Information processing method and device, equipment, storage medium and product

Abstract

The invention provides an information processing method and device, equipment, a storage medium and a product, and relates to the technical field of computers. The method comprises the steps that an initial text is extracted from a first picture, and a user to which the first picture belongs is a first user; a second picture is searched, a user to which the second picture belongs is a second user, the second picture comprises a target text, the similarity of the first picture and the second picture meets a first preset similarity requirement, and the target text comprises all or part of text content in the initial text; target prompt information is determined according to the association information of the second user, the target prompt information is used for the first client to establish a preset association relationship with the second client, the first client is the client used by the first user, and the second client is the client used by the second user; and sending the target prompt information to the first client. By adopting the technical scheme, the convenience and efficiency of establishing the preset association relationship can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to information processing methods, apparatus, devices, storage media, and products. Background Technology

[0002] With the rapid development of internet technology, applications are becoming increasingly feature-rich, providing users with more and more diverse interaction methods and experiences.

[0003] Currently, on some internet platforms, users can interact with each other by establishing connections, such as becoming friends or joining the same group.

[0004] However, the current way for users to establish connections with other users usually involves actively entering keywords or other information about the other user or group to search. When the keywords or other information are unavailable or inaccurate, the process of establishing connections becomes inefficient and needs to be improved. Summary of the Invention

[0005] This disclosure provides information processing methods, apparatus, devices, storage media, and products, which can provide an information processing solution to solve the problem of low efficiency in the process of establishing association relationships.

[0006] In a first aspect, embodiments of this disclosure provide an information processing method, including:

[0007] Extract initial text from the first image, where the user to whom the first image belongs is the first user;

[0008] Find a second image, wherein the user to which the second image belongs is a second user, the second image includes target text, and the similarity between the first image and the second image meets a first preset similarity requirement, and the target text includes all or part of the text content in the initial text;

[0009] The target prompt information is determined based on the association information of the second user, wherein the target prompt information is used to enable the first client to establish a preset association relationship with the second client, the first client being the client used by the first user, and the second client being the client used by the second user;

[0010] Send the target prompt information to the first client.

[0011] Secondly, embodiments of this disclosure also provide an information processing apparatus, including:

[0012] An initial text extraction module is used to extract initial text from a first image, wherein the user to which the first image belongs is the first user;

[0013] The search module is used to search for a second image, wherein the user to which the second image belongs is the second user, the second image includes target text, and the similarity between the first image and the second image meets a first preset similarity requirement, and the target text includes all or part of the text content in the initial text;

[0014] The prompt information determination module is used to determine target prompt information based on the association information of the second user, wherein the target prompt information is used to enable the first client to establish a preset association relationship with the second client, the first client being the client used by the first user, and the second client being the client used by the second user;

[0015] The information processing module is used to send the target prompt information to the first client.

[0016] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0017] One or more processors;

[0018] Storage device for storing one or more programs.

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the information processing method provided in the embodiments of this disclosure.

[0020] Fourthly, embodiments of this disclosure also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the information processing method provided in embodiments of this disclosure.

[0021] Fifthly, embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements the information processing method provided in embodiments of this disclosure.

[0022] The information processing scheme provided in this disclosure extracts initial text from a first image belonging to a first user, searches for a second image belonging to a second user, wherein the second image includes all or part of the text content of the initial text, and the similarity between the first image and the second image meets a first preset similarity requirement. Based on the association information of the second user, target prompt information is determined and sent to a first client used by the first user, allowing the first client to establish a preset association relationship with the second client used by the second user. By adopting the above technical solution, the convenience and efficiency of establishing preset association relationships can be improved. Attached Figure Description

[0023] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0024] Figure 1 This is a schematic flowchart of an information processing method provided in an embodiment of the present disclosure;

[0025] Figure 2 This is a schematic flowchart illustrating another information processing method provided in an embodiment of this disclosure;

[0026] Figure 3 A schematic diagram provided for an embodiment of this disclosure;

[0027] Figure 4 This is a schematic diagram illustrating a target prompt information display provided in an embodiment of the present disclosure;

[0028] Figure 5 This is a schematic diagram of the structure of an information processing device provided in an embodiment of the present disclosure;

[0029] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0030] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0031] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0032] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0033] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0034] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0035] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0036] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0037] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0038] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0039] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0040] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0041] Figure 1This is a flowchart illustrating an information processing method provided in an embodiment of the present disclosure. This embodiment is applicable to information processing that establishes relationships between users. The method can be executed by an information processing device, which can be implemented in the form of software and / or hardware. Optionally, it can be implemented by an electronic device, such as a personal computer (PC) or a server. The electronic device can be configured as a server.

[0042] like Figure 1 As shown, the method includes:

[0043] Step 101: Extract initial text from the first image, where the user to which the first image belongs is the first user.

[0044] For example, the first user can be any user who sends an image to the server. The image sent by the first user is denoted as the first image, meaning the user to which the first image belongs is the first user. The first user can send the first image to the server through a first client (the client used by the first user, such as a preset client installed on the terminal device used by the first user). The preset client can be a preset application or a webpage. Electronic devices can be configured as the server of the preset client, such as the Internet platform corresponding to the preset client.

[0045] For example, a preset client can be configured with a function to establish preset relationships by sending images (hereinafter referred to as the relationship establishment function). Preset relationships may include, for example, following relationships, friend relationships, or group member relationships. A first user can trigger an image sending control on the preset page corresponding to the relationship establishment function in the first client. In response to the triggering of the image sending control, a shooting page or an image selection page is displayed in the preset client. The first user can take a photo based on the shooting page and send it as the first image, or select an image (such as an image from the local album) based on the image selection page and send it as the first image. The server can then receive the first image sent by the first client. For example, the first client can respond to the first user's operation by taking photos of objects such as university graduation photos, admission notices, performance tickets, residential community gates, and office buildings to obtain the first image. For example, the first client can also respond to the first user's operation by taking a screenshot of the terminal device's interface to obtain the first image, such as capturing a video frame from a playing video as the first image.

[0046] For example, when the first image contains text, initial text is extracted from the first image. The initial text may include all or part of the text contained in the first image. The specific extraction method is not limited; for example, it can be extracted using an image text recognition algorithm, such as Optical Character Recognition (OCR) technology. Optionally, text with character sizes larger than a preset size threshold is extracted from the first image as initial text to reduce interference from smaller characters in subsequent steps and improve the search efficiency for the second image. For example, if the first user is user a, and the first image is a graduation photo, graduation photos typically contain text, such as "Graduation Commemoration of the Class of 2006, College B, University of A". "Graduation Commemoration of the Class of 2006, College B, University of A" or "Graduation of the Class of 2006, College B, University of A" can be extracted as initial text.

[0047] Step 102: Locate the second image, wherein the user to which the second image belongs is the second user, the second image includes target text, and the similarity between the first image and the second image meets the first preset similarity requirement, and the target text includes all or part of the text content in the initial text.

[0048] For example, the second user is a user who has already sent an image through their client (denoted as the second client, such as a preset client installed on the terminal device used by the second user). The second user is the user to be selected by the first client to establish a preset association relationship, used to prompt the first client used by the first user. If an image sent by a user through their preset client is identified as the second image, then that user can become the found second user. The process of the second user sending an image through the second client is similar to the process of the first user sending an image through the first client, and will not be elaborated here. For example, after any user sends an image through the relationship establishment function of the preset client, the server can extract the text from the sent image and save the extracted text and the sent image in a preset image library, or save the extracted text and the image features of the sent image, for subsequent operations such as determining image similarity with other images. In the preset image library, the image-related content (such as the extracted text, the sent image, or the image features of the sent image) is associated and stored with the user identifier of the user who sent the image, so as to quickly identify the corresponding second user when the second image is found. It should be noted that users were informed and their full authorization was obtained before storing and using the images or image features sent by users.

[0049] In this step, based on the initial text extracted from the first image and the first image itself, an image search is performed in a preset image library. If an image containing the target text and whose similarity to the first image meets a first preset similarity requirement is found, then the found image is designated as the second image. There can be one or more second images. The user who sent the second image is designated as the found second user. There can also be one or more second users. The target text includes all or part of the text content in the initial text; that is, the second image and the first image must have at least one identical character, which is contained in the initial text. Optionally, the proportion of the target text to the initial text must be greater than or equal to a preset proportion threshold, such as 0.5, which can be set according to actual needs. The method for determining the similarity between the first image and the second image is not limited. For example, it can be the similarity of the main object in the image (e.g., the subject of the photograph, where the proportion of the main object's image in the image is greater than a preset value, such as 80%), the similarity of the image content, or the similarity of the target text's position in the image, etc. The first preset similarity requirement can be set according to actual needs, such as the similarity between the first image and the second image being greater than or equal to a first preset similarity threshold.

[0050] As illustrated above, suppose user b sent a picture of a short-sleeved shirt with the words "A University, College B, Class of 2006" printed on it; user c also sent a picture of a graduation photo containing the phrase "A University, College B, Class of 2006 Graduation Commemorative Photo". The initial text extracted from the first picture sent by user a is "A University, College B, Class of 2006 Graduation", and the target text is "A University, College B, Class of 2006". Although the picture sent by user b contains the target text, its subject is different from the first picture, resulting in low similarity and failing to meet the first preset similarity requirement. However, the picture sent by user c not only contains the target text but also shares the same subject as the first picture, thus satisfying the first preset similarity requirement. Therefore, the photo sent by user c can be identified as the second picture, and user c can be identified as the second user.

[0051] Optionally, if no second user or second image is found, a preset prompt message can be returned to the first client to indicate that no matching user or group has been found. Optionally, at the same time or after returning the preset prompt message to the first client, a preset query message can also be returned to the first client, for example, to ask whether to create a corresponding group based on the first image. If a confirmation message is received from the first client, the group is created, and the first user can automatically become the first member of the created group.

[0052] Step 103: Determine target prompt information based on the association information of the second user, wherein the target prompt information is used to enable the first client to establish a preset association relationship with the second client, the first client being the client used by the first user, and the second client being the client used by the second user.

[0053] For example, the association information of the second user may include the user's user ID, the contact information of the second user, and may also include the group ID of the group to which the second user belongs.

[0054] Optionally, the target prompt information is determined based on the association information of the second user, including: determining the target prompt information based on the user identifier of the second user and / or the group identifier of the group to which the second user belongs. This allows the first client to quickly establish friend and / or group relationships with the second client.

[0055] For example, the second user's user identifier may include the second user's username in a preset client, and the groups the second user belongs to may include all or some of the groups the second user belongs to. Optionally, some of the groups the second user belongs to may specifically be groups created or joined by the second user by sending the second image. Optionally, some of the groups the second user belongs to may specifically be groups joined by the second user within the most recent preset time period (e.g., within the last year).

[0056] Step 104: Send the target prompt information to the first client.

[0057] For example, after determining the target prompt information, the target prompt information is sent to the first client, enabling the first client to establish a preset association with the second client based on the target prompt information. For example, the target prompt information is displayed in the first client, and in response to a preset trigger operation by the first user regarding the target prompt information, a preset association between the first client and the second client is established, such as establishing a friend relationship within a preset client. Optionally, in response to a preset trigger operation by the first user regarding the target prompt information, a request message to establish a preset association with the first client is sent to the second client, and after receiving an acceptance request message from the second client, the preset association between the first client and the second client is established.

[0058] Optionally, the target prompt information may include multiple prompts, such as user identifiers corresponding to multiple second users, or group identifiers of one or more groups to which at least one second user belongs. The first user can select to add one or more users as friends or join one or more groups by viewing the user identifiers and group identifiers.

[0059] The information processing method provided in this disclosure extracts initial text from a first image belonging to a first user, searches for a second image belonging to a second user, wherein the second image includes all or part of the text content of the initial text, and the similarity between the first image and the second image meets a first preset similarity requirement. Based on the association information of the second user, target prompt information is determined and sent to a first client used by the first user, allowing the first client to establish a preset association relationship with a second client used by the second user. By adopting the above technical solution, by accurately matching a suitable second client used by a first user to establish a preset association relationship through the text content in the image and the image similarity, the convenience and efficiency of establishing the preset association relationship can be improved.

[0060] In some embodiments, after extracting initial text from the first image, the method further includes: determining first feature information of the first image using a preset feature determination method, wherein the first feature information is determined based on the position information of the target text in the first image; wherein the similarity between the first image and the second image satisfies a first preset similarity requirement, including: the similarity between the first feature information of the first image and the second feature information of the second image satisfies the first preset similarity requirement, and the second feature information is determined based on the second image using the preset feature determination method. Therefore, determining the feature information of an image based on the position information of the target text in the image, and measuring the similarity of images based on the similarity of the image feature information, can improve the calculation efficiency of similarity and further improve the search efficiency of the second image.

[0061] For example, the preset feature determination method can be a pre-defined method for determining the features of an image, and the specific determination method is not limited. The positional information of the target text in the image can be the relative positional information of the characters in the target text in the image, such as coordinate information in the image. The second feature information is determined based on the positional information of the target text in the second image.

[0062] Optionally, the initial text is typically contained within the main object of the image, and the location information of the target text within the image can specifically be the relative position information of the characters in the target text within the image of the main object. This can improve the accuracy of image feature representation.

[0063] For example, taking the first image as an example, the first feature information of the first image is determined by using a preset feature determination method, including: determining the target object to which the initial text belongs in the first image, and extracting the target image of the target object from the first image; and determining the first feature information of the first image based on the relative position information of the characters in the target text in the target image.

[0064] In this process, a subject recognition algorithm can be used to identify at least one subject from the first image, determining the subject containing the initial text as the target object. Edge recognition algorithms can then be used to extract the target image of the target object from the first image. For example, when a user takes a graduation photo, they might include objects such as the tabletop used to hold the photo in the first image. The target object can be determined as the graduation photo based on the extracted initial text, and an image of the graduation photo can be extracted from the first image as the target image for determining image features.

[0065] For example, for the second image, the second feature information can be determined after the second image is sent, and the determined second feature information is stored in a preset image library. Determining the second feature information of the second image using a preset feature determination method includes: determining the target object to which the initial text extracted from the second image belongs in the second image, and cropping the target image of the target object from the second image; determining the second feature information of the second image based on the relative position information of the characters in the target text in the target image.

[0066] Figure 2 This is a flowchart illustrating another information processing method provided in this embodiment. This embodiment optimizes the various optional solutions described above, refining the process for determining image features. Specifically, the method includes the following steps:

[0067] Step 201: Extract the initial text from the first image.

[0068] For example, suppose student AA receives a university admission letter and wants to use the relationship-building function to find new classmates in the same college as them before enrollment. Student AA can take a photo of their admission letter as the first user and send this photo as the first image to the server through the first client. Figure 3 This is a schematic diagram provided for an embodiment of the present disclosure, showing a photograph taken by student aa containing an admission notice.

[0069] Optionally, a text library (denoted as the preset text library) can be pre-built. The preset text library includes preset keywords to be extracted during initial text extraction, such as university, admission notice, college, major, and date. During initial text extraction, the preset keywords and characters whose text distance from the preset keywords is within a preset distance range can be extracted as initial text to avoid excessive redundant text in the image, which could interfere with subsequent image matching. For example, as shown above, the initial text could be extracted as "AA University Admission Notice BB College CC Major 2017".

[0070] Step 202: Determine the target object to which the initial text belongs in the first image, and extract the target image of the target object from the first image.

[0071] For example, after extracting the initial text, algorithms such as subject recognition can be used to identify the target object to which the initial text belongs in the first image, such as... Figure 3 As shown, the admission notice to which the initial text belongs was identified as the target object. After identifying the target object, the image corresponding to the target object was extracted from the first image and used as the target image. Figure 3 As shown, the image of the admission notice is cropped from the first image and used as the target image.

[0072] Step 203: Divide the target image into a preset number of sub-regions and determine the number of each sub-region within the preset number of sub-regions.

[0073] Optionally, if the target image is rotated or deformed in the first image due to the user's shooting perspective, the target image can be preprocessed and adjusted to be a front view of the target object, which can then be used as the preprocessed target image.

[0074] For example, the target image or the preprocessed target image (if preprocessed) is divided into a preset number of sub-regions. The preset number can be pre-set according to actual needs, such as 12 or 18, and can be determined based on the accuracy of image similarity matching; generally, a larger preset number results in higher accuracy. When dividing the sub-regions, an average division method can be used, meaning that each sub-region has an equal area.

[0075] For example, such as Figure 3 As shown, the target image is divided into 12 sub-regions on average, and each sub-region is assigned a corresponding number.

[0076] Step 204: Perform word segmentation on the initial text and determine the target word segment based on the word segmentation results, wherein the target text includes the target word segment.

[0077] For example, a preset word segmentation algorithm can be used to segment the initial text to achieve semantic splitting of the initial text, such as word segmentation methods based on string matching, word segmentation algorithms based on n-gram syntax, word segmentation algorithms based on hidden Markov models, or word segmentation algorithms based on conditional random fields, etc.

[0078] For example, such as Figure 3As shown, the extracted initial text "AA University Admission Notice BB College CC Major 2017" can be divided into multiple initial word segments, such as "AA University", "Admission Notice", "BB College", "CC Major", and "2017". In other words, the word segmentation result can include these multiple initial word segments. The target word is determined based on the word segmentation result. Specifically, this can be done by using all the initial word segments in the result as the target word, or by selecting a subset of the initial word segments as the target word.

[0079] Optionally, determining the target word based on the word segmentation processing result includes: determining multiple candidate words based on the word segmentation processing result; sending the multiple candidate words to the first client so that the first client can display the multiple candidate words; and determining the target word based on the word segmentation confirmation information sent by the first client, wherein the word segmentation confirmation information is determined based on the selection operation received by the first client for the multiple candidate words. This allows users to independently select the target word, improving the accuracy of the prompt information.

[0080] For example, all or part of the initial segmented words from the word segmentation process can be identified as candidate segmented words and sent to the first client. This allows the first client to display multiple candidate segmented words, enabling the first user to view them. The display method for these candidate segmented words is not limited; they can be displayed as a list or on top of the first image. Optionally, the text content in the first image corresponding to the multiple candidate segmented words can be highlighted, for example, by adding selection markers (such as selection boxes). After viewing the multiple candidate segmented words, the first user can trigger a selection operation by clicking or other means. The candidate segmented word that receives the first user's selection operation is then identified as the target segmented word.

[0081] As in the example above, if student AA wants to find new classmates in the same college as them at the same university before enrollment, the first user can enter their selections for "AA University", "Admission Notice", "BB College", and "2017".

[0082] Optionally, determining the target word based on the word segmentation processing result includes: determining multiple candidate word segments based on the word segmentation processing result; filtering and / or combining the multiple candidate word segments to obtain at least one set of target word segments. This allows for automatic determination of target word segments, improving the efficiency of image matching, and also enables the determination of multiple sets of target word segments, enhancing the diversity of prompt information.

[0083] For example, all or part of the initial word segments from the word segmentation process can be identified as candidate word segments. Multiple candidate word segments can be filtered and / or combined to obtain one or more sets of target word segments. If multiple sets of target word segments are obtained, subsequent operations such as associating region labels and determining first feature information can be performed on each set of target word segments. That is, there can be multiple first feature information segments, resulting in multiple target prompts; in other words, each set of target word segments can correspond to one target prompt.

[0084] As exemplified above, by filtering and / or combining multiple candidate word segments, the following target word segments can be obtained: 1) “AA University”; 2) “AA University” and “admission notice”; 3) “AA University”, “admission notice” and “BB College”; 4) “AA University”, “admission notice” and “2017”; 5) “AA University”, “admission notice”, “BB College” and “2017”; 6) “AA University”, “admission notice”, “BB College”, “CC major” and “2017”, etc.

[0085] Step 205: Associate region labels with the target word segmentation, where the region labels are used to indicate the sub-region number where the target word segmentation is located in the target image.

[0086] For example, for each target word, a region label is associated with the current target word based on the number of the sub-region in the target image where the current target word is located. The region label can be represented by the number of the sub-region.

[0087] For example, as in the examples above, the regional label for “AA University” is (2), the regional label for “Admission Notice” is (5), the regional label for “BB College” is (8), the regional label for “CC Major” is (8, 9) and the regional label for “2017” is (12).

[0088] Step 206: Determine the first feature information of the first image based on the target word segmentation and the region label associated with the target word segmentation.

[0089] For example, the target word segment and the region label associated with the target word segment are determined as the first feature information of the first image.

[0090] As exemplified above, assuming the target words are “AA University”, “Admission Notice”, “BB College” and “2017” selected by the user, the first feature information can be “AA University (2) Admission Notice (5) BB College (8) 2017 (12)”.

[0091] Step 207: Locate the second image, wherein the second feature information of the second image includes the target word in the first feature information, and the region label associated with the target word in the second feature information is the same as the region label associated with the target word in the first feature information.

[0092] For example, for each sent image (referred to as a historical image), a preset feature determination method can be used to determine the historical feature information of the historical image, and the historical feature information is associated with the sending user of the historical image and stored in a preset image library. After determining the first feature information of the first image, the preset image library is searched for historical feature information that matches the first feature information to obtain the second feature information. That is, the second feature information is historical feature information that includes the target word in the first feature information and whose associated region label is the same as the region label associated with the target word in the first feature information. Specifically, for each target word, the region label associated with the current target word in the second feature information is the same as the region label associated with the first feature information.

[0093] As illustrated in the example above, suppose student BB once took a photo of their admission notice. BB's admission notice is from AA University's BB College, DD major, in 2017. Since admission notices from the same batch at AA University have the same format, after feature extraction, the positions of each target word in the admission notice image correspond one-to-one with the positions in the admission notice image sent by student aa. Therefore, it can be identified as the second image, and student BB can be found, becoming the second user.

[0094] Step 208: Determine the target prompt information based on the user ID of the second user and / or the group ID of the group to which the second user belongs.

[0095] For example, as mentioned above, the target prompt information can be determined based on bb's user identifier in the preset client, or it can be determined based on the group identifier of the groups that bb has joined in the preset client.

[0096] Step 209: Send the target prompt message to the first client.

[0097] Figure 4 This is a schematic diagram illustrating a target prompt information display provided in an embodiment of this disclosure, such as... Figure 4As shown, multiple target prompts can be displayed on the first client, such as user bb, "AA University BB College 2017 Freshman Group" and "AA University BB College Basketball Group", etc. Student aa, as the first user, can choose to follow or add friends, or join group chats, etc., according to their own needs.

[0098] The information processing method provided in this embodiment extracts initial text from a first image sent by a first client used by a first user, identifies the target object where the initial text is located and captures the corresponding target image, divides the target image into regions, performs word segmentation on the initial text, determines the target word, and uses the number of the sub-region where the target word is located in the target image as the target word association region label. By comparing the region labels, a second image that meets the similarity requirements with the first image can be quickly and accurately matched, thereby determining the second user, and sending corresponding prompt information to the first client according to the user identifier or the group identifier of the second user's group, further improving the convenience and efficiency of establishing preset association relationships.

[0099] In some embodiments, before extracting the initial text from the first image, the method further includes: acquiring the first image; determining whether the first image contains text; if it does, determining to extract the initial text from the first image; if it does not, searching for a third image, wherein the similarity between the first image and the third image satisfies a second preset similarity requirement, and the user to which the third image belongs is the second user. Thus, even when the image sent by the user's client does not contain text, the second user can be determined based on the image similarity, enriching the function of establishing preset associations through image sending, and further improving the convenience and efficiency of establishing preset associations.

[0100] For example, the method for determining the similarity between the first and third images is not limited. It can be at least one of the following: the similarity of the main subject in the images (e.g., the subject of the photograph, whose image occupies a proportion greater than a preset value, such as 80%), the similarity of the image content, and the similarity of the geographical locations associated with the images. The second preset similarity requirement can be set according to actual needs, such as the similarity between the first and third images being greater than or equal to a second preset similarity threshold.

[0101] For example, suppose the first image sent by the first client used by the first user is a picture of building B taken at scenic spot A. If the third image sent by the client used by another user is also a picture of building B taken at scenic spot A, then that user can be identified as the second user. This allows the first client to establish a preset association with the clients used by users who have visited the same scenic spot and taken pictures of the same building as the first user.

[0102] Figure 5 This is a schematic diagram of the structure of an information processing device provided in an embodiment of the present disclosure, as shown below. Figure 5 As shown, the device includes:

[0103] The initial text extraction module 501 is used to extract initial text from the first image, wherein the user to which the first image belongs is the first user;

[0104] The search module 502 is used to search for a second image, wherein the user to which the second image belongs is a second user, the second image includes target text, and the similarity between the first image and the second image meets a first preset similarity requirement, and the target text includes all or part of the text content in the initial text;

[0105] The prompt information determination module 503 is used to determine target prompt information based on the association information of the second user, wherein the target prompt information is used to enable the first client to establish a preset association relationship with the second client, the first client being the client used by the first user, and the second client being the client used by the second user;

[0106] The information processing module 504 is used to send the target prompt information to the first client.

[0107] The information processing apparatus provided in this embodiment of the present disclosure uses the text content in the image and the similarity of the image to accurately match a suitable client used by a second user for a first user to establish a preset association relationship, which can improve the convenience and efficiency of establishing the preset association relationship.

[0108] Optionally, the device may also include:

[0109] The first feature determination module is used to determine the first feature information of the first image by using a preset feature determination method after extracting the initial text from the first image, wherein the first feature information is determined based on the position information of the target text in the first image;

[0110] Wherein, the similarity between the first image and the second image meets the first preset similarity requirement, including: the similarity between the first feature information of the first image and the second feature information of the second image meets the first preset similarity requirement, and the second feature information is determined based on the second image using the preset feature determination method.

[0111] Optionally, the first feature determination module includes:

[0112] The target image cropping unit is used to determine the target object to which the initial text belongs in the first image after extracting the initial text from the first image, and to crop the target image of the target object from the first image;

[0113] The numbering determination unit is used to divide the target image into a preset number of sub-regions and determine the number of each sub-region in the preset number of sub-regions;

[0114] The target word segmentation determination unit is used to perform word segmentation processing on the initial text and determine the target word segmentation based on the word segmentation processing result, wherein the target text includes the target word segmentation;

[0115] A region label association unit is used to associate region labels with the target word segmentation, wherein the region label is used to indicate the number of the sub-region where the target word segmentation is located in the target image;

[0116] The first feature determination unit is used to determine the first feature information of the first image based on the target word segmentation and the region label associated with the target word segmentation.

[0117] Wherein, the second image includes the target text, and the similarity between the first feature information of the first image and the second feature information of the second image meets a first preset similarity requirement, including:

[0118] The second feature information includes the target word in the first feature information, and the region label associated with the target word in the second feature information is the same as the region label associated with the target word in the first feature information.

[0119] Optionally, determining the target word based on the word segmentation processing result includes:

[0120] Multiple candidate word segments are determined based on the word segmentation processing results; the multiple candidate word segments are sent to the first client so that the first client can display the multiple candidate word segments; the target word segment is determined based on the word segmentation confirmation information sent by the first client, wherein the word segmentation confirmation information is determined based on the selection operation for the multiple candidate word segments received by the first client; or,

[0121] Multiple candidate word segments are determined based on the word segmentation results; the multiple candidate word segments are screened and / or combined to obtain at least one set of target word segments.

[0122] Optionally, the device may also include:

[0123] The first image acquisition module is used to acquire the first image before extracting the initial text from the first image;

[0124] The text detection module is used to determine whether the first image contains text.

[0125] The first determining module is used to determine, when the first image contains text, to extract initial text from the first image.

[0126] The user search module is used to search for a third image when the first image does not contain text, wherein the similarity between the first image and the third image meets a second preset similarity requirement, and the user to which the third image belongs is the second user.

[0127] Optionally, the prompt information determination module is used to: determine the target prompt information based on the user identifier of the second user and / or the group identifier of the group to which the second user belongs.

[0128] The information processing apparatus provided in this disclosure can execute the information processing method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects for executing the method.

[0129] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this disclosure.

[0130] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Reference is made below. Figure 6 It illustrates an electronic device suitable for implementing embodiments of the present disclosure (e.g., Figure 6 The diagram below shows the structure of the terminal device or server 600. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0131] like Figure 6As shown, electronic device 600 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of electronic device 600. The processing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. An edit / output (I / O) interface 605 is also connected to bus 604.

[0132] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0133] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of embodiments of this disclosure.

[0134] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0135] The electronic device provided in this embodiment and the information processing method provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0136] This disclosure provides a computer storage medium storing a computer program that, when executed by a processor, implements the information processing method provided in the above embodiments.

[0137] This disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the information processing method provided in the above embodiments.

[0138] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0139] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0140] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0141] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: extract initial text from a first image, wherein the user to which the first image belongs is a first user; search for a second image, wherein the user to which the second image belongs is a second user, the second image includes target text, and the similarity between the first image and the second image satisfies a first preset similarity requirement, the target text including all or part of the text content in the initial text; determine target prompt information based on the association information of the second user, wherein the target prompt information is used to enable a first client to establish a preset association relationship with a second client, the first client being a client used by the first user, and the second client being a client used by the second user; and send the target prompt information to the first client.

[0142] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0144] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a module does not necessarily limit the module itself; for example, the initial text extraction module can also be described as "a module that extracts initial text from a first image".

[0145] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0146] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0147] According to one or more embodiments of this disclosure, an information processing method is provided, comprising:

[0148] Extract initial text from the first image, where the user to whom the first image belongs is the first user;

[0149] Find a second image, wherein the user to which the second image belongs is a second user, the second image includes target text, and the similarity between the first image and the second image meets a first preset similarity requirement, and the target text includes all or part of the text content in the initial text;

[0150] The target prompt information is determined based on the association information of the second user, wherein the target prompt information is used to enable the first client to establish a preset association relationship with the second client, the first client being the client used by the first user, and the second client being the client used by the second user;

[0151] Send the target prompt information to the first client.

[0152] According to one or more embodiments of this disclosure, after extracting the initial text from the first image, the method further includes:

[0153] A first feature information of the first image is determined using a preset feature determination method, wherein the first feature information is determined based on the position information of the target text in the first image;

[0154] Wherein, the similarity between the first image and the second image meets the first preset similarity requirement, including: the similarity between the first feature information of the first image and the second feature information of the second image meets the first preset similarity requirement, and the second feature information is determined based on the second image using the preset feature determination method.

[0155] According to one or more embodiments of this disclosure, a first feature information of the first image is determined using a preset feature determination method, including:

[0156] Determine the target object to which the initial text belongs in the first image, and extract the target image of the target object from the first image;

[0157] The target image is divided into a preset number of sub-regions, and the number of each sub-region within the preset number of sub-regions is determined;

[0158] The initial text is segmented into words, and the target words are determined based on the segmentation results, wherein the target text includes the target words.

[0159] Associating region labels with the target word segmentation, wherein the region labels are used to indicate the sub-region number where the target word segmentation is located in the target image;

[0160] The first feature information of the first image is determined based on the target word segmentation and the region label associated with the target word segmentation.

[0161] Wherein, the second image includes the target text, and the similarity between the first feature information of the first image and the second feature information of the second image meets a first preset similarity requirement, including:

[0162] The second feature information includes the target word in the first feature information, and the region label associated with the target word in the second feature information is the same as the region label associated with the target word in the first feature information.

[0163] According to one or more embodiments of this disclosure, determining the target word based on the word segmentation processing result includes:

[0164] Multiple candidate word segments are determined based on the word segmentation processing results; the multiple candidate word segments are sent to the first client so that the first client can display the multiple candidate word segments; the target word segment is determined based on the word segmentation confirmation information sent by the first client, wherein the word segmentation confirmation information is determined based on the selection operation for the multiple candidate word segments received by the first client; or,

[0165] Multiple candidate word segments are determined based on the word segmentation results; the multiple candidate word segments are screened and / or combined to obtain at least one set of target word segments.

[0166] According to one or more embodiments of this disclosure,

[0167] Before extracting the initial text from the first image, the process also includes:

[0168] Get the first image;

[0169] Determine whether the first image contains text;

[0170] If it is contained, then determine to extract the initial text from the first image;

[0171] If not found, a third image is searched, wherein the similarity between the first image and the third image meets the second preset similarity requirement, and the user to which the third image belongs is the second user.

[0172] According to one or more embodiments of this disclosure, determining the target prompt information based on the association information of the second user includes:

[0173] The target prompt information is determined based on the user ID of the second user and / or the group ID of the group to which the second user belongs.

[0174] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0175] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0176] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. An information processing method, characterized in that, include: Extract initial text from the first image, where the user to whom the first image belongs is the first user; Find a second image, wherein the user to which the second image belongs is a second user, the second image includes target text, and the similarity between the first image and the second image meets a first preset similarity requirement, and the target text includes all or part of the text content in the initial text; The target prompt information is determined based on the association information of the second user, wherein the target prompt information is used to enable the first client to establish a preset association relationship with the second client, the first client being the client used by the first user, and the second client being the client used by the second user; Send the target prompt information to the first client.

2. The method according to claim 1, characterized in that, After extracting the initial text from the first image, the process also includes: A first feature information of the first image is determined using a preset feature determination method, wherein the first feature information is determined based on the position information of the target text in the first image; Wherein, the similarity between the first image and the second image meets the first preset similarity requirement, including: the similarity between the first feature information of the first image and the second feature information of the second image meets the first preset similarity requirement, and the second feature information is determined based on the second image using the preset feature determination method.

3. The method according to claim 2, characterized in that, The first feature information of the first image is determined using a preset feature determination method, including: Determine the target object to which the initial text belongs in the first image, and extract the target image of the target object from the first image; The target image is divided into a preset number of sub-regions, and the number of each sub-region within the preset number of sub-regions is determined; The initial text is segmented into words, and the target words are determined based on the segmentation results, wherein the target text includes the target words. Associating region labels with the target word segmentation, wherein the region labels are used to indicate the sub-region number where the target word segmentation is located in the target image; The first feature information of the first image is determined based on the target word segmentation and the region label associated with the target word segmentation. Wherein, the second image includes the target text, and the similarity between the first feature information of the first image and the second feature information of the second image meets a first preset similarity requirement, including: The second feature information includes the target word in the first feature information, and the region label associated with the target word in the second feature information is the same as the region label associated with the target word in the first feature information.

4. The method according to claim 3, characterized in that, The step of determining the target word based on the word segmentation processing result includes: Multiple candidate word segments are determined based on the word segmentation processing results; the multiple candidate word segments are sent to the first client so that the first client can display the multiple candidate word segments; the target word segment is determined based on the word segmentation confirmation information sent by the first client, wherein the word segmentation confirmation information is determined based on the selection operation for the multiple candidate word segments received by the first client; or, Multiple candidate word segments are determined based on the word segmentation results; the multiple candidate word segments are screened and / or combined to obtain at least one set of target word segments.

5. The method according to claim 1, characterized in that, Before extracting the initial text from the first image, the process also includes: Get the first image; Determine whether the first image contains text; If it is contained, then determine to extract the initial text from the first image; If not found, a third image is searched, wherein the similarity between the first image and the third image meets the second preset similarity requirement, and the user to which the third image belongs is the second user.

6. The method according to any one of claims 1-5, characterized in that, The step of determining the target prompt information based on the association information of the second user includes: The target prompt information is determined based on the user ID of the second user and / or the group ID of the group to which the second user belongs.

7. An information processing device, characterized in that, include: An initial text extraction module is used to extract initial text from a first image, wherein the user to which the first image belongs is the first user; The search module is used to search for a second image, wherein the user to which the second image belongs is the second user, the second image includes target text, and the similarity between the first image and the second image meets a first preset similarity requirement, and the target text includes all or part of the text content in the initial text; The prompt information determination module is used to determine target prompt information based on the association information of the second user, wherein the target prompt information is used to enable the first client to establish a preset association relationship with the second client, the first client being the client used by the first user, and the second client being the client used by the second user; The information processing module is used to send the target prompt information to the first client.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the information processing method as described in any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the information processing method as described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the information processing method as described in any one of claims 1-6.

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