1. A method for generating English recitation lecture material using image association technique, comprising: receiving, through an input means, a set of source materials in English; processing the received source material using a natural language processing algorithm to extract key concepts and relevant information; selecting one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content; and generating a set of lecture contents in English based on the extracted information and the associated images, where each lecture content includes at least one image.

2. The method of claim 3, wherein the lecture contents are structured to facilitate the development of reading comprehension skills, selected from vocabulary, inference, and critical thinking.

3. The method of claim 1, further comprising generating a description or label for the identified object, feature, or concept for each images.

4. The method of claim 3, wherein the lecture contents are generated in different levels of difficulty and complexity, to cater to different reading proficiency levels.

5. The method of claim 1, wherein the image association is based on a visual similarity or analogy between the image and the extracted content.

6. The method of claim 1, wherein the images are selected from a database of pre-existing images or are generated using a generative adversarial network (GAN) or other machine learning techniques.

7. The method of claim 1, wherein the lecture contents are delivered through a digital platform, such as a website, an e-learning platform, or a mobile application.

8. The method of claim 7, wherein the digital platform provides interactive features, such as quizzes, games, or feedback mechanisms, to enhance the learning experience.

9. A system for generating English recitation lecture materials using image association, comprising a computing device configured to receive a set of source materials in English, process them using a natural language processing algorithm to extract key concepts and relevant information, select one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content, and generate a set of recitation lecture materials in English based on the extracted information and the associated images, where each lecture content includes at least one image.   IMAGE ASSOCIATION MECHANISM FOR ENGLISH COMPREHENSION Abstract A method for creating English recitation lecture material using an image association methodology may be included in certain embodiments of the current disclosure. This method may involve receiving, via an input mechanism, a set of source materials written in English. In certain embodiments, there is also the possibility of analysing the source material that has been received by means of a natural language processing algorithm in order to extract important ideas and information. Embodiments may additionally comprise choosing one or more pictures associated with each extracted idea or information, depending on the relevance and coherence of the image with the extracted material. It is also possible for embodiments to comprise the generation of a set of lecture contents in English based on the extracted information and the related pictures, where each lecture content contains at least one image, and where the information and images are connected with each other. Fig. 1 , Claims:Claims :

1. A method for generating English recitation lecture material using image association technique, comprising: receiving, through an input means, a set of source materials in English; processing the received source material using a natural language processing algorithm to extract key concepts and relevant information; selecting one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content; and generating a set of lecture contents in English based on the extracted information and the associated images, where each lecture content includes at least one image.

2. The method of claim 3, wherein the lecture contents are structured to facilitate the development of reading comprehension skills, selected from vocabulary, inference, and critical thinking.

3. The method of claim 1, further comprising generating a description or label for the identified object, feature, or concept for each images.

4. The method of claim 3, wherein the lecture contents are generated in different levels of difficulty and complexity, to cater to different reading proficiency levels.

5. The method of claim 1, wherein the image association is based on a visual similarity or analogy between the image and the extracted content.

6. The method of claim 1, wherein the images are selected from a database of pre-existing images or are generated using a generative adversarial network (GAN) or other machine learning techniques.

7. The method of claim 1, wherein the lecture contents are delivered through a digital platform, such as a website, an e-learning platform, or a mobile application.

8. The method of claim 7, wherein the digital platform provides interactive features, such as quizzes, games, or feedback mechanisms, to enhance the learning experience.

9. A system for generating English recitation lecture materials using image association, comprising a computing device configured to receive a set of source materials in English, process them using a natural language processing algorithm to extract key concepts and relevant information, select one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content, and generate a set of recitation lecture materials in English based on the extracted information and the associated images, where each lecture content includes at least one image.

Description:IMAGE ASSOCIATION MECHANISM FOR ENGLISH COMPREHENSION
Field of the Invention
[0001] The present invention relates to advance image processing technology. More specifically to system and method for generation of English comprehension from image.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] English comprehension teaching is a fundamental aspect of English language learning. It is the process of teaching students how to understand, interpret and analyse written or spoken English language. The goal of comprehension teaching is to help students develop skills that enable them to read and comprehend a variety of texts, including novels, articles, essays, and reports. Generally visual aids can enhance English language learning by providing a visual reference that can help learners make connections between new vocabulary and concepts and their existing knowledge. This is particularly important for learners who have limited exposure to English language and culture, as they may struggle to understand abstract or unfamiliar concepts. Image association mechanism for English comprehension can take many forms, including the use of pictures, diagrams, charts, and videos. These visual aids can be used to illustrate vocabulary words, sentence structures, and grammatical concepts. For example, a picture of a cat can be used to illustrate the word "cat," while a diagram of a sentence structure can be used to show the placement of subject, verb, and object in a sentence. In addition to facilitating English language learning, the use of visual aids can also make the learning process more engaging and enjoyable for learners. It can help to create a more immersive learning experience, allowing learners to see and interact with the language in a more meaningful way.Patent literature is rich source of disclosure for image to text conversion technique. Few of them are discussed below.
[0004] The CN105190678A (By: MEDIA MOUTH) - A method of providing a language learning environment comprises accessing media content, and identifying a number of content objects associated within the media instance. An apparatus for providing a language learning environment, comprises a processor, and a memory communicatively coupled to the processor, in which the memory comprises of metadata for the media, and in which the processor executes computer program instructions to, access media, and identify a number of content objects associated within the media instance. A computer program product for providing a language learning environment comprises a computer readable storage medium comprising computer usable program code embodied therewith, the computer usable program code comprising computer usable program code to, when executed by a processor, access media, computer usable program code to, when executed by the processor, identify a number of content objects associated within a media instance.
[0005] The US20130196292A1 (By: SHARP) - A multimedia-based language learning method and system which includes implementing via one or more processors the steps of-receiving an input of multimedia content, where the multimedia content comprises a plurality of component tracks; separating the multimedia content into multimedia sections in which the plurality of component tracks share a same start and end time; retrieving a user model representing a learner's knowledge and/or interest in a foreign language; automatically assigning one or more learner-specific evaluations to the multimedia sections by evaluating one or more of the component tracks based on the user model within each of the multimedia sections; and adapting the multimedia content within the multimedia sections based on the assigned learner-specific evaluations to render the multimedia content more useful to the learner for learning the foreign language.
[0006] The KR10-2022-0168871A (By : PARK, MYUNG JAE) - An English class content providing method performed by a user terminal includes outputting English class content including a plurality of English sentences, outputting a plurality of restoration selection interfaces corresponding to the plurality of English sentences at positions adjacent to each of the plurality of English sentences, And generating, based on the user input to at least one interface of the plurality of ambulatory selection interfaces, English ambulatory class content including at least one English sentence associated with the at least one interface.
[0007] US10453353B2 (By: FULL TILT AHEAD) - According to some embodiments, a reading comprehension apparatus comprises a processor, an optimized reading comprehension display, and a selection tool to select text within a digitized portion of a written work. A word ranking engine ranks each word from the selected text, via the processor. A filtering engine identifies a plurality of top-ranked words within the selected text and an animation engine animates and displays each of the top-ranked words identified by the processor in a sequential manner on the optimized reading comprehension display. Furthermore, the animation engine displays a presentation of the selected text including the top-ranked words in a highlighted manner.
[0008] However, known techniques are non-efficient and required heavy capital investment, thus become non-affordable. Thus, there is scope of improvement in this technological domain.

Summary
[0009] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00010] The following paragraphs provide additional support for the claims of the subject application.
[00011] The present invention relates to advance image processing technology. More specifically to system and method for generation of English comprehension from image.
[00012] Embodiments of the present disclosure may include a method for generating English recitation lecture material using image association technique, which includes receiving, through an input means, a set of source materials in English. Embodiments may also include processing the received source material using a natural language processing algorithm to extract key concepts and relevant information.
[00013] Embodiments may also include selecting one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content. Embodiments may also include generating a set of lecture contents in English based on the extracted information and the associated images, where each lecture content includes at least one image.
[00014] In some embodiments, the lecture contents may be structured to facilitate the development of reading comprehension skills, selected from vocabulary, inference, and critical thinking. In some embodiments, the method may include generating a description or label for the identified object, feature, or concept for each image.
[00015] In some embodiments, the lecture contents may be generated in different levels of difficulty and complexity, to cater to different reading proficiency levels. In some embodiments, the image association may be based on a visual similarity or analogy between the image and the extracted content. In some embodiments, the images may be selected from a database of pre-existing images or may be generated using a generative adversarial network (GAN)or any other machine learning techniques. In some embodiments, the lecture contents may be delivered through a digital platform, such as a website, an e-learning platform, or a mobile application. In some embodiments, the digital platform provides interactive features, such as quizzes, games, or feedback mechanisms, to enhance the learning experience.
[00016] Embodiments of the present disclosure may also include a system for generating English recitation lecture materials using image association, including a computing device configured to receive a set of source materials in English. Followed by processing them using a natural language processing algorithm to extract key concepts and relevant information. Further, selecting one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content, and generate a set of recitation lecture materials in English based on the extracted information and the associated images, where each lecture content includes at least one image.
Brief Description of the Drawings
[00017] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00018] FIG. 1 is a flowchart illustrating a method for generating English recitation lecture material, according to some embodiments of the present disclosure.
Detailed Description
[00019] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00020] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00021] The present invention relates to advance image processing technology. More specifically to system and method for generation of English comprehension from image.
[00022] The process for producing English recitation lecture material is shown in flowchart form in FIG. 1, which also provides a description of the technique in accordance with various embodiments of the present disclosure. In some implementations of the technique, step 110 may include the process of receiving, through an input means, a collection of source materials written in English. At step 120, the technique can involve analysing the source material (that was received) using a natural language processing algorithm in order to extract important ideas and information that is pertinent to the situation. At step 130, the technique may involve choosing one or more photos associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content, depending on how many images are connected with each extracted idea or information. At step 140, the technique may comprise the step of producing a set of lecture contents in English based on the extracted information and the related pictures, with at least one image being included in each of the lecture contents that are generated.
[00023] In certain implementations, the lecture material may be organised in a way that makes it easier to acquire reading comprehension skills, such as vocabulary, inference, and critical thinking. Producing a description or label for each picture based on the detected item, feature, or idea is an optional step that may be included in certain implementations of the approach. The lecture material may be created in a variety of various levels of difficulty and complexity, depending on the embodiment. This is done so that it may be tailored to individuals with varying degrees of reading skill.
[00024] The picture connection may, in certain implementations, be based on a visual likeness or analogy between the image and the extracted text. A generative adversarial network (GAN) or other approaches for machine learning may be used in certain implementations to produce the photos, or alternatively, the images may be picked from an existing image database and used in those implementations. In some implementations, the lecture material might be provided to the audience by means of a digital medium, such as a website, an online learning environment, or a mobile application. The digital platform may, in certain embodiments, include interactive elements to improve the learning experience. Some examples of these features include quizzes, games, and feedback systems.
[00025] A system that uses picture association to generate English recitation lecture materials is shown in certain implementations of the system. At least one visual example
[00026] A method for creating English recitation lecture material using an image association methodology may be included in certain embodiments of the current disclosure. This method may involve receiving, via an input means, a set of source materials written in English. In certain embodiments, there is also the possibility of analysing the source material that has been received by means of a natural language processing algorithm in order to extract important ideas and information.
[00027] In some embodiments, the selection of one or more pictures associated with each extracted idea or information is based on the relevance and coherence of the image with the material that was extracted. In other embodiments, the selection of images is not based on relevance or coherence. It is also possible for embodiments to comprise the generation of a set of lecture contents in English based on the extracted information and the related pictures, where each lecture content contains at least one image, and where the information and images are connected with each other.
[00028] In certain implementations, the lecture material may be organised in a way that makes it easier to acquire reading comprehension skills, such as vocabulary, inference, and critical thinking. Producing a description or label for each picture based on the detected item, feature, or idea is an optional step that may be included in certain implementations of the approach.
[00029] The lecture material may be created in a variety of various levels of difficulty and complexity, depending on the embodiment. The picture connection may, in certain implementations, be based on a visual likeness or analogy between the image and the extracted text. This similarity or analogy may exist between the two entities. The GAN or other approaches for machine learning may be used in certain implementations to produce the photos, or alternatively, the images may be picked from an existing image database and used in those implementations. In some implementations, the lecture material might be provided to the audience by means of a digital medium, such as a website, an online learning environment, or a mobile application. The digital platform may, in various embodiments, make available interactive elements, such as quizzes, games, or feedback systems, with the intention of improving the quality of the educational experience.
[00030] A system for generating English recitation lecture materials using image association may also be included in embodiments of the present disclosure. This system includes a computing device that is configured to receive a set of source materials in English, process those materials using a natural language processing algorithm to extract key concepts and relevant information, select one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the concept or information, and then generate the English recitation lecture materials.
[00031] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00032] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00033] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00034] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00035] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.
 

Claims
I/We Claim:
1. A method for generating English recitation lecture material using image association technique, comprising:
receiving, through an input means, a set of source materials in English;
processing the received source material using a natural language processing algorithm to extract key concepts and relevant information;
selecting one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content; and
generating a set of lecture contents in English based on the extracted information and the associated images, where each lecture content includes at least one image.
2. The method of claim 3, wherein the lecture contents are structured to facilitate the development of reading comprehension skills, selected from vocabulary, inference, and critical thinking.
3. The method of claim 1, further comprising generating a description or label for the identified object, feature, or concept for each images.
4. The method of claim 3, wherein the lecture contents are generated in different levels of difficulty and complexity, to cater to different reading proficiency levels.
5. The method of claim 1, wherein the image association is based on a visual similarity or analogy between the image and the extracted content.
6. The method of claim 1, wherein the images are selected from a database of pre-existing images or are generated using a generative adversarial network (GAN) or other machine learning techniques.
7. The method of claim 1, wherein the lecture contents are delivered through a digital platform, such as a website, an e-learning platform, or a mobile application.
8. The method of claim 7, wherein the digital platform provides interactive features, such as quizzes, games, or feedback mechanisms, to enhance the learning experience.
9. A system for generating English recitation lecture materials using image association, comprising a computing device configured to receive a set of source materials in English, process them using a natural language processing algorithm to extract key concepts and relevant information, select one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content, and generate a set of recitation lecture materials in English based on the extracted information and the associated images, where each lecture content includes at least one image.
 

IMAGE ASSOCIATION MECHANISM FOR ENGLISH COMPREHENSION
Abstract
A method for creating English recitation lecture material using an image association methodology may be included in certain embodiments of the current disclosure. This method may involve receiving, via an input mechanism, a set of source materials written in English. In certain embodiments, there is also the possibility of analysing the source material that has been received by means of a natural language processing algorithm in order to extract important ideas and information. Embodiments may additionally comprise choosing one or more pictures associated with each extracted idea or information, depending on the relevance and coherence of the image with the extracted material. It is also possible for embodiments to comprise the generation of a set of lecture contents in English based on the extracted information and the related pictures, where each lecture content contains at least one image, and where the information and images are connected with each other.

Fig. 1 , Claims:Claims
I/We Claim:
1. A method for generating English recitation lecture material using image association technique, comprising:
receiving, through an input means, a set of source materials in English;
processing the received source material using a natural language processing algorithm to extract key concepts and relevant information;
selecting one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content; and
generating a set of lecture contents in English based on the extracted information and the associated images, where each lecture content includes at least one image.
2. The method of claim 3, wherein the lecture contents are structured to facilitate the development of reading comprehension skills, selected from vocabulary, inference, and critical thinking.
3. The method of claim 1, further comprising generating a description or label for the identified object, feature, or concept for each images.
4. The method of claim 3, wherein the lecture contents are generated in different levels of difficulty and complexity, to cater to different reading proficiency levels.
5. The method of claim 1, wherein the image association is based on a visual similarity or analogy between the image and the extracted content.
6. The method of claim 1, wherein the images are selected from a database of pre-existing images or are generated using a generative adversarial network (GAN) or other machine learning techniques.
7. The method of claim 1, wherein the lecture contents are delivered through a digital platform, such as a website, an e-learning platform, or a mobile application.
8. The method of claim 7, wherein the digital platform provides interactive features, such as quizzes, games, or feedback mechanisms, to enhance the learning experience.
9. A system for generating English recitation lecture materials using image association, comprising a computing device configured to receive a set of source materials in English, process them using a natural language processing algorithm to extract key concepts and relevant information, select one or more images associated with each extracted concept or information, based on the relevance and coherence of the image with the extracted content, and generate a set of recitation lecture materials in English based on the extracted information and the associated images, where each lecture content includes at least one image.