a card type identification result and an integrity detection result in two output branches of the fourth depth detection model;
step S305A, judging whether the card type identification result is the target type, if the card type identification result belongs to the target type, continuing to execute the step S306, if the card type identification result does not belong to the target type, abandoning the current image to be detected, and executing the step S301 again;
step S305B, judging whether the integrity detection result indicates that the card in the image to be detected is complete or not, if the card is complete, continuing to execute step S306, and if the card is incomplete, giving up the current image to be detected, and re-executing step S301;
step S306, performing image correction on the image to be detected;
step S307, based on the card type identification result obtained in the step S304, performing template matching on the card in the image to be detected to obtain a card template corresponding to the card contained in the image to be detected;
s308, extracting at least one region to be detected corresponding to the position information of at least one key field from the image to be detected based on the position information of at least one key field contained in the card template obtained by matching;
step S309, performing area quality detection on each area to be detected in at least one area to be detected;
step S310, judging the final quality of the image to be detected based on the region quality detection result of each region to be detected, outputting the image with qualified quality to execute subsequent OCR recognition processing under the condition that the final quality is judged to be qualified, abandoning the current image to be detected and executing step S301 again under the condition that the final quality is judged to be unqualified.
It is understood that there is no sequential limitation in the execution of the above steps S305A and S305A, and the steps S305A and S305A may be executed simultaneously.
After the quality of the image to be detected is judged to be acceptable based on step S310, and before the quality-acceptable image is output to perform the subsequent OCR recognition processing, the output quality-acceptable image may be input again into the acquisition system to perform the above-described steps S301 to S310 again. If the final quality of the qualified image is also judged to be qualified in step S310 during the second image quality inspection of the qualified image, the image is output to perform the subsequent OCR recognition processing.
It can be understood that, for the same image, the above steps S301 to S310 may be repeated multiple times to ensure the quality of the output image and improve the effect of detecting the image quality.
According to another aspect of the present disclosure, fig. 4 shows a flowchart of a text recognition method according to an exemplary embodiment of the present disclosure, and as shown in fig. 4, the method may include: s401, acquiring an image to be identified; s402, performing quality detection on the image to be identified by adopting the image quality detection method to obtain the image to be identified with qualified quality; and step S403, performing character recognition on the obtained image to be recognized with qualified quality. Therefore, the accuracy of character recognition can be effectively improved.
According to another aspect of the present disclosure, as shown in fig. 5, there is also provided an image quality detecting apparatus 500, as shown in fig. 5, the apparatus 500 comprising: the first detection unit 501 is configured to perform global quality detection on an image to be detected to obtain a global quality detection result; an extracting unit 502 configured to extract at least one region to be detected from an image to be detected in response to determining that the global quality detection result satisfies a first preset condition; the second detection unit 503 is configured to perform, for each to-be-detected area in the at least one to-be-detected area, area quality detection on the to-be-detected area to obtain a corresponding area quality detection result; and a determining unit 504 configured to determine the quality of the image to be detected based on the respective region quality detection results corresponding to the at least one region to be detected.
According to some embodiments, the detection accuracy of the regional quality detection is higher than the detection accuracy of the global quality detection.
According to some embodiments, the global quality detection comprises a global sharpness detection, the global quality detection result comprises a global sharpness detection result, the extraction unit is further configured to: and in response to determining that the global definition detection result is higher than a preset threshold, extracting at least one region to be detected from the image to be detected.
According to some embodiments, the determining unit comprises: the first determining subunit is configured to determine, for each to-be-detected area in the at least one to-be-detected area, the to-be-detected area as a qualified area in response to determining that an area quality detection result corresponding to the to-be-detected area satisfies a second preset condition; and a second determining subunit configured to determine the quality of the image to be detected based on at least one of the total number or the total area of the regions to be detected determined as the qualified regions.
According to some embodiments, the image to be detected is a captured image of a card.
According to some embodiments, the apparatus further comprises: the third detection unit is configured to perform card integrity detection on the image to be detected before at least one region to be detected is extracted from the image to be detected; and the extraction unit is further configured to perform extraction of at least one region to be detected on the image to be detected in response to determining that the detection result obtained by the card integrity detection indicates that the card in the image to be detected is intact.
According to another method of the present disclosure, there is also provided a character recognition apparatus 600, as shown in fig. 6, the apparatus 600 includes: an acquisition unit 601 configured to acquire an image to be recognized; the fourth detecting unit 602 is configured to perform quality detection on the image to be recognized by using the image quality detecting method described above, so as to obtain an image to be recognized with qualified quality; and a recognition unit 603 configured to perform character recognition on the resulting quality-qualified image to be recognized.
According to another aspect of the present disclosure, there is also provided a computing device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described method.
According to another aspect of the present disclosure, there is also provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the above-described method.
According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, wherein the computer program realizes the above-mentioned method when executed by a processor.
Referring to fig. 7, a block diagram of a structure of an electronic device 700, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. The input unit 706 may be any type of device capable of inputting information to the device 700, and the input unit 706 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote controller. Output unit 707 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 708 may include, but is not limited to, magnetic or optical disks. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 1302.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.
Claims (20)
1. An image quality detection method comprising:
performing global quality detection on the image to be detected to obtain a global quality detection result;
in response to determining that the global quality detection result meets a first preset condition, extracting at least one region to be detected from the image to be detected;
aiming at each area to be detected in the at least one area to be detected, performing area quality detection on the area to be detected to obtain a corresponding area quality detection result; and
and determining the quality of the image to be detected based on the respective corresponding region quality detection result of the at least one region to be detected.
2. The method of claim 1, wherein the detection accuracy of the regional quality detection is higher than the detection accuracy of the global quality detection.
3. The method according to claim 1 or 2, wherein the global quality detection comprises a global sharpness detection, the global quality detection result comprises a global sharpness detection result,
wherein, in response to determining that the global quality detection result satisfies a first preset condition, extracting at least one region to be detected from the image to be detected comprises:
and in response to determining that the global definition detection result is higher than a preset threshold value, extracting at least one region to be detected from the image to be detected.
4. The method according to any one of claims 1 to 3, wherein the determining the quality of the image to be detected based on the region quality detection result corresponding to each of the at least one region to be detected comprises:
for each to-be-detected area in the at least one to-be-detected area, determining the to-be-detected area as a qualified area in response to determining that the area quality detection result corresponding to the to-be-detected area meets a second preset condition; and
determining the quality of the image to be detected based on at least one of the total number or the total area of the regions to be detected determined as qualified regions.
5. The method of any of claims 1 to 4, further comprising:
before extracting at least one region to be detected from the image to be detected, performing correction on the image to be detected based on affine transformation.
6. The method according to any one of claims 1 to 5, wherein the image to be detected is a captured image of a card.
7. The method of claim 6, further comprising:
before at least one to-be-detected region is extracted from the to-be-detected image, performing card integrity detection on the to-be-detected image; and
and in response to the fact that the detection result obtained by the card integrity detection indicates that the card in the image to be detected is complete, extracting at least one region to be detected from the image to be detected.
8. The method of claim 6 or 7, further comprising:
before at least one to-be-detected region is extracted from the to-be-detected image, performing card type identification on the to-be-detected image; and
and extracting at least one to-be-detected region from the to-be-detected image based on the identification result of the card type identification.
9. The method according to claim 8, wherein said performing, based on the recognition result of said identification of the type of said card, extraction of at least one region to be detected on said image to be detected comprises:
acquiring at least one key field position information corresponding to the identification result; and
and aiming at each key field position information in the at least one key field position information, extracting a region to be detected corresponding to the key field position information from the image to be detected.
10. A method of word recognition, comprising:
acquiring an image to be identified;
performing quality detection on the image to be identified by adopting the image quality detection method according to any one of claims 1 to 9 to obtain the image to be identified with qualified quality; and
and executing character recognition on the obtained image to be recognized with qualified quality.
11. An image quality detection apparatus comprising:
the first detection unit is configured to execute global quality detection on the image to be detected to obtain a global quality detection result;
an extraction unit configured to extract at least one region to be detected from the image to be detected in response to determining that the global quality detection result satisfies a first preset condition;
the second detection unit is configured to perform area quality detection on each area to be detected in the at least one area to be detected to obtain a corresponding area quality detection result; and
a determining unit configured to determine the quality of the image to be detected based on the respective region quality detection results corresponding to the at least one region to be detected.
12. The apparatus of claim 11, wherein a detection accuracy of the regional quality detection is higher than a detection accuracy of the global quality detection.
13. The apparatus according to claim 11 or 12, wherein the global quality detection comprises a global sharpness detection, the global quality detection result comprises a global sharpness detection result,
wherein the extraction unit is further configured to:
and in response to determining that the global definition detection result is higher than a preset threshold value, extracting at least one region to be detected from the image to be detected.
14. The apparatus of any of claims 11 to 13, wherein the determining unit comprises:
the first determining subunit is configured to determine, for each to-be-detected area in the at least one to-be-detected area, in response to determining that the area quality detection result corresponding to the to-be-detected area satisfies a second preset condition, the to-be-detected area as a qualified area; and
a second determining subunit configured to determine a quality of the image to be detected based on at least one of a total number or a total area of the regions to be detected determined as qualified regions.
15. The apparatus according to any one of claims 11 to 14, wherein the image to be detected is a captured image of a card.
16. The apparatus of claim 15, further comprising:
the third detection unit is used for performing card integrity detection on the image to be detected before at least one region to be detected is extracted from the image to be detected; and
the extraction unit is further configured to perform extraction of at least one region to be detected on the image to be detected in response to determining that the detection result obtained by the card integrity detection indicates that the card in the image to be detected is intact.
17. A character recognition apparatus comprising:
an acquisition unit configured to acquire an image to be recognized;
a fourth detection unit, configured to perform quality detection on the image to be identified by using the image quality detection method according to any one of claims 1 to 9, so as to obtain an image to be identified with qualified quality; and
and the identification unit is configured to perform character identification on the obtained image to be identified with qualified quality.
18. A computing device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
19. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.
20. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-10 when executed by a processor.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114495103A (en) * | 2022-01-28 | 2022-05-13 | 北京百度网讯科技有限公司 | Text recognition method, text recognition device, electronic equipment and medium |
| CN114511694A (en) * | 2022-01-28 | 2022-05-17 | 北京百度网讯科技有限公司 | Image recognition method, image recognition device, electronic equipment and medium |
| CN114998282A (en) * | 2022-06-16 | 2022-09-02 | 平安科技（深圳）有限公司 | Image detection method, image detection device, electronic equipment and storage medium |
| WO2023040314A1 (en) * | 2021-09-15 | 2023-03-23 | 上海商汤智能科技有限公司 | Image test method and apparatus, electronic device, and storage medium |
| CN117058689A (en) * | 2023-10-09 | 2023-11-14 | 巴斯夫一体化基地(广东)有限公司 | Offline detection data processing method for chemical production |
| CN114998282B (en) * | 2022-06-16 | 2024-05-31 | 平安科技（深圳）有限公司 | Image detection method, device, electronic equipment and storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120026292A1 (en) * | 2010-07-27 | 2012-02-02 | Hon Hai Precision Industry Co., Ltd. | Monitor computer and method for monitoring a specified scene using the same |
| US20120324534A1 (en) * | 2009-11-17 | 2012-12-20 | Holograms Industries | Method and system for automatically checking the authenticity of an identity document |
| CN105631457A (en) * | 2015-12-17 | 2016-06-01 | 小米科技有限责任公司 | Method and device for selecting picture |
| CN110175980A (en) * | 2019-04-11 | 2019-08-27 | 平安科技（深圳）有限公司 | Image definition recognition methods, image definition identification device and terminal device |
| CN110263775A (en) * | 2019-05-29 | 2019-09-20 | 阿里巴巴集团控股有限公司 | Image-recognizing method, device, equipment and authentication method, device, equipment |
| CN110335246A (en) * | 2019-05-29 | 2019-10-15 | 成都数之联科技有限公司 | A kind of license picture clarity evaluation method |
| CN110458790A (en) * | 2018-05-03 | 2019-11-15 | 优酷网络技术（北京）有限公司 | A kind of image detecting method, device and computer storage medium |
| CN111161251A (en) * | 2019-12-31 | 2020-05-15 | 普联技术有限公司 | Method and device for calculating definition of face image |
| CN111860489A (en) * | 2019-12-09 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Certificate image correction method, device, equipment and storage medium |
| CN112102309A (en) * | 2020-09-27 | 2020-12-18 | 中国建设银行股份有限公司 | Method, device and equipment for determining image quality evaluation result |
| CN112367518A (en) * | 2020-10-30 | 2021-02-12 | 福州大学 | Power transmission line unmanned aerial vehicle inspection image quality evaluation method |
| CN112418009A (en) * | 2020-11-06 | 2021-02-26 | 中保车服科技服务股份有限公司 | Image quality detection method, terminal device and storage medium |
-
2021
- 2021-05-24 CN CN202110566042.1A patent/CN113256583A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120324534A1 (en) * | 2009-11-17 | 2012-12-20 | Holograms Industries | Method and system for automatically checking the authenticity of an identity document |
| US20120026292A1 (en) * | 2010-07-27 | 2012-02-02 | Hon Hai Precision Industry Co., Ltd. | Monitor computer and method for monitoring a specified scene using the same |
| CN105631457A (en) * | 2015-12-17 | 2016-06-01 | 小米科技有限责任公司 | Method and device for selecting picture |
| CN110458790A (en) * | 2018-05-03 | 2019-11-15 | 优酷网络技术（北京）有限公司 | A kind of image detecting method, device and computer storage medium |
| CN110175980A (en) * | 2019-04-11 | 2019-08-27 | 平安科技（深圳）有限公司 | Image definition recognition methods, image definition identification device and terminal device |
| CN110263775A (en) * | 2019-05-29 | 2019-09-20 | 阿里巴巴集团控股有限公司 | Image-recognizing method, device, equipment and authentication method, device, equipment |
| CN110335246A (en) * | 2019-05-29 | 2019-10-15 | 成都数之联科技有限公司 | A kind of license picture clarity evaluation method |
| CN111860489A (en) * | 2019-12-09 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Certificate image correction method, device, equipment and storage medium |
| CN111161251A (en) * | 2019-12-31 | 2020-05-15 | 普联技术有限公司 | Method and device for calculating definition of face image |
| CN112102309A (en) * | 2020-09-27 | 2020-12-18 | 中国建设银行股份有限公司 | Method, device and equipment for determining image quality evaluation result |
| CN112367518A (en) * | 2020-10-30 | 2021-02-12 | 福州大学 | Power transmission line unmanned aerial vehicle inspection image quality evaluation method |
| CN112418009A (en) * | 2020-11-06 | 2021-02-26 | 中保车服科技服务股份有限公司 | Image quality detection method, terminal device and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 徐珩;刘学平;: "基于多对象匹配与融合字符特征的印刷质量检验方法", 包装工程, no. 11, 10 June 2019 (2019-06-10) * |
| 朱利伟;蔡晓东;曾泽兴;梁奔香;: "一种基于视觉注意模型的人脸图像评估算法", 微处理机, no. 06, 31 December 2015 (2015-12-31) * |
| 魏振婷;张仁杰;江磊;: "基于人脸识别与畸变图像校正的监控研究", 电子测量技术, no. 14, 23 July 2020 (2020-07-23) * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023040314A1 (en) * | 2021-09-15 | 2023-03-23 | 上海商汤智能科技有限公司 | Image test method and apparatus, electronic device, and storage medium |
| CN114495103A (en) * | 2022-01-28 | 2022-05-13 | 北京百度网讯科技有限公司 | Text recognition method, text recognition device, electronic equipment and medium |
| CN114511694A (en) * | 2022-01-28 | 2022-05-17 | 北京百度网讯科技有限公司 | Image recognition method, image recognition device, electronic equipment and medium |
| CN114511694B (en) * | 2022-01-28 | 2023-05-12 | 北京百度网讯科技有限公司 | Image recognition method, device, electronic equipment and medium |
| CN114998282A (en) * | 2022-06-16 | 2022-09-02 | 平安科技（深圳）有限公司 | Image detection method, image detection device, electronic equipment and storage medium |
| CN114998282B (en) * | 2022-06-16 | 2024-05-31 | 平安科技（深圳）有限公司 | Image detection method, device, electronic equipment and storage medium |
| CN117058689A (en) * | 2023-10-09 | 2023-11-14 | 巴斯夫一体化基地(广东)有限公司 | Offline detection data processing method for chemical production |
| CN117058689B (en) * | 2023-10-09 | 2024-02-20 | 巴斯夫一体化基地(广东)有限公司 | Offline detection data processing method for chemical production |
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