KR20200086112A - System for memorizing and studying of index applying the image in book cover - Google Patents

Abstract

According to the present invention, provide is a contents subject memorization learning system applying an image to a book cover, which comprises: a contents input module receiving contents including a major topic from an author; a keyword extraction module extracting any one of words and phrases constituting the contents for each major topic and setting the any one as a keyword; an image processing interface including an image database including an image for expressing the keyword and an image extraction module extracting an image reminiscent of or referring to the meaning of the keyword from the image database; and a book cover application module arranging and displaying the images extracted for each major subject while excluding the contents.

Description

A system for memorizing and learning the subject of a table of contents by applying an image to a book cover{SYSTEM FOR MEMORIZING AND STUDYING OF INDEX APPLYING THE IMAGE IN BOOK COVER}

The present invention relates to a system for memorizing and learning the subject of the table of contents. More specifically, the image associated with the table of contents composed of text is applied to the initial page of the book in which the table of contents is composed, and only the corresponding image is inserted into the cover of the book without the table of contents. Therefore, the present invention relates to a system for memorizing and learning the subject of a table of contents by applying an image to a book cover that enables the reader to remember the table of contents more effectively by the image association method.

The table of contents lists the major parts of the main body of the publication in order or importance, making it easy to find and often referred to as a turn.

This table of contents serves to outline the overall flow of the text simply and clearly, and serves to outline the overall story of the text and to have the reader search the text in advance.

Sometimes, the reader memorizes the table of contents before reading the text in order to understand the text in a systematic order.

However, there is a problem that the reader is difficult to memorize the contents because the contents of the bulletin books and booklets are made of text only, so the contents are hard and formal, and memorizing the table of contents by a two-character memorization method after the first letter of the table of contents There are also disadvantages that two characters are difficult to memorize as long as the two characters themselves do not have a specific meaning.

For example, in the'Automatic Table of Contents Apparatus and Method' of Korean Patent Registration No. 10-238030, there is an effect of providing convenience of creating a table of contents by automatically scanning the document and extracting the table of contents title according to the criteria. It is disclosed.

However, according to the above technique, although it may provide convenience of creating a table of contents, it is still insufficient to solve the above-mentioned problem because it is still composed of text characters as well as a known book.

In order to solve this problem, as filed by the present applicant, in the'Image Table of Contents Editing System for Memorizing the Table of Contents of a Book' of Korean Patent Application Publication No. As a result, the technology that promotes the convenience of memorizing and understanding the table of contents by composing the image as a part of the table of contents together with the table of contents by combining the words that are the core of the text characters or words designated by the artist into images.

However, in the above technology, the table of contents is composed only of the initial page of the book, so if the book is stuck on a bookshelf, etc., the reader has the trouble of opening the book in order to check the table of contents. It is a situation that requires skill.

The present invention has been devised to overcome the problems of the technology, and applies an image reminiscent of a table of contents made of text to the initial page of a book in which the table of contents is composed, and only inserts the image without a table of contents on the cover of the book. The main purpose of this study is to provide a table of contents memorization learning system that applies images to a book cover that enables readers to remember the table of contents more effectively by the image association method.

In order to achieve the above object, a table of contents memorization learning system by applying an image to a book cover according to the present invention includes: a table of contents input module that receives a table of contents including a major topic from an author; A keyword extraction module that extracts any one of the words and phrases constituting the table of contents for each of the major topics and sets them as keywords, an image database containing images for expressing the keywords, and reminiscent or referring to the meaning of the keywords An image processing interface including an image extraction module for extracting images from the image database; And a book cover application module for arranging and displaying the images extracted for each major subject in a state other than the table of contents.

In addition, the book cover is characterized in that at least one of the front cover, the back cover, and the belt of the book.

In addition, the book cover application module is characterized in that it comprises a sequence number display unit for displaying the sequence number of the main subject in the order of the table of contents on one side of each image.

The table of contents subject memorization learning system in which an image is applied to a book cover according to the present invention allows a reader to more easily understand or memorize a table of contents through images associated with a main topic and a sub topic in a table of contents composed of text.

Furthermore, by inserting only the image on the book cover as well as the table of contents, the reader can check the image even when the book is not opened, so that the table of contents can be more effectively remembered through the image association method.

In addition, by inserting a composite image obtained by synthesizing images associated with each of the major topics and sub-themes into the table of contents, or by inserting a tree diagram related to the system of the main-subject images and sub-subject images into the table of contents, readers can more easily remember the major topics and their sub-classes, sub-topics can do.

In addition, by inserting the subject topic image into the body of the book, the reader can repeatedly check the subject topic image when reading the text, thereby effectively remembering the table of contents.

Also, as each image data in the image database has tags corresponding to keywords, it may be implemented to extract image data having tags corresponding to the meaning of keywords in the image extraction module.

In addition, the author's comment provided by the author can be additionally provided to the reader by inserting the author's comment into the second area, which is near the first area where the soju theme is displayed.

In particular, in this case, the distance between the centers of the first and second areas is differentially controlled according to the degree of association between the subject keywords and the key words of the author, so that the reader only checks the distances between the centers of the subject keywords and authors. The degree of association with the key word can be grasped, and the distance between the centers of the first and second regions can be differentially controlled according to the size of the first region, thereby ensuring the comfort of the reader's visual visibility.

1 is a block diagram showing the configuration of a table of contents memorization learning system according to the present embodiment.
FIG. 2 is a conceptual diagram showing a cover provided by the table of contents subject memorization learning system of FIG. 1;
3 is a conceptual diagram showing a state in which an image is inserted into a table of contents.
Figure 4 is a conceptual diagram showing an embodiment showing an image of the connection relationship between the middle title and the small title of the table of contents.
5 is a conceptual diagram showing an embodiment in which the image of the subtitle of FIG. 4 is inserted into the text.
Figure 6 is a conceptual diagram showing another embodiment showing an image of the connection relationship between the middle title and the small title of the table of contents.
FIG. 7 is a conceptual diagram illustrating an embodiment in which an image relating to a subtitle of FIG. 6 is inserted into a text.
8 is a conceptual diagram showing another embodiment in which the image relating to the subtitle of FIG. 6 is inserted into the text.
9 is a conceptual diagram illustrating an embodiment of calculating the distances of the first and second regions.
10 is a conceptual diagram showing another embodiment of calculating the distances of the first and second regions.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. The accompanying drawings are not drawn to scale, and the same reference numbers in each drawing refer to the same components.

1 is a block diagram showing the configuration of a table of contents subject learning system 10 according to the present embodiment, and FIG. 2 is a conceptual diagram showing a cover provided by the table of contents subject memory learning system 10 of FIG. 3 is a conceptual diagram showing a state in which an image is inserted into a table of contents.

Contents subject memorization learning system (10) of the present invention can be applied to e-books using electronic media as well as booklets made of paper, providing the above-mentioned booklet and e-book itself as well as a basis for producing them It encompasses things.

In other words, the table of contents subject learning memorization system 10 of the present invention may be implemented as a book production system 10 used in a publisher or the like that produces a book.

The table of contents subject learning learning system 10 of the present invention includes hardware and software that perform detailed functions to be described later, and the hardware may include a system 10 server, a DB server, a web/app application server, and a table of contents server. Each server includes CPU, memory, and storage space. In addition, software will be embodied below as performing a program or a series of processes based on the hardware. That is, the system 10 of the present invention means that such hardware and software are organically combined.

More specifically, referring to FIG. 1, the table of contents subject learning system 10 is a table of contents subject learning system 10 by applying an image to a book cover, the table of contents input module 100, the image processing interface 200, Book cover application module 300, table of contents classification module 400, table of contents application module 500, composite image application module 600, subtopic image application module 700, system image application module 800, and association degree identification module (900).

The table of contents input module 100 of the present invention is to receive a table of contents composed of text from the author, and when the artist wants to produce the content he has written into a book (including paper books, e-books, etc.), the table of contents related to the above contents, namely By creating a text that summarizes the subject or core content of the main content of the content in a simple or concise manner, and organizes it into a table of contents, it is intended to inform the reader of the overall story of the main content.

Such a table of contents is mainly composed of word combinations or sentence forms, that is, text, and when the author creates and provides a table of contents in this way, the table of contents input module 100 receives the table of contents from the author to receive the table of contents. You will be able to compose a table of contents on the initial page of the book.

For example, as will be described later, as illustrated in FIG. 2(a), chapters 1, 2, and 3 are referred to as major subjects, and the major subjects may receive sub-subjects, which are sub-concepts, represented by 1), 2), and 3). Can be.

At this time, the table of contents input module 100 may be provided with a document in which the table of contents is created from the author's client server (PC or application), and may perform a function of storing the provided document in a separate table of contents server. .

Here, the table of contents server mainly means a server provided in a publishing company, which can be linked with a computer program or an application of a smartphone, and has a function of inputting and storing an article written by an author as well as a function of receiving a table of contents. Can provide.

Meanwhile, the table of contents 11 received from the author may include one or more major topics 13 and sub-topics 14 for each topic as shown in FIG. 2(b), and accordingly, the table of contents input module ( 100) may be said to receive a table of contents 11 including the main subject 13 and the sub-form subject 14.

The image processing interface 200 of the present invention inserts an image related to at least one of the major subject and the minor subject of the table of contents into the page area of the book occupied by the table of contents, the cover area of the book, or the area where the body content of the book is written The keyword extraction module 210 includes an image database 220 and an image extraction module 230.

The keyword extraction module 210 serves to extract any one of words and phrases constituting the table of contents for each of the major topics and set them as keywords. Here, the keyword means the most representative or symbolic word or phrase among a plurality of words included in each major subject of the table of contents, and has a meaning of converting images to be inserted into a book cover, table of contents, text, and the like.

In this case, the writer who created the table of contents may select words that he or she considers important or regard as a representative word as a keyword. Alternatively, the key words in the context of sentences included in the table of contents in the table of contents memorization learning system 10 Keywords may be selected by extracting important words through an extraction algorithm that extracts.

Meanwhile, the keyword extraction module 210 of the present invention may primarily extract words corresponding to nouns from, for example, sentences constituting the main subject. That is, in general, verbs and adjectives constituting sentences are more difficult to express as images than nouns, so nouns are preferentially selected as keyword targets to ensure intuition of image expression.

In addition to this, the keyword extraction module 210 may be provided with a language library to analyze keywords and to understand the meaning of the keywords. Here, the language library may include an associative word database containing associative words composed of any one of words and phrases whose meanings have been reconfigured so as to associate words corresponding to non-fluids with a specific image.

The image database 220 is to record an image for expressing the keyword, that is, to express an image in which a specific word is expressed as a specific fluid or a picture that can cause an associative action.

The image extraction module 230 performs a function of extracting an image reminiscent or referring to the meaning of the keyword from the image database 220. In other words, the image extraction module 230 extracts images suitable for each major subject from the image database 220.

At this time, each image data in the image database 220 may be provided with tags corresponding to keywords. For example, when the sunflower picture is image data, a tag may be applied hierarchically, such as'flower' for the main tag and'sunflower' for the sub tag. Accordingly, the image extraction module 230 may implement a specific function by extracting image data having tags corresponding to the meaning of keywords.

This may be performed automatically by accessing the image database 220 at the dimension memorization learning system 10 dimension of the table of contents, and provides an environment in which an author can view the image database 220 to provide specific keywords or associated words. It is also possible to upload the extracted image by directly extracting the image that suits or fits the most appropriately.

Of course, since the keyword itself is often set directly by the author, it is not desirable to allow the reader to arbitrarily change the keyword itself, but when the image database 220 has a strong image of the reader itself, the keyword itself The reader's personal preference can be added by allowing the user to select an image reminiscent of the keyword as a separate image.

Since the image processing interface 200 is the same as the image extraction configuration of Patent Application No. '10-2013-0123206', which is the patent of the applicant, a separate detailed description is omitted.

The book cover application module 300 of the present invention serves to arrange the images extracted for each major subject in the image extraction module 230 and display them on the book cover. In this case, the book cover is a front cover, a back cover (located in a direction facing the front cover), a strip (between the front cover and the back cover, positioned vertically with each of the front cover and the back cover) It can be at least one. Here, since the front cover or the back cover is formed with a relatively larger area than the strip, the book cover application module 300 is configured so that the reader can more easily check images corresponding to the table of contents without opening the book. It may be more preferable to arrange the image on the front cover or the back cover.

For example, as illustrated in FIG. 2, images extracted for each major topic of the table of contents may be arranged on the front cover of the book.

Furthermore, a sequence number display unit may be provided on one side of each image to display the sequence number of the major topics according to the order of the table of contents.

That is, as illustrated in FIG. 2, when the main theme of the table of contents is 1, 2, *?**?*, 7, seven images may be arranged on the book cover, and at this time, one of each image On the side, a sequence number indicating numbers 1, 2, *?**?*, 7 may be formed.

On the other hand, the table of contents classification module 400 of the present invention can classify the table of contents into a plurality of blocks based on the main topic and insert them into the initial page of the book as shown in FIG. 2.

That is, the table of contents classification module 400 performs a function of grasping the main topic of the table of contents and processing the table of contents as a block based on the topic. For example, as shown, one major subject is'Chapter 1. Schumann's wife, a female pianist and composer, Clara, and the subtopics included in it, as well as another major topic,'Chapter 2. You can classify each block as a paragraph by classifying each block into a block that contains the infinite willful bailist paganini” and the small subjects included in it.

In the table of contents classification module 400, the image is set by setting a formal criterion in which an image can be arranged before inserting an image into an area or a side of each block in the table of contents application module 500 to be described later. It serves to prevent bias from being placed in any one of a plurality of classified blocks, that is, to present a formal criterion for image generation that allows one image to be generated for each block classified in the table of contents.

In the table of contents of the book, the table of contents application module 500 of the present invention performs a function of displaying the image corresponding to the main topic in or on one side of the block. That is, as shown in FIG. 3, since each table of contents 20 in chapters 1 to 7 is block-processed as described above, each block {21, 22, *?**?*, 26, 27} As shown in the figure, an image referring to or reminiscent of a keyword of a large subject may be inserted.

In this case, it is not standardized to insert one image per block, and it is also possible to set two or more images per block by the author's setting or the system 10.

On the other hand, Figure 4 is a conceptual diagram showing an embodiment showing the connection relationship between the middle title and the small title of the table of contents, Figure 5 is a conceptual diagram showing an embodiment in which the image of the subtitle of Figure 4 is inserted into the text .

The table of contents input module 100 may receive at least one sub-category that is a sub-category of the main topic from the author. For example, in the table of contents shown in FIG. 2,'Section 1'includes five sub-categories of its sub-categories, and'Chapter 2'and'Chapter 3'each include three sub-categories of its sub-categories, ' Chapter 4'may include two sub-categories that are sub-categories, and as such, it is preferable that at least one sub-category is included in the main topic.

As illustrated in FIG. 4, the book may include a subtopics table of contents page 40 in which the main topic and subcategories subcategories thereof are displayed as text in pages containing contents related to a specific main topic.

That is, the book may constitute the subtopics table of contents page 40 composed of one page so that one main topic and the subtopics subcategory of the main topic are placed on the subtopics table of contents page 40. have.

The image processing interface 200 is a word constituting the sub-topic in order to insert the image referring to or reminiscent of the main topic, as well as each sub-category sub-category or image reminiscent of the sub-topics table of contents page 40. And a sub-theme image reminiscent of or referring to the sub-subject keyword extracted from each of the sub-subjects in the image database 220.

In other words, the keyword extraction module 210 of the image processing interface 200 extracts keywords of each of the sub-subjects that are sub-categories of the main subject as well as the main subject, thereby extracting the image of the image processing interface 200 The module 230 may extract, from the image database 220, an image reminiscent or referring to the meaning of the keyword of the sub-topic, as well as the keyword of the main topic.

As described above, in the state where the table of contents page 40 of the subdivision is provided, the composite image application module 600 generates a composite image of the image of the main topic and the image of the subdivision and creates a composite image of the subdivision table of contents page 40 The composite image is displayed on the side.

For example, as shown in FIG. 4, the composite image application module 600 may display the composite image in a space formed between the main subject and the subjugation in the subtopics table of contents page 40. However, the composite image is preferably formed between the main agent and the inter-subject agent, but alternatively, the sub-subject agent may be formed under the main agent or under the sub-agent.

In this case, the composite image may be formed in a form in which a part of the image representing the keyword of the main subject and a part of the image representing the keyword of the subject are overlapped with each other or arranged close to each other.

For example, as shown in FIG. 4, the main theme of the table of contents page 40 of the sub-category is'Chapter 1. In the case of Schumann's wife female pianist and composer Clara, the soju system consists of five sub-categories: '1) Germany, which bloomed the romantic culture of 1850', and '2) Düsseldorf, who moved in with five children. In this case, the images representing the keywords of the main subject and the images representing the keywords of each of the five sub-topics are synthesized into a single image in a manner in which they are partially overlapped or closely arranged to be inserted into the table of contents page 40 of the sub-topics. Can.

The synthesized image synthesized as described above is at least one page among pages containing content related to a specific major topic, as illustrated in FIG. 5, that is, when the major topic is'one piece', the major topic of the'one piece' It may be inserted on one side of at least one of the pages describing the content related to, and accordingly, when the reader is familiar with the description of the content related to the main topic, checks the composite image and displays the composite image and the main topic. It becomes possible to match and understand, and it is possible to recognize contents related to the main subject represented by the composite image and sub-subjects thereof, by simply memorizing the composite image.

Furthermore, although not shown, the sub-subject image application module 700 may display the sub-subject image on one side of at least one page among pages containing content related to the main subject, thereby allowing the reader to memorize the image. You can make it easier to memorize the content related to the soju.

FIG. 6 is a conceptual diagram showing another embodiment of an image showing a connection relationship between a title and a sub-title of the table of contents, and FIG. 7 is a conceptual diagram showing an embodiment in which the image of the sub-title of FIG. 6 is inserted into the text; FIG. Is a conceptual diagram showing another embodiment in which the image relating to the subtitle of FIG. 6 is inserted into the text.

When the table of contents input module 100 receives at least one sub-category that is a sub-category of a specific main topic from the author, the book is a text of the main topic and its sub-category sub-topic within pages containing contents related to the specific main topic. Can be displayed.

At this time, the book may include a subtopics system table of contents page including a schematic diagram in which the subtopics are connected by an achematic line based on the main topic. Here, the phylogenetic diagram shows a systematic relationship between objects, and by connecting a plurality of sub-themes to the main subject, it may indicate that the plurality of sub-subjects are sub-classifications of the main subject.

In other words, the system table of contents page 60 includes a system diagram connected to each other through at least one sub-subject which is a sub-category thereof to one major subject, as shown in FIG. 6, in this embodiment In particular, when a plurality of sub-agents are formed, each of the sub-subjects forms a tree diagram connected to the main subject through the branch line 61, so that the readers are sub-classes of the sub-subjects connected by the reader through the branch line 61 You can make it easier to recognize.

On the other hand, in the present embodiment, the image processing interface 200 extracts from the image database 220 a subtopical image reminiscent of or refers to a subtopical keyword in which any one of the words and phrases constituting the subtopical is extracted for each subtopical. do.

In other words, the keyword extraction module 210 of the image processing interface 200 extracts keywords of each of the sub-subjects, which are sub-categories of the main subject, as well as the main subject, thereby extracting the image of the image processing interface 200 The module 230 extracts from the image database 220 an image reminiscent or referring to the meaning of the keyword of the minor subject as well as the keyword of the major subject.

Furthermore, in this case, the system image applying module 800 of the present invention displays the image related to the large subject on one side of the main subject in the system diagram, and displays the image of the sub-percent related to the sub-agent on one side of the main subject. do.

That is, the system table of contents page 60 not only includes texts representing major subjects and minor subjects, but also each of the major subjects and the minor subjects at positions adjacent to the texts of the major subjects and the minor subjects in the system image application module 800 By displaying the image related to, the image related to each of the major subject and the minor subject is also provided.

Through this, the reader can more easily understand the main subject and sub-category sub-subjects through the system diagram, and at the same time, each image is displayed more easily through the image by inserting images representing the main subject and sub-subjects into the system diagram. It is possible to memorize the large main agent and the small main agent.

Meanwhile, referring to FIGS. 7 and 8, the sub-topic image applying module 700 of the present invention also displays the sub-topic image on one side of at least one page among pages including contents related to the main topic in this embodiment. .

More specifically, as illustrated in FIG. 7, the sub-topic image applying module 700 inserts the same plurality of sub-subject images 70 into the main body of the main body containing contents related to a specific sub-topical subject, thereby allowing the reader to read the main body. In the case of reading, it is possible to repeatedly recognize the subjugation image 70 so that when the subjugation image 70 is consciously or unconsciously recognized, the subjugation related to the subjugation image 70 can be understood.

For example, if the soju system is '1) Germany where flowers of romantic culture bloomed in 1850', the soju system is shown as an image representing'flower' (see FIG. 6), and the soju system image application module 700 As shown in Fig. 7, the image representing the'flower' can be put into the text containing the contents of the '1) Germany where flowers of romantic culture bloomed in 1850'.

As another example, referring to FIG. 8, when the soju agent is '2) Düsseldorf moved along with five children, the soju agent is illustrated as an image 80 representing five'apples' (see FIG. 6 ). In this case, the subjugation image application module 700 may put the image representing the'apple' in the text containing '2) Düsseldorf moved in with five children'.

Meanwhile, the text table of contents input module 100 may receive a priority from a writer for a plurality of the subject matter. For example, the artist may give priority to the sub-category '1)' of FIG. 4 over '2)'. In this case, the picture representing'flower' is the book cover and table of contents page of the book rather than the picture representing'apple'. It can be largely formed on the back.

In other words, when the priority of the sub-topic is inputted from the author, the sub-topics with priority in the book cover, the table of contents page of the book including the sub-topic page 40 and the systematic table of contents page 60 are further added. By making it larger, readers can read the book and check it to make them understand the more important subtopics. For example, when a reader is studying a book such as a reference book, the size of the image for each subtopic can be checked on the table of contents page of the reference book, so that the contents of the text on a specific subtopics that are relatively important can be carefully examined.

Meanwhile, in order to change the size of the image associated with each sub-topic based on the priority after receiving the priority for the sub-subject from the author as described above, the size of each sub-subject image is different according to the priority of the sub-subject. Since it is necessary to process, the system image application module 800 includes a size variable unit that performs this function.

That is, the variable sized portion of the system image application module 800 varies the size of the sub-subject image in accordance with the priority of the sub-subject, and adjusts the size of the sub-subject image in the order of priority of the sub-subject. Differential can be formed large.

In this case, the size variable unit may make the size of each sub-theme image uniformly increase at the same rate, or alternatively, may be adjusted to increase at a random rate.

For example, when the priority is in the order of the first image, the second image, and the third image, the size variable unit determines the size of the first image having the highest priority at random or as a predetermined size. , The size of the second image is determined by multiplying the first image by a specific constant (eg 1/2), and the size of the third image is the same as a specific constant (1/2) of the second image. It can be determined by the size multiplied by. Alternatively, the size variable unit first determines the size of the first image having the highest priority, and then multiplies the first image by a random constant (less than 1) in the order of the highest priority, that is, the second image. The size of the third image may be determined, and the second image may be multiplied by a random constant (less than 1) to determine the size of the third image.

On the other hand, the text table of contents input module 100 may receive a writer's comment for each sub-subject according to the height of the priority of the sub-subject from the author on the table of contents page. Here, the writer's comment may be a comment implying a sentence-type subtope in a relatively short word or phrase, but is not limited thereto, and may include any language the writer wants to convey to the reader. For example, if the soju system is'Germany that blooms the flowers of romantic culture' and the keyword of the soju system is'flowers', to interpret this meaning, the flower implies meanings such as revival or development. It can be seen that the actual meaning of soju-je is not the importance of the flowering situation itself, but the development of a specific era of romanticism. Therefore, the writer can symbolize this subdivision in a single word, which is called the writer's comment, and in the above example, it can be expressed as'development'.

Such author comments may be formed in a position adjacent to the sub-topic in addition to the sub-topic in both the sub-top table of contents page 40 and the line-up table of contents page 60 described below. Only the comment of the author is formed in (60).

In other words, as described above, the system table of contents page 60 is composed of a system diagram and is displayed on the system table of contents page 60 in a state in which sub-topics are respectively connected to the main topic through the system line. The author's comment can be inserted in the adjacent part.

That is, in the system table of contents page 60, the area in which the sub-theme is displayed is the first area (91, see FIG. 9) displaying the sub-subject image, and is located around the first area to display the author. It may be divided into a second region 92 (see FIG. 9).

In this case, the second region 92 is not shown on the drawing, but may be formed on the opposite side of the system line based on the subjugation image. This is because the lineage indicates that a plurality of sub-subjects are sub-categories by connecting the main subject and a plurality of sub-subjects, so if the author's comment is formed on the same side as the line based on the sub-subject image, the reader will This is because it can be confusing to grasp the systematic relationship of the above-mentioned soju.

Further, in order to more accurately distinguish the first and second regions 92, although not illustrated, the second region 92 may have a smaller area than the first region 91, in this case Furthermore, the second region 92 is formed in one quadrant of the first region 91 when the first region 91 is separated into a virtual X-axis and a Y-axis based on the center. You can make your comments more visible.

On the other hand, the association degree grasping module 900 of the present invention can grasp the degree of association between a subdivision topic and a word of the author about the subdivision. That is, the association degree determining module 900 may grasp the association degree between the word estimated as the key word in the subdivision topic (the keyword of the subdivision topic) and the word estimated as the key word in the author's comment. In this case, since the author's comment is preferably formed of a single noun or a compound noun combined with two nouns, the association identification module 900 may extract the first noun from the author's word as a key word. Also, in the above-described sub-topical text, the first noun can be extracted as a key word.

The association degree determining module 900 may extract a key word from each of the sub-themes and the authors, and then classify a category for each key word and calculate the association degree of each classified category.

For example, when the association between the sub-topic keyword and the author is based on the sub-topic keyword, the highest correlation is 10 points, that is, when the correlation is determined to be 0.1 to 0.9 according to relevance, a plurality of sub-topic keywords Categories including related words may be set. For example, when the subjugation keyword is'flower', the first category includes types of flowers such as'sunflower','rose', and'Rhododendron', and the second category is'happy', a symbolic meaning of flowers ','development','hope', and the like. With this logic, it is possible to classify the third to nth categories. At this time, each category may include an association value, and in the above example, it is possible to set the association value as 0.9 in the first category and 0.8 in the second category. With this principle, it is possible to set the association value from 0.1 to 0.9 depending on the number of categories. For example, if each category is divided into 0.1 points, the words included in each category are calculated from 0.1 to 0.9 points. , It may be desirable to form a total of nine categories. That is, the association degree determining module determines whether two words are included in a plurality of categories to which one word belongs based on two words, and the number of associations set in the included categories. It is possible to grasp the degree of association as a numerical value.

In this embodiment, a method for measuring category association based on word association may be implemented in a form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, or the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the present invention or may be known and usable by those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs, DVDs, and magnetic media such as floptical disks. -Hardware devices specifically configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language code that can be executed by a computer using an interpreter, etc., as well as machine language codes made by a compiler. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the present invention, and vice versa.

9 is a conceptual diagram illustrating an embodiment of calculating the distances of the first and second regions.

As described above, when the association degree grasping module 900 grasps the association degree between the subject topic keyword and the author, the region control unit 810 of the system image application module 800 determines the association degree according to the height of the association degree. The distance between the center of the first region and the center of the second region may be differentially controlled, and the region overlapping unit 820 of the system image application module 800 may control the distance according to the distance control of the first and second regions. It is determined whether or not overlapping is performed between the regions 1 and 2, and when the first and second regions overlap with each other, the closed curve 95 of the overlapping region may be deleted.

More specifically, referring to FIG. 9, the region control unit 810 processes the first and second regions 91 and 92 as virtual concentric circles of an area in which each of the sub-topical images and the artist's comments can be included. In this case, a distance between the center of the first region 91 and the center of the second region 92 may be calculated through Equation 1 below.

Equation 1.

는 상기 제1 영역의 중심 및 제2 영역의 중심 사이의 거리,

은 상기 제1 영역의 중심 및 제2 영역의 중심까지 기본으로 설정된 거리, r은 제1 영역의 반지름, c는 상기 소주제 키워드 및 상기 작가멘트 간의 연관도, 단, 0<c<1,

는 거리 보정치일 수 있다.here,

Is the distance between the center of the first region and the center of the second region,

Is a distance set as a default to the center of the first region and the center of the second region, r is the radius of the first region, c is the degree of association between the subject topic keyword and the author, provided that 0<c<1,

May be a distance correction value.

은 1이고

는 1이고 C는 0.9로 동일한 조건이 주어진 경우, 상기 수학식 1에 r값을 대입할 때, r이 4이면

는 2.15이고, r이 6이면

는 2.73이다. chamberlain,

Is 1

Is 1 and C is 0.9, when the same condition is given, when substituting r into Equation 1, if r is 4

Is 2.15, and if r is 6

Is 2.73.

Through this, it can be seen that as the radius of the first region 91 increases, the distance between the centers of the first and second regions 91 and 92 increases.

When the radius of the first region 91 is relatively large and the distance between the centers of the first and second regions 91 and 92 is close, the second region 92 is visually focused on the first region 91. It may be difficult to check, as described above, the size of the radius of the first region 91 and the distance between the centers of the first and second regions are proportional to ensure comfort of the visual visibility of the reader.

On the other hand, the greater the degree of association between the words of the subject matter and the writer's comment described above, the closer the center of the first and second regions 91 and 92 can be processed.

는 1이고

는 1로 동일한 조건이 주어진 경우, 연관도가 0.9 인 경우

는 1.72이며, 연관도가 0.8인 경우에는

가 1.9이다.At this time, in this embodiment, it is assumed that the correlations are 0.9, 0.8, *?**?*, and 0.1 in order from the highest order. Substituting this correlation into Equation 1, for example, r is 3

Is 1

Is 1, given the same condition, the correlation is 0.9

Is 1.72, and if the association is 0.8

Is 1.9.

That is, it can be seen that the higher the degree of association, the closer the distance between the center of the first region and the center of the second region, which is the distance between the center of the first region and the center of the second region when the degree of association is high. By making it closer, you can accurately tell the reader the connection between the subject and author's comment.

는 3.9이며, 연관도가 0.1인 경우에는

가 4.9이다. 이 때,

값들의 차이는 앞의 실시예에서 연관도 0.9인 경우의

값과 연관도 0.8인 경우의

값의 차이 보다 훨씬 큰 것을 알 수 있다. 이는 본 실시예에서

값을 log함수를 통해 구하기 때문이며, 연관도가 상대적으로 큰 경우(연관도가 0.9, 0.8, 0.7인 경우)에는 연관도에 따라 소주제 및 작가멘트의 거리를 차등적으로 제어하는 것이 의미가 있으나, 연관도가 상대적으로 작은 경우(예를 들어 연관도 0.3 이하)에서는 소주제 및 작가멘트 사이의 거리를 차등적으로 제어하는 것보다 단순히 소주제 및 작가멘트 사이의 거리를 멀리하는 것이 의미 있으므로 특정 연관도 이하에서는 상기 영역 제어부(810)가 상기 수학식 1의

값을 따르지 않고 상기 1,2 영역 사이를 임의의 거리로 이격시키도록 처리할 수 있다.On the other hand, further, when the association is 0.2

Is 3.9, and if the association is 0.1

Is 4.9. At this time,

The difference in values is when the correlation is 0.9 in the previous embodiment.

When the value and the association are 0.8

You can see that it is much larger than the difference in values. This is in this embodiment

This is because the value is obtained through the log function, and when the association is relatively large (in the case of correlations of 0.9, 0.8, and 0.7), it is meaningful to differentially control the distance between the subject and the author depending on the association. In the case where the association degree is relatively small (for example, the association degree is 0.3 or less), it is meaningful to simply distance the distance between the subject and the author rather than differentially controlling the distance between the subject and the author. In the area control unit 810 of the equation (1)

It can be processed so as to be spaced apart by an arbitrary distance between the areas 1 and 2 without following a value.

10 is a conceptual diagram illustrating another embodiment of calculating the distances of the first and second regions.

Referring to FIG. 10, the region control unit 810 sets the second region 92 to the xy coordinate plane in a state where the center of the first region 91 is set as an intersection point of a virtual xy coordinate plane. It may be displayed outside the perimeter of the first region 91 corresponding to the first quadrant. In this case, the angle formed by the center of the first and second regions 91 and 92 may be calculated by the following Equation 2.

Equation 2.

는 상기 제1,2 영역의 중심을 연결한 선이 x축과 이루는 각(degree),

은 제1,2 영역의 중심이 이루는 기준 각(40 내지 50°, 바람직하게는 45°),

는 보정치로서 10<

<20(바람직하게는 15), d는 x-y 좌표면을 기준으로 제1 영역의 최고점과 인접한 상부 측의 제1 영역의 최저점 간 y축 거리,

는 기준 y축 거리, r은 제1 영역의 반지름일 수 있다.here,

Is the angle that the line connecting the centers of the first and second regions forms with the x-axis,

Is the reference angle (40 to 50°, preferably 45°) formed by the center of the first and second regions,

Is 10 <

<20 (preferably 15), d is the y-axis distance between the highest point of the first area and the lowest point of the first area adjacent to the xy coordinate plane,

Is a reference y-axis distance, r may be a radius of the first region.

If the reference y-axis distance is smaller than the y-axis distance of the first and second areas by tanh, it is treated as a positive value between 0 and 1, and if the reference y-axis distance is greater than the y-axis distance of the first and second areas, -1 and 0 At the same time, it can be processed to converge to 1 or -1 as the distance difference is larger while processing as a negative value between.

That is, the radius of the first region may be treated as a denominator so that the tanh value is not easily converged to -1 or 1, and as a result, a correction value between 10 and 20 does not deviate from the first quadrant (for example, 30 degrees). The above angle) will be able to calculate the final angle.

As described so far, the configuration and operation of the table of contents memorization learning system in which an image is applied to a book cover according to the present invention are expressed in the above description and drawings, but this is merely an example and the spirit of the present invention is the above description and drawings Not limited to, various changes and modifications are possible within the scope of the technical idea of the present invention.

100: Table of contents input module 200: Image processing interface
210: keyword extraction module 220: image database
230: image extraction module 300: book cover application module
400: Table of contents classification module 500: Table of contents application module
600: composite image application module 700: sub-theme image application module
800: System image application module 900: Association degree identification module

Claims (12)

As a table of contents memorization learning system that applies an image to a book cover,
A table of contents input module that receives a table of contents including a major topic from an author;
A keyword extraction module that extracts any one of the words and phrases constituting the table of contents for each of the major topics and sets them as keywords, an image database containing images for expressing the keywords, and reminiscent or referring to the meaning of the keywords An image processing interface including an image extraction module for extracting images from the image database;
The table of contents subject memorization learning system applying an image to a book cover; characterized in that it consists of; a book cover application module for arranging the images extracted for each major subject in a state other than the table of contents and displaying them on the book cover. According to claim 1,
The book cover above,
Characterized in that at least one of the front cover, back cover, strip of the book, the table of contents subject memorization learning system applying an image to the book cover. According to claim 1,
The book cover application module,
And a sequence number display unit for displaying the sequence number of the major topics according to the order of the table of contents on one side of each image. According to claim 1,
The system,
A table of contents classification module that classifies the table of contents into a plurality of blocks based on a main topic;
In the table of contents of the book, a table of contents application module for displaying the image corresponding to the main topic in or on one side of the block; a table of contents memorization learning system applying an image to a book cover. According to claim 1,
The table of contents input module,
At least one sub-category, the sub-category, is input from the author,
The book above,
In the pages containing the content related to a specific major topic, there is a sub-topic table of contents page in which the major topic and its sub-categories sub-topics are displayed in text,
The image processing interface,
And extracting from the image database a sub-topical image reminiscent or referring to the sub-topical keyword that extracts any one of the words and phrases constituting the sub-topics for each sub-top topic,
The system,
And a composite image application module for generating a composite image obtained by synthesizing the image of the main subject and the image of the subtopic and displaying the composite image on one side of the page of the table of contents of the subtopic. Subject memorization learning system. According to claim 1,
The table of contents input module,
At least one sub-category, the sub-category, is input from the author,
The book above,
The main subject and sub-categories sub-subjects are displayed as text in pages containing contents related to a specific main subject, and the sub-subject includes a schematic diagram connected by a schematic line based on the main subject. We have a table of contents page for subtopics,
The image processing interface,
And extracting from the image database a sub-topical image reminiscent or referring to the sub-topical keyword that extracts any one of the words and phrases constituting the sub-topics for each sub-top topic,
The system,
In the system diagram, a system image application module for displaying the image related to the main subject on one side of the main subject, and displaying the image of the sub subject related to the sub subject on one side of the main subject. A table of contents memorization learning system that applies images to book covers. The method of claim 5 or 6,
The system,
And a sub-subject image application module that displays the sub-subject image on one side of at least one page among pages containing content related to the main subject. The method of claim 6,
The table of contents input module,
Priority is input for a plurality of the above sub-themes from the author,
The system image application module,
And a size variable unit for differentially processing the size of the sub-theme image according to the priority of the sub-theme, a table of contents subject memorization learning system applying an image to a book cover. The method of claim 8,
The table of contents input module,
Writer's comments for each of the sub-topics according to the high and low of the priorities are input,
In the schematic diagram, the area where the soju agent is displayed is,
A table of contents subject memorization learning system using an image on a book cover, characterized in that it is divided into a first area displaying the subdivision image and a second area displaying the author's comment as being located around the first area. The method of claim 9,
The system,
Also includes a module for determining the degree of association between two words as a numerical value,
The system image application module,
An area controller for differentially controlling the distance between the center of the first area and the center of the second area according to the height of the association between the sub-topic keywords and the author by the association degree determining module;
And an area overlapping unit for determining whether to overlap the first and second areas according to the distance control of the first and second areas and deleting and processing the closed curve of the overlapping area. Learning system. The method of claim 10,
The region control unit,
The first and second areas are processed as virtual concentric circles of an area that can contain each of the subject images and the artist's comments,
A table of contents subject memorization learning system using an image on a book cover, characterized in that the distance between the center of the first region and the center of the second region is calculated through Equation 1 below.
Equation 1.

(here,

Is the distance between the center of the first region and the center of the second region,

Is a distance set by default to the center of the first region and the center of the second region, r is the radius of the first region, c is the degree of association between the subject keyword and the author. However, 0<c<1,

Is the distance correction value) The method of claim 11,
The region control unit,
In the state where the center of the first region is set as the intersection point of the orthogonal axis of the virtual xy coordinate plane, the second region is displayed outside the perimeter of the first region corresponding to the first quadrant of the xy coordinate plane,
A table of contents memorization learning system using an image on a book cover, characterized in that an angle formed by the center of the first and second regions is calculated by the following Equation 2.
Equation 2.

(

Is the angle that the line connecting the centers of the first and second regions forms with the x-axis,

Is a reference angle (40 to 50°) formed by the center of the first and second regions,

Is 10 <

<20, d is the y-axis distance between the highest point of the first area and the lowest point of the first area adjacent to the xy coordinate plane,

Is the reference y-axis distance, r is the radius of the first region)

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