Picture processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a picture processing method and device, electronic equipment and a storage medium, and relates to the technical field of image processing, in particular to the technical field of computer vision. According to the specific implementation scheme, the to-be-recognized picture is obtained, the picture is recognized, the character representation of the target object displayed by the picture in the set language is obtained, the character representation of the target object in the set language is displayed, the character representation of the target object in the set language is automatically extracted from the picture, and the learning efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, in particular to the field of computer vision technology, and specifically to image processing methods, devices, electronic devices and storage media. Background Art

[0002] With the continuous development of science and technology, in the field of language learning, learners are eager to use various resources around them to learn and integrate learning into their daily lives. People are also increasingly demanding the convenience and efficiency of learning. Summary of the invention

[0003] The present application provides a method, device, electronic device and storage medium for image processing.

[0004] According to one aspect of the present application, a method for processing an image is provided, comprising:

[0005] Get the picture to be identified;

[0006] Recognize the image to obtain a text representation of a target object displayed in the image in a set language;

[0007] The text representation of the target object in the set language is displayed.

[0008] According to another aspect of the present application, there is provided a picture processing device, comprising:

[0009] An acquisition module is used to acquire the image to be identified;

[0010] A recognition module, used to recognize the image and obtain a text representation of a target object shown in the image in a set language;

[0011] The display module is used to display the text representation of the target object in the set language.

[0012] According to another aspect of the present application, there is provided an electronic device, including:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can perform the method described in the above embodiment.

[0016] According to another aspect of the present application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method according to the above embodiment.

[0017] According to another aspect of the present application, a computer program product is provided, including a computer program, wherein the computer program implements the steps of the method described in the above embodiment when executed by a processor.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present application.

[0020] Figure 1 A schematic diagram of a process flow of a picture processing method provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a flow chart of another image processing method provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of a picture processing scenario provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of a flow chart of another image processing method provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of a flow chart of another image processing method provided in an embodiment of the present application;

[0025] Figure 6 A schematic diagram of the structure of an image processing device provided in an embodiment of the present application;

[0026] Figure 7 It is a block diagram of an electronic device used to implement the image processing method of an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0028] In the related art, in the language learning scenario, the traditional word learning method is often boring and single, mainly relying on tools such as books and electronic dictionaries, requiring users to manually query, and lacks close integration with real life scenarios. At the same time, when learners encounter unfamiliar words, the query process is cumbersome and they cannot learn in a timely and effective manner. In addition, the existing learning software has limited ability to recognize and extract words in images, and cannot meet the needs of learners to use life scenarios to learn anytime and anywhere, resulting in low learning efficiency.

[0029] Therefore, in order to address at least one of the above-mentioned problems, the present application proposes an image processing method, device, electronic device and storage medium.

[0030] The following describes the image processing method, device, electronic device and storage medium of the embodiments of the present application with reference to the accompanying drawings.

[0031] Figure 1 A flowchart of a method for processing an image provided in accordance with an embodiment of the present application.

[0032] The image processing method of the embodiment of the present application can be executed by the image processing device of the embodiment of the present application, and the device can be configured in an electronic device to realize the image processing function.

[0033] Among them, the electronic device can be any device with computing capabilities, such as a personal computer, a mobile terminal, a server, etc. The mobile terminal can be, for example, a vehicle-mounted device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and other hardware devices with various operating systems, touch screens and / or display screens.

[0034] It should be noted that in the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are all carried out with the user's consent, and are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0035] like Figure 1 As shown, the method comprises the following steps:

[0036] Step 101, obtaining a picture to be identified.

[0037] In one implementation of the embodiment of the present application, the picture to be identified is a picture taken in real time by the user in an actual scene of daily life. As an example, in the first scenario, when a user is traveling abroad and encounters an unfamiliar road sign, menu or object, he can take a photo at any time through an electronic device, and the photo taken is the picture to be identified. In the second scenario, when a user is reading a foreign book and encounters an unknown word, he can also quickly obtain the picture to be identified by taking a photo. In the third scenario, when a user encounters an unfamiliar object during shopping, he can quickly obtain the picture to be identified by taking a photo.

[0038] In another implementation of the embodiment of the present application, the picture to be identified is a picture stored in the user's local storage, or a picture obtained from the Internet, for example, a picture obtained when the user browses information posted in an application such as Weibo.

[0039] Step 102: recognize the image to obtain a text representation of the target object shown in the image in a set language.

[0040] In the embodiment of the present application, the image is recognized by using image recognition technology to obtain the text representation of the name of the target object in the set language. The image recognition technology is implemented by the following implementation method:

[0041] As an implementation method of an embodiment of the present application, the picture includes text information, each character is an object, the picture is recognized by optical character recognition (OCR) technology, the target object included in the picture is identified, and the target object is converted into a text representation in a set language.

[0042] It should be noted that OCR technology can extract the text representation included in the image from the image in order to obtain the text information included in the image. Furthermore, if the text information in the image is different from the set language set by the user, the target object is converted into a text representation in the set language. As an implementation method, a mapping relationship between languages ​​is obtained, and the text representation in the first language in the image is converted into a text representation in the set language.

[0043] As another implementation method of the embodiment of the present application, if the picture does not include text information, or if the picture includes text information and objects, the target object in the picture can be recognized by a pre-trained neural network model, such as (Recognize Anything Model, RAM), and the text representation of the target object in the set language can be output. Among them, the target object is at least one. There can be multiple objects included in the picture, and the target object is at least one of the multiple objects that the user is concerned about.

[0044] The set language may be set by the user based on demand, including at least one. For example, if the user's native language is Chinese and he wants to learn English, the set language may be English and / or Chinese.

[0045] Step 103: Display the text representation of the target object in the set language.

[0046] Among them, the text representation includes word information.

[0047] In one scenario, the text information includes word information, and the word information includes words.

[0048] In another scenario, the text information includes a word and at least one of a common meaning and a part of speech of the word.

[0049] In the image processing method of the embodiment of the present application, a picture to be identified is obtained, the picture is identified, a text representation of the target object displayed in the picture in a set language is obtained, and the text representation of the target object in the set language is displayed, thereby automatically extracting the text representation of the target object in the set language from the picture, thereby improving learning efficiency.

[0050] Based on the above embodiments, Figure 2 A flowchart of another image processing method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method comprises the following steps:

[0051] Step 201, obtaining a picture to be identified.

[0052] Among them, step 201 can refer to the relevant explanations in the above-mentioned embodiment, and the principle is the same, which will not be repeated here.

[0053] In one scenario of an embodiment of the present application, a user opens learning software, clicks a capture button, and uses an electronic device installed with the learning software to capture a scene image to obtain learning materials in real time.

[0054] As an example, Figure 3 A schematic diagram of a picture processing scenario provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The picture corresponding to A in is the captured picture, that is, the picture to be recognized. When the user wants to use the current scene as learning material, the picture of the current scene is captured.

[0055] Step 202, obtaining the learning range set by the user.

[0056] Among them, the learning scope set by the user can be pre-set by the user based on the needs. As an example, when the user is learning English Level 4 vocabulary, the set learning scope is English Level 4 words; or, when the user is learning English Level 6 vocabulary, the set learning scope is English Level 6 words; in addition, it can also be other learning scopes. The set learning scope is not limited in this embodiment.

[0057] Step 203: recognize the picture to obtain text representations of various objects displayed in the picture in a set language.

[0058] In the embodiment of the present application, the image is recognized to obtain the text representation of all objects displayed in the image in the set language. The specific implementation method can refer to the relevant explanations in the above embodiments, which will not be repeated here.

[0059] In one implementation of an embodiment of the present application, the picture to be identified is a photo of a life scene taken by a user using an electronic device. Before the picture to be identified is identified, preprocessing is performed, including removing noise, adjusting brightness and contrast, etc., to provide clear and high-quality picture input for subsequent word information extraction based on image recognition technology, so as to improve the accuracy of word recognition.

[0060] Step 204: Determine, according to the learning scope, the text representation of the target object matching the learning scope in the set language.

[0061] In the embodiment of the present application, since the user has set the learning scope in advance, in order to improve the purposefulness of learning and improve learning efficiency, only the text representation belonging to the learning scope can be retained according to the learning scope, and the text representation of each identified object in the set language and the text representation of the objects included in the learning scope in the set language can be matched to determine the text representation of the target object matching the learning scope in the set language. By controlling the learning scope, the different learning needs of users can be met, targeted learning can be achieved, and the learning effect and efficiency can be improved.

[0062] As an example, if the user wants to learn English, the set learning scope includes English Level 4 words, the picture is recognized, and the word information in English of the object shown in the picture is obtained, including Level 6 words. In this case, the Level 6 words will be deleted, and only the word information of the Level 4 words will be displayed in the form of a list in the interactive interface of the application.

[0063] Step 205: Display the text representation of the target object in the set language.

[0064] Among them, the relevant explanations in the aforementioned embodiments are also applicable to this embodiment, and the principles are the same, so they will not be repeated here.

[0065] The text representation includes word information. As one implementation, the word information includes the word; as another implementation, the word information includes the word and at least one of the high-frequency interpretation and part of speech of the word.

[0066] As an example, the textual representation includes a word, and at least one of a simple definition and a part of speech of the word. Figure 3 As shown, the picture corresponding to A is identified and the result is Figure 3 The English words and Chinese definitions of the six target objects in the picture corresponding to A, as well as the part of speech represented in English: n, noun, are displayed, such as Figure 3 As shown in , the picture corresponding to B is displayed in the form of a list, including: woodland, n. neighborhood; mountain, n. mountain; snow, n. ice and snow; mist, n. fog; ice cold, n. ice cold; glacier, n. glacier. The meaning of the word can be the most commonly used high-frequency meaning to improve the simplicity of display.

[0067] That is to say, the setting language includes English and Chinese, and by displaying English and Chinese word information, it is convenient for users to understand and remember words when learning English, thereby improving the efficiency of information acquisition.

[0068] Step 206 , in response to detecting a selection operation on the text representation of the first target object in the set language, querying a set vocabulary database to obtain knowledge information associated with the text representation of the first target object in the set language.

[0069] The first target object is any one of the target objects, and is an object selected from the multiple target objects. The vocabulary database stores a large number of text representations and knowledge information associated with the text representations. The text representation includes word information. As an example, the word information includes words, and at least one of the high-frequency interpretations and parts of speech of the words. The knowledge information associated with the text representation includes detailed knowledge information such as detailed interpretations, pronunciations, phrases, and examples of the words.

[0070] It should be noted that the division of word information and knowledge information associated with word information in the embodiments of the present application is only an example. In actual applications, the contents of word information and knowledge information associated with word information can be divided based on needs, and this is not limited in the present embodiment. For example, word information also includes word pronunciation, while knowledge information associated with a word does not include word pronunciation.

[0071] In the embodiment of the present application, since the text representation of the target object in the set language contains less information, in order to provide users with more comprehensive learning resources, more detailed information about the first target object selected by the user can be obtained based on the user's selection operation of the displayed text representation of the target object in the set language, that is, the set vocabulary database is queried according to the text representation of the first target object in the set language to obtain knowledge information associated with the text representation of the first target object in the set language. As an example, the text representation is a word, a high-frequency interpretation of a word, and a part of speech, such as Figure 3 As shown in the picture corresponding to C in , the word selected by the user is woodland. The background color of woodland and the corresponding definition and part of speech are all displayed in gray to indicate that the text representation is the text representation selected by the user. The knowledge information associated with the text representation includes: the pronunciation of the word, the detailed definition of the word and the corresponding part of speech, the word deformation, the corresponding phrase, etc., see Figure 3 As shown in the picture corresponding to C.

[0072] Step 207: displaying knowledge information associated with the text representation of the first target object in the set language.

[0073] In the embodiment of the present application, not only the text representations of multiple target objects in the picture in the set language are displayed, but also based on the needs of the user, the knowledge information associated with the text representation of the first target object selected by the user in the set language can be displayed, thereby combining with the user's actual life scenarios, providing comprehensive learning resources, meeting the different learning needs of users, and improving learning efficiency.

[0074] In the image processing method of the embodiment of the present application, based on the recognition of the image, not only the text representations of multiple target objects in the image in the set language are displayed, but also based on the needs of the user, the knowledge information associated with the text representation of the first target object selected by the user in the set language is also displayed, thereby realizing integration with the user's actual life scenes. No manual input is required by the user, and information can be quickly acquired by taking photos, allowing the user to learn anytime and anywhere in daily life, thereby enhancing the fun and practicality of learning. At the same time, all learning resources can be provided to meet the different learning needs of users and improve learning efficiency.

[0075] Based on the above embodiments, Figure 4 A flowchart of another image processing method provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the method comprises the following steps:

[0076] Step 401, obtaining a picture to be identified.

[0077] Among them, step 401 can refer to the relevant explanations in the above-mentioned embodiments, and the principle is the same, which will not be repeated here.

[0078] Step 402, using image recognition technology to recognize the image, obtain the text representation of the name of the target object in the set language, and obtain the text representation of the associated information of the target object in the set language.

[0079] The text representation of the target object in the set language includes the text representation of the name of the target object in the set language and the text representation of the associated information of the target object in the set language. The name of the target object, i.e., the description information of the target object, is, as an example, a cat and the set language is English, then the text representation obtained is the English word corresponding to cat: cat. The associated information of the target object is information associated with the target object, which can be information about the characteristics of the target object, or can be understood as the derived meaning of the target object. As an example, the target object is a cat and the set language is English. Since cats are usually furry, the text representation of the associated information of the target object in the set language is the English word: fluffy.

[0080] In the embodiment of the present application, the image is recognized by using image recognition technology to obtain the text representation of the name of the target object in the set language, and the image is recognized by using image recognition technology to obtain the text representation of the associated information of the target object in the set language. Among them, the above-mentioned explanations on the text representation of the target object in the set language are also applicable to the text representation of the name of the target object and the associated information of the target object in the set language in this embodiment. The principles are the same and will not be repeated here.

[0081] As an example, the target object is a cat, the set language is English, and cats are usually furry and curly, so not only the text representation of the name of the target object in the set language, that is, the English word: cat, is recognized, but also the text representation of the associated information of the target object in the set language, that is, the English words: fluffy and curly.

[0082] In an embodiment of the present application, by recognizing an image, not only the text representation of the name of the target object in the set language is obtained, but also the text representation of the associated information of the target object in the set language is obtained, thereby achieving the expansion of information for the target object displayed in the picture, increasing the amount of information extracted from the picture, providing richer learning materials, and improving learning effects and efficiency.

[0083] Step 403: Obtain the learning difficulty level corresponding to the text representation of each target object in the set language and the display rules set by the user.

[0084] Step 404: Determine, according to the display rule, display information that matches the learning difficulty level corresponding to the text representation of each target object in the set language.

[0085] Step 405: Display the text representation of each target object in the set language according to at least one of the display font, size and color information included in the display information.

[0086] In an embodiment of the present application, in order to improve the display effect, the text representation of the target object in the set language can be controlled to be displayed based on the display rules set by the user and the learning difficulty level corresponding to the text representation of each target object in the set language. As an implementation method, the learning difficulty level corresponding to the text representation of each target object in the set language can be obtained, and the display information corresponding to different learning difficulty levels is set in the display rules, so that the display information corresponding to the text representation of target objects of various difficulty levels in the set language can be determined, and displayed according to at least one of the display font, size and color information included in the display information. For example, the text representation with a high difficulty level can be displayed in a striking color, a special font and a larger font to distinguish it from other text representations, thereby realizing display based on difficulty level and user preferences, meeting the display needs of different users, improving the display effect, facilitating users to learn in a targeted manner, and improving learning efficiency.

[0087] In the picture processing method of the embodiment of the present application, by recognizing the image, not only the text representation of the name of the target object in the set language is obtained, but also the text representation of the associated information of the target object in the set language is obtained, so that the information expansion of the target object displayed in the picture is realized, richer learning corpus is provided, and the learning effect and learning efficiency are improved. When displaying the text representation of each target object in the set language, it can be displayed based on the difficulty level and user preferences to meet the display needs of different users and improve the learning effect.

[0088] Based on the above embodiments, Figure 5 A flowchart of another image processing method provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the method comprises the following steps:

[0089] Step 501, obtaining a picture to be identified.

[0090] Step 502: Recognize the image to obtain a text representation of the target object shown in the image in a set language.

[0091] Step 503: Display the text representation of the target object in the set language.

[0092] Among them, steps 501 to 503 can refer to the relevant explanations in the aforementioned embodiments, and the principles are the same, so they will not be repeated here.

[0093] Step 504 : in response to detecting a selection operation on the displayed text representation of the second target object in the set language, adding the text representation of the second target object in the set language to the learning vocabulary.

[0094] In the embodiment of the present application, the text representations of interest to the user can be added to the learning word library, and the learning word library can also become a word book for subsequent review. Among them, the learning word library is stored in a setting server, and data synchronization can be achieved between different devices or platforms, which is convenient for users to learn on different devices or platforms.

[0095] As an example, Figure 3 What is displayed in D is the information of 4 words selected by the user to be added to the learning vocabulary. Among them, the 4 word information and the picture can be stored together as a word card, so that the user can combine graphics and text when learning to improve the learning effect.

[0096] Step 505, obtaining the user's historical learning information.

[0097] Among them, historical learning information includes the number of words that can be learned, word frequency, learning progress and other information during the user's historical learning process.

[0098] Step 506: Generate a learning plan for the user based on the historical learning information and the text representations of each target object in the set language stored in the learning vocabulary.

[0099] Step 507: Send learning reminder information to the user according to the learning tasks included in the learning plan.

[0100] In the embodiment of the present application, a personalized learning plan is provided to the user based on the user's historical learning information, and learning reminders are given to help the user better manage the learning process and improve learning effects and efficiency.

[0101] Step 508, recording the user's execution of the learning tasks in the learning plan, and storing the execution status in the setting server.

[0102] In an embodiment of the present application, the user's learning progress and execution status are recorded and stored in a set server. The set server, for example, is a cloud server or a local server, so that data can be synchronized with other learning platforms, making it convenient for users to learn on different devices and improve learning efficiency and experience.

[0103] The image processing method of the embodiment of the present application provides users with personalized learning plans and learning reminders based on the users' historical learning information, thereby helping users to better manage the learning process and improve learning effects and efficiency. The user's learning progress and execution status are recorded and stored in a set server. The set server, such as a cloud server or a local server, can achieve data synchronization with other learning platforms, making it convenient for users to learn on different devices and improve learning efficiency and experience.

[0104] In order to implement the above embodiment, the embodiment of the present application also proposes a picture processing device.

[0105] Figure 6 A schematic diagram of the structure of an image processing device provided in an embodiment of the present application.

[0106] like Figure 6 As shown, the device comprises:

[0107] The acquisition module 61 is used to acquire the image to be identified.

[0108] The recognition module 62 is used to recognize the image and obtain the text representation of the target object shown in the image in a set language.

[0109] The display module 63 is used to display the text representation of the target object in the set language.

[0110] Furthermore, in one implementation of the embodiment of the present application, the identification module 62 is further configured to:

[0111] Using image recognition technology to identify the image, and obtain a text representation of the name of the target object in the set language;

[0112] And / or, the image is recognized by using image recognition technology to obtain a text representation of the associated information of the target object in the set language.

[0113] In one implementation of the embodiment of the present application, the device further includes: a query module;

[0114] The query module is used to query a set vocabulary database in response to detecting a selection operation on the text representation of the first target object in the set language, and obtain knowledge information associated with the text representation of the first target object in the set language.

[0115] The display module 63 is further used to display the knowledge information associated with the text representation of the first target object in the set language.

[0116] In one implementation of the embodiment of the present application, the identification module 62 is further configured to:

[0117] Get the learning scope set by the user;

[0118] Recognize the picture to obtain text representation of each object displayed in the picture in a set language;

[0119] According to the learning scope, a text representation of a target object matching the learning scope in a set language is determined.

[0120] In one implementation of the embodiment of the present application, the device further includes: an adding module;

[0121] The adding module is used to add the text representation of the second target object in the set language to the learning word library in response to detecting a selection operation on the text representation of the second target object in the set language; wherein the learning word library is stored in the setting server.

[0122] In one implementation of the embodiment of the present application, the device further includes: a reminder module;

[0123] The reminder module is used to obtain the user's historical learning information, generate the user's learning plan based on the historical learning information and the text representation of the target object in the set language stored in the learning vocabulary, and send learning reminder information to the user based on the learning tasks included in the learning plan.

[0124] In one implementation of the embodiment of the present application, the device further includes: a storage module;

[0125] The storage module is used to record the user's execution status of the learning tasks in the learning plan and store the execution status in the setting server.

[0126] In one implementation of the embodiment of the present application, the display module 63 is further used for:

[0127] Obtaining the learning difficulty level corresponding to the text representation of each target object in the set language and the display rules set by the user;

[0128] Determining, according to the display rules, display information that matches the learning difficulty level corresponding to the text representation of each target object in the set language;

[0129] The text representation of each target object in the set language is displayed according to at least one of the display font, size and color information included in the display information.

[0130] It should be noted that the explanation of the aforementioned method embodiment is also applicable to the device of this embodiment, so it will not be repeated here.

[0131] In the image processing device of the embodiment of the present application, a picture to be identified is obtained, the picture is identified, a text representation of the target object displayed in the picture in a set language is obtained, and the text representation of the target object in the set language is displayed, thereby automatically extracting the text representation of the target object in the set language from the picture, thereby improving efficiency.

[0132] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0133] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can 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 merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0134] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 702 or a computer program loaded from a storage unit 708 to a RAM (Random Access Memory) 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 via a bus 704. An I / O (Input / Output) interface 705 is also connected to the bus 704.

[0135] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0136] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the image processing method. For example, in some embodiments, the image processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the image processing method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to execute the image processing method in any other appropriate manner (eg, by means of firmware).

[0137] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0139] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A 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, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 can be received in any form (including acoustic input, voice input, or tactile input).

[0141] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0142] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services (Virtual Private Server). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0143] According to an embodiment of the present application, the present application also provides a computer program product, which, when an instruction processor in the computer program product executes, executes the image processing method proposed in the above embodiment of the present application.

[0144] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0145] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for processing an image, characterized in that: include: Get the picture to be identified; Recognize the image to obtain a text representation of a target object displayed in the image in a set language; The text representation of the target object in the set language is displayed.

2. The method of claim 1, wherein: The step of identifying the image to obtain a text representation of a target object displayed in the image in a set language includes: Using image recognition technology to identify the image, and obtain a text representation of the name of the target object in the set language; And / or, the image is recognized by using image recognition technology to obtain a text representation of the associated information of the target object in the set language.

3. The method of claim 1, wherein: After displaying the text representation of the target object in the set language, the method further includes: In response to detecting a selection operation on a text representation of a first target object in a set language, querying a set vocabulary database to obtain knowledge information associated with the text representation of the first target object in the set language; The knowledge information associated with the text representation of the first target object in the set language is displayed.

4. The method of claim 1, wherein: The step of identifying the image to obtain a text representation of a target object displayed in the image in a set language includes: Get the learning scope set by the user; Recognize the image to obtain text representation of each object displayed in the image in a set language; According to the learning scope, a text representation of a target object matching the learning scope in a set language is determined.

5. The method according to any one of claims 1 to 4, wherein: The method further comprises: In response to detecting a selection operation on the displayed text representation of the second target object in the setting language, the text representation of the second target object in the setting language is added to a learning word library; wherein the learning word library is stored in a setting server.

6. The method of claim 5, wherein: After adding the text representation of the second target object in the set language to the learning vocabulary, the method further includes: Get the user's historical learning information; generating a learning plan for the user according to the historical learning information and the text representation of the target object in the set language stored in the learning vocabulary; According to the learning tasks included in the learning plan, learning reminder information is sent to the user.

7. The method according to claim 6, further comprising: Recording the user's performance of the learning tasks in the learning plan; The execution status is stored in the setting server.

8. The method of claim 1, wherein: The displaying of the text representation of the target object in the set language includes: Obtaining the learning difficulty level corresponding to the text representation of each target object in the set language and the display rules set by the user; Determining, according to the display rules, display information that matches the learning difficulty level corresponding to the text representation of each target object in the set language; The text representation of each target object in the set language is displayed according to at least one of the display font, size and color information included in the display information.

9. A picture processing device, characterized in that: include: An acquisition module is used to acquire the image to be identified; A recognition module, used to recognize the image and obtain a text representation of a target object shown in the image in a set language; The display module is used to display the text representation of the target object in the set language.

10. The device of claim 9, wherein: The identification module is further used for: Using image recognition technology to identify the image, and obtain a text representation of the name of the target object in the set language; And / or, the image is recognized by using image recognition technology to obtain a text representation of the associated information of the target object in the set language.

11. The device according to claim 9, further comprising: Query module; The query module is used to query a set vocabulary database in response to detecting a selection operation on the text representation of the first target object in the set language, and obtain knowledge information associated with the text representation of the first target object in the set language; The display module is further used to display knowledge information associated with the text representation of the first target object in a set language.

12. The device of claim 9, wherein: The identification module is further used for: Get the learning scope set by the user; Recognize the image to obtain text representation of each object displayed in the image in a set language; According to the learning scope, a text representation of a target object matching the learning scope in a set language is determined.

13. The device according to any one of claims 9 to 12, wherein: The device further includes: an adding module; The adding module is used to add the text representation of the second target object in the set language to the learning vocabulary in response to detecting a selection operation on the text representation of the displayed second target object in the set language; wherein the learning vocabulary is stored in the setting server.

14. The device of claim 13, further comprising: Reminder module; The reminder module is used to obtain the user's historical learning information, generate the user's learning plan based on the historical learning information and the text representation of the target object in the set language stored in the learning vocabulary, and send learning reminder information to the user based on the learning tasks included in the learning plan.

15. The device of claim 14, further comprising: Storage module; The storage module is used to record the user's execution status of the learning tasks in the learning plan and store the execution status in the setting server.

16. The device of claim 9, wherein: The display module is also used for: Obtaining the learning difficulty level corresponding to the text representation of each target object in the set language and the display rules set by the user; Determining, according to the display rules, display information that matches the learning difficulty level corresponding to the text representation of each target object in the set language; The text representation of each target object in the set language is displayed according to at least one of the display font, size and color information included in the display information.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

19. A computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

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