Method and system for identifying chat record message based on OCR (Optical Character Recognition)

Abstract

The invention provides a method and system for recognizing chat record messages based on OCR, and the method comprises the steps: capturing newly received message contents in a chat software interface in real time or at intervals, and recognizing the message contents through an OCR technology to obtain complete text contents in the message contents; dividing vocabularies in the text content and extracting key information, and determining the type of the text content based on the divided vocabularies and the key information; filtering the text content belonging to the user-defined filtering type, and filtering the text content containing the user-defined filtering keyword; judging the quantity of the residual text contents, and if the quantity of the text contents is greater than 1, performing duplicate removal processing on the text contents; performing grammar and semantic calibration on the remaining text content; and outputting the calibrated text content to a terminal device for prompting. Compared with traditional message popup prompt, the method can prompt complete character information in message content, and through analysis, filtering, duplicate removal and calibration operations, the quality of the information can be improved, and invalid information can be removed.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method and system for identifying chat record messages based on OCR. Background Art

[0002] With the continuous development of communication technology and the continuous iteration of smart terminal devices, communication between people has become more and more convenient, which is reflected in the overall transformation from communicating through text letters to communicating through chat software. Using chat software, you can easily send text, voice, pictures or make voice and video calls to others. Chat software communication has become an indispensable part of people's lives. However, with the continuous development of chat software functions, its functional innovation and humanized optimization have gradually reached bottlenecks, and there are actually many problems that need to be optimized and solved.

[0003] For example, the traditional message prompt method of chat software is generally based on its own message notification function, and the message summary is displayed in a pop-up window. However, this prompt method has some disadvantages. For example, users cannot directly obtain the complete content of the message. They need to open the chat software to view the complete message details. There are many operation processes and it is inconvenient to read. For example, the existing pop-up prompt method cannot filter out invalid information such as spam and advertising information in the message, and cannot flexibly filter information according to user needs. In addition, the existing pop-up prompt method cannot automatically filter out duplicate messages in the message. Repeated pop-up prompts of duplicate messages will greatly affect the user's use of the device, resulting in a poor user experience.

[0004] Based on this, the present invention intends to propose a solution to solve at least one of the above problems. Summary of the invention

[0005] In order to overcome the defects of the above-mentioned prior art, the present invention provides a method and system for identifying chat record messages based on OCR. The method of the present invention does not rely on the message notification function of the chat software itself and can be applied to different chat software. The present method realizes the complete acquisition and complete prompt of the chat message, which is convenient for users to read. In addition, the present method effectively improves the quality of the information by performing a series of processing operations on the message, and the user can customize certain processing operations, which has good flexibility and adaptability, improves the user experience, and has significant technical advantages and application value.

[0006] The specific technical solutions are as follows:

[0007] A first aspect of the present invention provides a method for identifying chat record messages based on OCR, the method comprising:

[0008] Capturing the content of newly received messages in the chat software interface in real time or at intervals, and recognizing the content of the message by using OCR recognition technology to obtain the complete text content of the message;

[0009] Analyzing the text content, dividing the words in the text content and extracting key information in the text content, and determining the type of the text content based on the divided words and key information;

[0010] Filtering the text content, filtering the text content belonging to the custom filtering type, and filtering the text content containing the custom filtering keyword;

[0011] Determining the amount of remaining text content, and in response to the amount of remaining text content being more than one, performing deduplication processing on the text content;

[0012] Perform grammatical and semantic calibration on the remaining text content;

[0013] The calibrated text content is output to the terminal device where the chat software is located and / or an external terminal device for prompting.

[0014] In a specific embodiment, capturing the message content specifically includes: opening a new session in the chat software interface, and taking a screenshot of an area of ​​the new session; the message content is included in the captured picture;

[0015] The “identifying the message content by using OCR recognition technology” includes: identifying the captured image by using OCR recognition technology.

[0016] In a specific embodiment, the message content includes voice message content; the method further includes: before identifying the message content, converting the voice message content into a text message content and displaying it in the area of ​​the new conversation.

[0017] In a specific embodiment, the “obtaining the complete text content in the message content” further includes: determining whether the text content has been cached, and if not, caching the text content;

[0018] The “analyzing the text content” includes: analyzing the text content that is determined to be not cached; and deleting the cached text content after a preset time.

[0019] In a specific embodiment, the method further comprises:

[0020] In the case of capturing the message content at intervals, the updating frequency of the message content in the chat software interface is calculated so that the capturing time interval matches the updating frequency.

[0021] In a specific embodiment, the “extracting key information from the text content” includes: performing part-of-speech tagging and named entity recognition on the divided words, thereby extracting the key information;

[0022] The “outputting the calibrated text content to the terminal device where the chat software is located and / or the external terminal device” includes: highlighting the key information in the text content and outputting it to the terminal device where the chat software is located and / or the external terminal device.

[0023] In a specific embodiment, the “filtering the text content” further includes:

[0024] Invalid content in the text content is identified and filtered through a preset machine learning model.

[0025] In a specific embodiment, the “de-duplication processing of text content” includes:

[0026] Calculate the similarity between different text contents, and retain one of the multiple text contents whose similarity is higher than a preset threshold; and / or calculate the similarity between different text contents from the same message source, and retain one of the multiple text contents whose similarity is higher than a preset threshold.

[0027] In a specific embodiment, the “outputting the calibrated text content to the terminal device where the chat software is located and / or the external terminal device” includes: sending the text content to the application on the terminal device where the chat software is located, and / or sending the text content to the application on the external terminal device;

[0028] The “providing a prompt” includes: the application program displays the text content on the screen of the terminal device; and / or the application program converts the text content into voice and broadcasts it through the terminal device.

[0029] A second aspect of the present invention provides a system for identifying chat record messages based on OCR, comprising:

[0030] The OCR recognition module is used to capture the content of newly received messages in the chat software interface in real time or at intervals, and recognize the content of the message through OCR recognition technology to obtain the complete text content of the message content;

[0031] An analysis module, used to analyze the text content, divide the words in the text content and extract key information in the text content, and determine the type of the text content based on the divided words and key information;

[0032] A filtering module, used to filter the text content, filter the text content belonging to the custom filtering type, and filter the text content containing the custom filtering keywords;

[0033] a deduplication module, configured to determine the number of remaining text contents, and in response to the number of text contents being at least two, perform deduplication processing on the text contents;

[0034] A calibration module, used for grammatical calibration and semantic calibration of the remaining text content;

[0035] The output module is used to output the calibrated text content to the terminal device where the chat software is located and / or an external terminal device for prompting.

[0036] The present invention has at least the following beneficial effects:

[0037] The present invention provides a method and system for identifying chat record messages based on OCR. Compared with the traditional pop-up message prompt method, the method provided by the present invention does not rely on the message notification function of the chat software itself, so it can be applied to different chat software and has good applicability. Based on OCR recognition technology, the method can obtain the complete text content in the message content, and the user can read the complete information without opening the chat software, which reduces the operating cost and improves the message reading experience. In addition, the method can exclude junk messages and invalid information in the text content by analyzing, filtering and deduplicating the text content, so that the text content finally output to the user terminal device has higher quality, and the processing operation of the text content can be customized according to user needs, with good flexibility. And through the calibration operation of the text content, the output text content also has better accuracy, further improving the user experience. In practical applications, the method can integrate and prompt messages received by different chat software, making it more convenient for users to read messages. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A schematic diagram of the steps of the method provided in Example 1;

[0040] Figure 2 A simplified schematic diagram of the steps of the method provided in Example 1;

[0041] Figure 3This is a schematic diagram of the structure of the system provided in Example 2.

[0042] Reference numerals:

[0043] 1-OCR recognition module; 2-analysis module; 3-filtering module; 4-deduplication module; 5-calibration module; 6-output module. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] OCR (Optical Character Recognition) technology is a technology that can convert text in an image into machine-encoded text. OCR technology is widely used in many fields such as document digitization and automated data entry, but in the application scenario of chat message prompts, there is currently no technical application that uses OCR technology to recognize chat record messages and finally provide message prompts. Based on this, the present invention intends to provide a method and system for recognizing chat record messages based on OCR, which recognizes the message content in the chat software through OCR recognition technology, obtains the text content from the message content, and finally outputs it to the user's terminal device after analysis, filtering, deduplication, calibration and other operations, so that the user can obtain complete message information, and the content of the message information has higher quality after processing.

[0046] Example 1

[0047] Please refer to Figure 1 and Figure 2 This embodiment provides a method for identifying chat record messages based on OCR, and the method specifically includes:

[0048] S1: Capturing the newly received message content in the chat software interface in real time or at intervals, and recognizing the message content through OCR recognition technology to obtain the complete text content in the message content.

[0049] It should be noted that the message content refers to a chat record message sent by a message source in the chat software, such as "Hello" sent by Zhang San. Therefore, the number of message contents captured in this step may be zero, one or more, and the text content finally recognized may also be zero, one or more. When it is zero, it means that there is no newly received message content in the chat software for the time being. The newly received message content refers to the message content in the chat software that has not been read by the user and has not been captured. The text content is obtained by recognizing the message content, and each text content contains the complete text information in a message content.

[0050] Exemplarily, the chat software includes but is not limited to WeChat, QQ, Weibo, etc.

[0051] In this embodiment, capturing the message content may specifically include: opening a new session in the chat software interface, and taking a screenshot of the area of ​​the new session. By taking a screenshot, a picture containing the message content can be obtained. It should be noted that the new session refers to a session that has not been opened and read by the user. A new session is generally prompted by an identifier in the chat software interface, such as a dot, which indicates that the session has not been opened and read by the user. Exemplarily, the method can determine whether the session is a new session by identifying whether the session bar contains the identifier.

[0052] The “identifying the message content by using OCR recognition technology” includes: identifying the captured image by using OCR recognition technology.

[0053] In other embodiments, the message content in the chat software interface may be captured in real time through real-time screen capture technology, or the message content may be directly obtained through the API interface of the chat software.

[0054] In this embodiment, the message content includes text message content and voice message content. Step S1 of this method also includes: before identifying the message content, converting the voice message content into a text message content, that is, the text message content, and displaying it in the area of ​​the new conversation. In this way, the voice message content can also be captured in the form of a screenshot. In other embodiments, the message content may also include picture message content, and this method may directly identify the picture message content through OCR recognition technology to obtain the text information contained in the picture message content.

[0055] Furthermore, the step S1 of the method of "obtaining the complete text content in the message content" may also include: determining whether the text content has been cached, and if not, caching the text content. The step S2 of "analyzing the text content" includes: analyzing the text content that is determined to be not cached. The cached text content is deleted after a preset time.

[0056] Understandably, this method caches the text content so that when the same text content is recognized next time, it can be determined that it has been recognized, and thus subsequent analysis and output operations are not performed. On the one hand, it reduces the consumption of computing power, and on the other hand, it also avoids repeatedly prompting messages that have been prompted before, which affects the user experience. Specifically, determining whether the text content has been cached may specifically include: comparing the text content obtained by OCR recognition technology with all the text content in the cache. If there is the same text content, it means it has been cached; otherwise, it has not been cached.

[0057] Exemplarily, the preset time can be set to 3 days. The reason is that under normal circumstances of continuously receiving new message content, after 3 days, the historical message content generally will no longer be displayed in the session area, that is, the historical message content will not be captured when taking a screenshot, so that the text content that has been recognized will not be recognized again. Therefore, the text content can be deleted from the cache. The preset time can be flexibly adjusted according to the actual situation and will not be specifically limited here.

[0058] Furthermore, step S1 of this method may further include: in the case of capturing message content at intervals, calculating the update frequency of the message content in the chat software interface so that the captured time interval matches the update frequency. The higher the update frequency, the shorter the captured time interval; the lower the update frequency, the longer the captured time interval. Exemplarily, if it is detected that the update frequency of the message content in the chat software is one message per 10 seconds, the captured time interval can be set to 5 seconds, for example.

[0059] S2: Analyze the text content, divide the vocabulary in the text content, and extract the key information in the text content. Determine the type of the text content based on the divided vocabulary and key information.

[0060] Specifically, before dividing the vocabulary, preprocessing operations can first be performed on the text content. Preprocessing is the basis for subsequent vocabulary division, which helps reduce noise and improve the accuracy of subsequent word segmentation. The preprocessing operations include but are not limited to removing irrelevant characters such as punctuation marks and special symbols, as well as removing high-frequency but meaningless words and phrases such as "de" (的) and "le" (了). After preprocessing, the text content can be divided into vocabulary using the word segmentation algorithm in natural language processing (NLP) technology.

[0061] In this embodiment, "extracting the key information in the text content" in step S2 may include: performing part-of-speech tagging and named entity recognition on the divided vocabulary to extract the key information.

[0062] Part-of-speech tagging is the process of identifying the part-of-speech of each word in the text content. Part-of-speech information is crucial for understanding the grammatical structure and semantics of a sentence, and is conducive to the subsequent extraction of key information. Exemplarily, part-of-speech tagging can be performed using a pre-trained part-of-speech tagging model. The part-of-speech tagging model can use machine learning or deep learning algorithms to learn the mapping relationship between vocabulary and part-of-speech by analyzing a large amount of annotated text data.

[0063] Named entity recognition is to identify entities with specific meanings from text, such as names of people, places, time, organization names, etc. These entities carry key information in the text content. Named entity recognition can also be achieved through pre-trained models, so I will not go into details here.

[0064] In this embodiment, the "determining the type of text content based on the divided vocabulary and key information" described in step S2 can specifically be: based on the word segmentation results and the key information extraction results, combined with predefined rules or templates, check whether the text content contains specific vocabulary, sentence structure or key information set by the rules or templates, so as to determine the type of text content. For example, text content containing words such as "news" and "report" is likely to belong to news report type text, while text content containing words such as "discount" and "purchase" is likely to belong to sales advertisement type text. Based on this, the text content can be classified for the processing operations in subsequent steps. The types of text content include but are not limited to notifications, chats, advertisements, and news.

[0065] In other embodiments, it may also be based on machine learning methods, such as naive Bayes, support vector machine, decision tree, etc., or based on deep learning methods, such as convolutional neural network, recurrent neural network, etc. The model is trained by these methods to learn the features and patterns of different types of texts, thereby realizing the classification of text content.

[0066] S3: Filter the text content, filter the text content belonging to the custom filtering type, and filter the text content containing the custom filtering keywords. It can be understood that this step is performed on the basis of segmenting and classifying the text content. This step filters the text content containing the user-defined keywords and the text content of the user-defined type, so that the text content finally output is what the user needs. For example, the user can filter the text content of the advertisement type through customization, so that the advertisement type message will not be prompted in the end.

[0067] Since both the filter type and filter keywords can be customized by the user, it can meet the different filtering needs of users and has good flexibility and applicability.

[0068] In this embodiment, filtering the text content may also include: identifying and filtering invalid content in the text content through a preset machine learning model.

[0069] S4: Determine the number of remaining text contents, and in response to the number of remaining text contents being more than one, perform deduplication processing on the text contents. It can be understood that deduplication processing can avoid multiple prompts of similar or repeated messages, which affects the user experience.

[0070] In this embodiment, a counter can be set to count the number of captured initial message contents, which is the number of text contents. After filtering the text contents, the number of text contents may decrease, and when it decreases, the counter subtracts the corresponding value, so the number of remaining text contents can be obtained by observing the counter.

[0071] The "deduplicated processing of text content" described in step S4 may include: calculating the similarity between different text contents, retaining one of the multiple text contents with a similarity higher than a preset threshold, or calculating the similarity between different text contents from the same message source, retaining one of the multiple text contents with a similarity higher than a preset threshold. It can be understood that the former method is to calculate the similarity of all text contents, while the latter method is to calculate the similarity between different text contents from the same message source.

[0072] Specifically, the similarity between different text contents can be calculated by methods other than but not limited to the cosine similarity algorithm, the Jaccard similarity algorithm, and the text similarity algorithm based on deep learning. Exemplarily, taking the cosine similarity algorithm as an example, in the similarity calculation of text content, the text content is first converted into a vector representation (such as a TF-IDF vector), and then the cosine value between the vectors is calculated. The cosine similarity algorithm evaluates the similarity of two vectors by calculating the cosine value of the angle between them. The closer the cosine value is to 1, the more similar the text is. Therefore, the preset threshold value can be set to 0.95, 0.9, etc., for example.

[0073] S5: Perform grammar calibration and semantic calibration on the remaining text content. It can be understood that by performing grammar calibration and semantic calibration on the text content, errors in the text can be corrected, the quality of the text content can be improved, and preparations can be made for subsequent output.

[0074] Specifically, in terms of grammar calibration, error detection and correction technology can be used to check whether the vocabulary divided in the previous steps conforms to the rules in the dictionary, and replace and correct the incorrect vocabulary. If it is English characters, it is also necessary to determine whether there are spelling errors in individual words. In addition, it is necessary to check whether the overall structure of the sentence conforms to grammatical rules, such as whether there are punctuation errors and grammatical errors in the sentence. In terms of semantic calibration, semantic analysis technology can be used to analyze the entities identified in the previous steps and the key information extracted, determine the relationship between entities and construct an association network between entities, so as to understand and calibrate the semantics of the text content.

[0075] S6: Output the calibrated text content to the terminal device where the chat software is located and / or the external terminal device for prompting. It can be understood that the calibrated text content can be output to the terminal device where the chat software is located, or to other external terminal devices, while the traditional prompt method only prompts in the local terminal, so this method has better flexibility and applicability. Exemplarily, the terminal device includes but is not limited to mobile phones, tablets, bracelets, computers and other devices.

[0076] In this embodiment, the step S6 of "outputting the calibrated text content to the terminal device where the chat software is located and / or the external terminal device" may include: sending the text content to the application on the terminal device where the chat software is located, and / or sending the text content to the application on the external terminal device. Specifically, the sending method may be sending via the Internet or sending via near field communication technology, which is not specifically limited here.

[0077] Furthermore, the "prompting" described in step S6 may include: the application displays the text content on the screen of the terminal device, and / or the application converts the text content into voice and broadcasts it through the terminal device. Exemplarily, the application can display the text content on the screen of the terminal device in the form of a text window, and the application can be customized by the user. For example, the user can select the number and type of chat software in the application, so that the text content recognized from the message content of different chat software is sent to the application and integrated by the application, and finally the text content of different chat software is all displayed in the application, which is convenient for the user to monitor and read each chat software.

[0078] Furthermore, the step S6 of "outputting the calibrated text content to the terminal device where the chat software is located and / or the external terminal device" may also include: highlighting key information in the text content and outputting it to the terminal device where the chat software is located and / or the external terminal device. Exemplarily, the highlighting may be, for example, the font size, font color, etc. of the key information. Highlighting the key information can help the user understand the message content faster and better.

[0079] Example 2

[0080] Please refer to Figure 3 This embodiment provides a system for identifying chat record messages based on OCR, including:

[0081] The OCR recognition module 1 is used to capture the newly received message content in the chat software interface in real time or at intervals, and recognize the message content through OCR recognition technology to obtain the complete text content in the message content.

[0082] The analysis module 2 is used to analyze the text content, divide the words in the text content and extract the key information in the text content, and determine the type of the text content based on the divided words and key information.

[0083] The filtering module 3 is used to filter the text content, filter the text content belonging to the custom filtering type, and filter the text content containing the custom filtering keyword.

[0084] The deduplication module 4 is used to determine the number of remaining text contents, and in response to the number of text contents being at least two, perform deduplication processing on the text contents.

[0085] The calibration module 5 is used to perform grammar calibration and semantic calibration on the remaining text content.

[0086] The output module 6 is used to output the calibrated text content to the terminal device where the chat software is located and / or an external terminal device for prompting.

[0087] In this embodiment, the OCR recognition module 1 can open a new session in the chat software interface and take a screenshot of the area of ​​the new session, thereby capturing the message content by recognizing the captured image through the OCR recognition technology. The message content includes voice message content, and the OCR recognition module 1 is also used to convert the voice message content into text format before recognizing the message content, and display it in the area of ​​the new session, so that the voice message content can also be captured in the form of a screenshot.

[0088] Furthermore, the OCR recognition module 1 is also used to determine whether the text content has been cached, and if not, the text content is cached. The cached text content is deleted after a preset time. The analysis module 2 is used to analyze the text content that is determined to be not cached.

[0089] Furthermore, when the OCR recognition module 1 captures the message content at intervals, the OCR recognition module 1 also calculates the update frequency of the message content in the chat software interface and matches the capture time interval with the update frequency. The higher the update frequency, the shorter the capture time interval, and the lower the update frequency, the longer the capture time interval.

[0090] In this embodiment, the analysis module 2 can extract key information from the text content by performing part-of-speech tagging and named entity recognition on the divided words. When outputting the text content, the output module 6 can highlight the key information in the text content so that the user can understand the message content faster and better.

[0091] In this embodiment, the filtering module 3 may include a preset machine learning model. In addition to custom type and custom keyword filtering, the filtering module 3 may also identify and filter invalid content in the text content through the preset machine learning model.

[0092] Furthermore, when performing deduplication processing on text content, the deduplication module 4 is specifically used to: calculate the similarity between different text contents, and retain one of the multiple text contents whose similarity is higher than a preset threshold. Alternatively, it is used to: calculate the similarity between different text contents from the same message source, and retain one of the multiple text contents whose similarity is higher than a preset threshold.

[0093] Furthermore, the output module 6 can directly output the text content to the terminal device where the chat software is located and / or an external terminal device, or it can convert the text content into voice and output it to the terminal device, so that the text content can be displayed on the screen of the terminal device, or the text content can be broadcast by the terminal device in the form of voice.

[0094] In summary, the present invention provides a method and system for identifying chat record messages based on OCR. Compared with the traditional pop-up message prompt method, the method provided by the present invention does not rely on the message notification function of the chat software itself, so it can be applied to different chat software and has good applicability. Based on OCR recognition technology, the method can obtain the complete text content in the message content, and the user can read the complete information without opening the chat software, which reduces the operating cost and improves the message reading experience. In addition, the method can exclude junk messages and invalid information in the text content by analyzing, filtering and deduplicating the text content, so that the text content finally output to the user terminal device has higher quality, and the processing operation of the text content can be customized according to user needs, with good flexibility. And through the calibration operation of the text content, the output text content also has better accuracy, further improving the user experience. In practical applications, the method can integrate and prompt messages received by different chat software, making it more convenient for users to read messages.

[0095] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0096] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying chat record messages based on OCR, characterized in that: The method comprises: Capturing the content of newly received messages in the chat software interface in real time or at intervals, and recognizing the content of the message by using OCR recognition technology to obtain the complete text content of the message; Analyzing the text content, dividing the words in the text content and extracting key information in the text content, and determining the type of the text content based on the divided words and key information; Filtering the text content, filtering the text content belonging to the custom filtering type, and filtering the text content containing the custom filtering keyword; Determining the amount of remaining text content, and in response to the amount of remaining text content being more than one, performing deduplication processing on the text content; Perform grammatical and semantic calibration on the remaining text content; The calibrated text content is output to the terminal device where the chat software is located and / or an external terminal device for prompting.

2. The method for identifying chat record messages based on OCR according to claim 1, characterized in that: Capturing the message content specifically includes: opening a new session in the chat software interface, and taking a screenshot of the area of ​​the new session; the message content is included in the screenshot; The “identifying the message content through OCR recognition technology” includes: identifying the captured image through OCR recognition technology.

3. The method for identifying chat record messages based on OCR according to claim 2, characterized in that: The message content includes voice message content; the method further includes: before identifying the message content, converting the voice message content into a message content in text format and displaying it in the area of ​​the new conversation.

4. The method for identifying chat record messages based on OCR according to claim 1, characterized in that: The "obtaining the complete text content in the message content" further includes: determining whether the text content has been cached, and if not, caching the text content; The “analyzing the text content” includes: analyzing the text content that is determined to be not cached; and deleting the cached text content after a preset time.

5. The method for identifying chat record messages based on OCR according to claim 1, characterized in that: The method further comprises: In the case of capturing the message content at intervals, the updating frequency of the message content in the chat software interface is calculated so that the capturing time interval matches the updating frequency.

6. The method for identifying chat record messages based on OCR according to claim 1, characterized in that: The “extracting key information from the text content” includes: performing part-of-speech tagging and named entity recognition on the divided words, thereby extracting the key information; The "outputting the calibrated text content to the terminal device where the chat software is located and / or the external terminal device" includes: highlighting the key information in the text content and outputting it to the terminal device where the chat software is located and / or the external terminal device.

7. The method for identifying chat record messages based on OCR according to claim 1, characterized in that: The “filtering the text content” also includes: Invalid content in the text content is identified and filtered through a preset machine learning model.

8. The method for identifying chat record messages based on OCR according to claim 1, characterized in that: The “de-duplication processing of text content” includes: Calculate the similarity between different text contents, and retain one of the multiple text contents whose similarity is higher than a preset threshold; and / or calculate the similarity between different text contents from the same message source, and retain one of the multiple text contents whose similarity is higher than a preset threshold.

9. The method for identifying chat record messages based on OCR according to claim 1, characterized in that: The “outputting the calibrated text content to the terminal device where the chat software is located and / or the external terminal device” includes: sending the text content to the application on the terminal device where the chat software is located, and / or sending the text content to the application on the external terminal device; The “providing a prompt” includes: the application program displays the text content on the screen of the terminal device; and / or the application program converts the text content into voice and broadcasts it through the terminal device.

10. A system for identifying chat log messages based on OCR, characterized in that: include: The OCR recognition module is used to capture the content of newly received messages in the chat software interface in real time or at intervals, and recognize the content of the message through OCR recognition technology to obtain the complete text content of the message content; An analysis module, used to analyze the text content, divide the words in the text content and extract key information in the text content, and determine the type of the text content based on the divided words and key information; A filtering module, used to filter the text content, filter the text content belonging to a custom filtering type, and filter the text content containing a custom filtering keyword; a deduplication module, used for determining the number of remaining text contents, and in response to the number of text contents being at least two, performing deduplication processing on the text contents; A calibration module, used for grammatical calibration and semantic calibration of the remaining text content; The output module is used to output the calibrated text content to the terminal device where the chat software is located and / or an external terminal device for prompting.

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