RU2464631C2 - System and method for automatic measurement of line height, size and other parameters of international font - Google Patents

Abstract

FIELD: information technology.

SUBSTANCE: method of displaying text in display medium with change in font size is realised by determining character density of the font, dividing the font contrast value by character density to obtain the fraction of the font size. Further, that fraction is added to the font size to adjust line height and text with adjusted line height is displayed in said display medium.

EFFECT: automatic determination of readability characteristics, such as line height, font size and line length, so that text can be displayed on any display in any language and font of any size without manual setup.

7 cl, 27 dwg, 1 tbl

Description

FIELD OF TECHNOLOGY

The invention relates generally to fonts, alignment of text and characters, more specifically to a system and method for automatically measuring the parameters of an international font (for texts in different languages).

BACKGROUND OF THE INVENTION

“They said: Let us build a city and a tower, from the top of which you can reach heaven; and let us make our names known before we are scattered abroad in all countries. ” Pretty soon, work began in full swing; “And they used bricks instead of stones and sludge (bitumen) instead of mortar.” But God confused their language so that they did not understand each other's speech, and thus scattered them from that place to all countries, and they stopped building the city. Genesis 11: 1-9. Since the fall of the Tower of Babel, finding ways to communicate between different languages and their associated writing systems has been and remains a difficult problem.

With the beginning of globalization, the task of solving this complex problem has become central. However, there are complex cultural differences between nations that prevent the creation of a fully integrated global society. In the computer industry, these differences cause problems with international cooperation due to limitations in displaying data and exchanging data in different languages.

One of the steps to solve this complex problem by the multinational computing industry was the creation in 1988 of the Unicode Consortium, which developed a global standard for identifying characters. The task of the consortium was to develop a standard that allows uniquely identifying characters for each language. The consortium has developed the Unicode Standard, currently version 2.1, which is available from Addison-Wesley Developers Press 1997 (available at http://www.unicode.org).

Unfortunately, the ability to print and display characters from one of many languages is only a small step towards solving this complex international problem. The problem is the same, and sometimes more important, is the formatting of the document, the characteristics of the fonts and the general requirements for the text, which make the text readable. However, headsets and fonts of existing languages are different, eclectic and do not obey the same rules. For example, a typical English font, Times New Roman, derives from a typographic formula that is based on a unique Latin font in which the line height is typically set to 120% of the font size in points. The terminology is based on the direct font. The concept of direct font (Roman) includes the baseline, the height of the uppercase letters, the height of the superscript element, the height of the subscript element and the height of the line.

Written languages do not follow the same rules for font characteristics as standard string length or typeface. Rather, each language and handwritten font originated on different cultural bases. For example, some Asian handwritten fonts use special characters (glyphs) that evolved from drawings, while other Asian handwritten fonts are read from right to left and emerged from characters. Even within the same language, handwritten and printed fonts do not adhere to a predetermined characteristic formula. Finding a formula to determine the standard characteristics of a font seems an impossible task. Instead of looking for a formula that matches all languages, graphic designers rely on visual changes in line length, font and line height, changing each font so that the design is pleasing to the eye and meets the legibility requirements known to the design artist.

There is currently no solution for characterizing a font that can be applied to all known fonts. Each font arose from its culture, and they have different bases and completely different concepts. Even if the formula works for a particular font and a specific language, there is no linear relationship between many font characteristics within each font. For example, any changes in font size or line length in the same font require changes in line height, which are taken into account for readability. Designers usually visually change the length of the string, the typeface, and the height of the string. Longer font lines require a larger line height for readability. Also, if the font size is related to the line height, a larger font does not require the same line height as a smaller font.

Since there are no linear or other obvious relationships between line height, line length, and font size, many computer applications require manual changes. Further complicates the placement of text (layout) is the fact that graphic designers do not know on which device the font will be played due to the abundance of computers for reproduction. A printed page intended for use as a web page, for example, can be played on a screen of any size, leaving unknown the optimal reading values for line length, line height, and font size. What is needed is a method for automatically detecting such characteristics that currently require the presence of an artist-designer. What is needed is an automatic method and a system that can calculate readability parameters, such as line height, font size and line length so that text can be played on any display, in any language and font of any size without manual adjustment.

SUMMARY OF THE INVENTION

Accordingly, the system and method automatically determines the appropriate font characteristics for various display means and various readability parameters in any language. One of the embodiments is intended for a method for determining font characteristics by receiving data that includes a font identifier and a language identifier, creating a characteristic text string in an identified language and an identified font, measuring characteristics of a characteristic text string and normalizing measurement results for a plurality of fonts and a plurality languages. More specifically, measuring the characteristics of a characteristic line of text may include measuring the ratio of the number of black pixels to the total number of pixels in a plurality of rows of pixels in a characteristic line of text, identifying the row with the highest ratio of the number of black pixels to the total number of pixels, and using the identified series to determine characteristics font in a characteristic line of text compared to many fonts and languages.

In one embodiment, measuring characteristics of a characteristic font string further includes identifying the font height and, within the font height, identifying the first part, which includes the row with the highest ratio of the number of black clusters to the total number of clusters, the first part including the first row of pixels and the last row of pixels, which are darker than the average value of the row of pixels, the identification of the second part located above the first part, and the identification of the third part, p subjacent below the first part. In the discussion that follows, these parts are referred to as dimensions associated with the term “black river,” which is a term used to identify printed measured characteristics to determine the average gray level in the direction perpendicular to the direction of reading. Medium gray allows comparison of many fonts and languages.

In one embodiment, an international translation tool is used to receive data that includes a font identifier and a language identifier, and the received data may include text in any language. The language identifier may include one or more languages into which the international translation tool translates the text.

Another embodiment is intended for a method for placing text using an international font, in which text placement refers to a line of text that is reproduced in a font whose size is determined in terms of skittle (round) spacing, this method uses the measured characteristics of a normalized characteristic line of an international text typed by a predetermined font, to determine the optimal font size, line height, number of columns and line length. The characteristics of the normalized characteristic line of the “international” text provide the number of clusters in one fixation of the gaze and the number of clusters per bowling.

An embodiment intended for an international text placement system printed in one of the fonts includes an input structure and placement filter configured to read one or more text parameters, a metric reader attached to the data structure, and the metric reader for receiving one or more text parameters, and a text parameter generating means attached to the indicator reading means. The means for generating text parameters includes a module configured to determine the font graphic density, which is the average gray level in reproduced text for a given font in an identified language. Features (characteristics) of the font, determined by the means of forming text parameters, include values representing the number of clusters per skittle, font saturation, font width, width of the darkest horizontal part of the font, and saturation of the darkest horizontal part of the font. Another system according to one embodiment includes an international translation tool configured in a hosting tool and connected to the application by an application program. The international translation tool receives data from the application and ensures that the translated text is transmitted to the application with the appropriate placement characteristics of the translated text. In one embodiment, the international translation tool is configured to receive one or more language and text identifiers from the application and to provide the translation of the translated text and to determine the parameters necessary for the input filter of the structure and placement to create one or more data structures that act with the means of reading indicators and means of forming text parameters. The international translation tool may be an external international translation tool invoked from the application. An application can be a server or a client machine and display translated text according to language identifiers with readability parameters corresponding to any display that the application calls. The application may also be an Internet application available for multiple computers, the application receiving requests for transmitting translated text and transmitting the translated text, respectively formatted for each display of each of the plurality of computers, and in a language that requires each of the plurality of computers.

Additional features and advantages of the present invention will be apparent from the following detailed description of the shown embodiments, which is made with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Although the appended claims specifically state the features of the present invention, the invention, together with its objectives and advantages, can be best understood from the following detailed description, taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram generally illustrating an exemplary computer system in which the present invention is implemented.

FIG. 2 is a block diagram illustrating an example data stream within a computer system in accordance with one embodiment of the present invention.

FIG. 3 is a more detailed block diagram illustrating the data flow shown in FIG. 2, in accordance with one embodiment of the present invention.

FIG. 4 is a flowchart illustrating a method for characterizing an international text in accordance with one embodiment of the present invention.

FIG. 5 is a flowchart illustrating in more detail a method for characterizing an international text in accordance with one embodiment of the present invention.

FIG. 6 is a flowchart illustrating in more detail a method for characterizing an international text in accordance with one embodiment of the present invention.

FIG. 7 shows two images representing graphs and text resulting from the application of methods in accordance with one embodiment of the present invention.

FIG. 8 is a flowchart illustrating a method for automatically determining line height and font size in accordance with one embodiment of the present invention.

FIG. 9A illustrates a graph resulting from a method for characterizing a font in accordance with one embodiment of the present invention.

FIG. 9B illustrates another graph resulting from a method for characterizing a font in accordance with one embodiment of the present invention.

FIG. 10 is a flowchart illustrating a method for determining font size in accordance with one embodiment of the present invention.

FIG. 11 is a graph illustrating a curve corresponding to characterization of text as a function of viewing distance in accordance with one embodiment of the present invention.

FIG. 12 is a graph that illustrates how the area of sharpest focusing is determined in accordance with one embodiment of the present invention.

FIG. 13 is a graph that illustrates how trigonometric functions are used to determine the area of sharpest focus in accordance with one embodiment of the present invention.

FIG. 14 is a flowchart illustrating a method for automatically determining a minimum and maximum font size in accordance with one embodiment of the present invention.

FIG. 15 is a block diagram illustrating a data stream that is used to determine font size in accordance with one embodiment of the present invention.

FIG. 16 is a block diagram illustrating a data stream that is used to characterize text in accordance with one embodiment of the present invention.

FIG. 17 shows an example of the same font for different languages, showing a change in their width in accordance with one embodiment of the present invention.

FIG. 18 is a flowchart illustrating a method for automatically scaling a font size in accordance with one embodiment of the present invention.

FIG. 19 is a flowchart illustrating a method for adjusting row height based on a function of the number of clusters per row, in accordance with one embodiment of the present invention.

FIG. 20 is a graph illustrating empirically found possible line heights for a given character size in points in accordance with one embodiment of the present invention.

FIG. 21 is a flowchart illustrating a method for determining a change amount of a default row height in accordance with one embodiment of the present invention.

FIG. 22 is a block diagram illustrating a data stream within a metric reader in accordance with one embodiment of the present invention.

FIG. 23 is a graph illustrating determination of line length in accordance with one embodiment of the present invention.

FIG. 24 is a block diagram illustrating a data stream using an international translation facility located in a computer system in accordance with one embodiment of the present invention.

FIG. 25 is a flowchart illustrating a method of using a translation tool in combination with embodiments in accordance with one embodiment of the present invention.

FIG. 26 is a flowchart illustrating a method of using a translation tool located in a host means in accordance with one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Turning to the drawings, in which like reference signs refer to like elements, the invention is illustrated as being embodied in an appropriate computing environment. Although not required, the invention is described in the general context of computer-executable instructions, such as program modules, being executed by a personal computer. In general, program modules include routines, programs, tasks, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, specialists should recognize that the invention can be used with other computer system configurations, which include handheld computers, multiprocessor systems, programmable or microprocessor-based consumer electronics, network PCs, minicomputers, universal computers, etc. The invention can also be used in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

FIG. 1 illustrates an example of a suitable computing system environment 100 in which the invention may be practiced. The computing system environment 100 is only one example of an appropriate computing environment and does not impose any limitation on the forms of use or operation of the invention. Similarly, computing environment 100 should not be interpreted as having any dependency or requirements relating to one or a combination of components illustrated in typical processing environment 100.

The invention works with a variety of other universal or special computing system environments or configurations. Examples of known computing systems, environments and / or configurations that can be used with this invention include personal computers, servers, handheld or laptop computers, multiprocessor systems, microprocessor based systems, set top boxes, programmable consumer electronic devices, network PCs, minicomputers , universal (large) computers, distributed computing environments that include, but are not limited to, any of the above systems or devices, etc.

The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. In general, program modules include routines, programs, tasks, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention can also be applied in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media, which include storage devices.

Turning to FIG. 1, an exemplary system for implementing the invention includes a universal computing device in the form of a computer 110. Computer 110 may include the following components: a processor 120, a system memory 130, and a system bus 121 that connects various system components that include system memory , with a processor 120, but its composition is not limited to them. The system bus 121 may be any of several types of bus structures, which includes a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. For example, but not by way of limitation, such an architecture includes an industry standard (ISA) architecture bus, microchannel architecture bus (MCA), an extended standard architecture bus for industrial applications (EISA), a local bus of the Video Electronics Standards Association ( VESA) and a peripheral connection bus (PCI), also known as an expansion bus.

Computer 110 typically includes a variety of computer readable media. Computer-readable media can be any available media that can be accessed by computer 110, and they include both volatile and non-volatile media, removable and non-removable media. By way of example, but not limitation, computer-readable media may include computer storage media and communication media. Computer storage media includes both volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information, such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes: random access memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or memory made using another technology, compact discs (CD-ROM), digital multifunction discs (DVDs) or other optical disk storage devices, magnetic tapes, magnetic tape, magnetic disk storage devices or other magnetic storage devices or any other medium that It can be combined to store the desired information and which can accessed by computer 110, but are not limited to. The communication medium typically embodies computer-readable instructions, data structures, program modules or other data into a modulated data signal, such as a carrier or other transport mechanism, and includes any means of information delivery. The term “modulated data signal” means a signal that has one or more of its characteristics that are set or changed in such a way as to encode information in the signal. By way of example, and not by way of limitation, a communication medium includes wired communication channels, such as wired networks or a direct wired connection, and wireless communication channels, such as acoustic, radio frequency (RF), infrared, and other wireless communication channels. Computer-readable media should also include combinations of any of the above media.

System memory 130 includes computer storage media in the form of volatile and / or non-volatile memory, such as read-only memory (ROM) 131 and random access memory (RAM) 132. A base input / output system (BIOS) 133 comprising basic routines that help move information between elements in computer 110, for example, during startup, it is usually stored in ROM 131. OP 132 typically contains data and / or program modules that are instantly available for processing and / or are currently being processed by processor 120. By way of example, and not by way of limitation, FIG. 1 shows an operating system 134, application programs 135, other program modules 136, and program data 137.

Computer 110 may also include other removable / non-removable, volatile / non-volatile computer storage media. By way of example only, FIG. 1 shows a hard disk drive 141 that reads information or that writes information to a non-removable non-volatile magnetic medium, a magnetic disk drive that reads information or writes information to a removable non-volatile magnetic disk 152, and an optical disk drive that reads information or writes information onto a removable non-volatile optical disc 156, such as a compact disc (CD-ROM) or other optical media. Other removable / non-removable, volatile / non-volatile computer storage media that can be used in a typical environment include: magnetic tape cassettes, flash memory cards, digital multifunction disks, digital video tape, semiconductor random access memory, semiconductor ROM, etc. ., but not limited to them. The hard disk drive 141 is typically connected to the system bus 121 via a means for interfacing with a non-removable storage device, such as a pairing means 140, and the magnetic disk drive 151 and an optical disk drive 155 are typically connected to the system bus 121 via a means for interfacing with a removable storage device, such as interface means 150 (interface).

The devices and associated computer storage media described above and shown in FIG. 1 provide storage of computer readable instructions, data structures, program modules, and other data for computer 110. FIG. 1, for example, a hard disk 141 is shown as a storage device for an operating system 144, application programs 145, other program modules 146, and program data 147. It should be noted that these components may be the same or may differ from operating system 134, application programs 135, other program modules 136 and program data 137. The operating system 144, application programs 145, other program modules 146, and program data 147 are assigned various designations to indicate that they are at least different copies. The user can enter commands and information into the computer 110 through data input devices, such as a keyboard 162 and a positioning device 161, commonly referred to as a “mouse”, trackball (trackball), or touch pad. Other data input devices (not shown) may include a microphone, joystick, gaming keyboard, satellite dish, scanner, or the like. These and other data input devices are often connected to the processor 120 via input user interface means 160 (input user interface), which is connected to the system bus, but can be connected using another interface and bus structures, such as a parallel port, a game port, or universal serial bus (USB). A monitor 191 or other type of display device is also connected to the system bus 121 via an interface (interface), such as a video interface 190. In addition to the monitor, computers can also include other peripheral output devices, such as speakers 197 and a printer 196, which can be connected through a means of 195 interface with external output devices.

Computer 110 may operate in a network environment using logical connections to one or more remote computers, such as remote computer 180. Remote computer 180 may be another personal computer, server, router, network PC, peer network device, or other normal network node, and typically includes many or all of the elements described above with respect to the personal computer 110, although only the storage device 181 is shown in FIG. 1. The logical connections shown in FIG. 1 include a local area network (LAN) 171 and a wide area network (WAN) 173, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, corporate networks (intranets), and the Internet.

When working in a LAN environment, the personal computer 110 is connected to the LAN 171 via a network interface (network interface tool) or adapter 170. When operating in a global network environment, computer 110 typically includes a modem 172 or other means of establishing communication with the global network 173, such as the Internet. The modem 172, which may be internal or external, may be connected to the system bus 121 via a user input interface 160 or other appropriate mechanism. In a networked environment, program modules depicted relative to a personal computer 110 or portions thereof may be stored in a remote storage device. By way of example, and not by way of limitation, FIG. 1 shows that the remote application programs 185 are located in the storage device 181. It should be recognized that the network connections shown are exemplary and other means of establishing communication between computers can be used.

In the following description, the invention is described with reference to actions and symbolic representations of operations that are performed by one or more computers, unless otherwise indicated. It is also understood that such actions and operations, which are referred to from time to time as performed by a computer, include processing by the computer processor of electrical signals representing data in a structured form. Such processing converts data or stores it in the cells of the computer’s memory system, which reconfigure or otherwise change the computer’s operation in a way that is well understood by specialists. The data structures where the data is stored are physical memory cells that have specific properties defined by the data format. However, although the invention is described in the above context, it should not be a limitation, as those skilled in the art will recognize that the various acts and operations described below can also be implemented in hardware.

In accordance with one important aspect of the invention, reference is made to FIG. 2, which is a block diagram of client software, such as a Windows® client, suitable for implementing embodiments of the present invention. The block diagram shows an application program 200, which may be any application program running on the computer 110 shown in FIG. 1. Relevant applications include, for example, Microsoft Word, Microsoft Publisher, QuarkXPress, Adobe InDesign, and the like. The application 200 shown includes a property sheet 210. The property sheet 210 interacts with the placement tool 202, which may reside in the computer 110 or may be accessible through a client-server connection. The host means 202 may be a client application running on computer 110, or may be accessible through a network connection. The property sheet 210, located in the application 200, provides property values for the placement means 202, which are received in the structure and placement input filter 230. In addition to the input filter 230 structure and placement, the means 202 placement also includes means 240 for reading indicators (metrics) and means 250 for generating text parameters. The property sheet data structures include those elements that are required by the placement tool 202 to provide appropriate placement for rendering fonts and text for the application program 200. The structure and placement input filter 230 generates elements for input to the metric reader 240 and provides input properties to means 240 reading indicators. The metric reader 240 processes the properties and sends the data to the text parameter generator 250. The text parameter generating means 250 receives data, which includes identifiers for the font typeface and identifiers for the language, and outputs the calculated data related to the text characteristics intended for use by the indicator reading means 240.

As described in more detail below, the metric reader 240 processes the input values to provide optimal typographic settings within the constraints given in the calling applications. Entry restrictions may include, but are not limited to, environmental restrictions and design restrictions. Environmental restrictions may include magnification, screen size and resolution, optical magnification for the reader, and distance to the reader. Design restrictions may include font requirements, margin requirements, column requirements, and placement rectangle requirements. Given these limitations, a metric reader 240 provides transmission of settings that include parameters for a window, page, and actual set sizes, margin sizes, optimal number of columns, optimal column and gutter sizes, and optimal font size and line height. The transmitted settings can be measured in pixels or in points according to design requirements. Advantageously, the settings output from the metric reader 240 correspond to known languages and fonts.

Turning now to FIG. 3, the block diagram shown in FIG. 2, expanded in the form of a block diagram to illustrate the flow of information in a data structure. FIG. 3 shows a property sheet 210, which includes elements such as page size 312, page margins 314, font family 316, font size 318, height 320 lines, number 322 columns, width 334 columns, and sizes 326 pages. Also, the property sheet 210 includes those values that cannot be changed. These are the values that are configured using the module 329, and those values that are not transmitted to the input filter 230 structure and placement. More specifically, the structure and placement input filter 230 includes a feature unit 330 and text stream detection means 352. Element module 330 generates data in a data structure. A typical data structure shown includes a font family 332, a language 334, a change in font size 336, a change in line height 338, page margins 340, a page margin range 342, and a preferred number of columns 344. Data is generated in the module 330 elements and transmitted as input properties to the means 346 reading indicators. In turn, the text stream detection means 352 provides a window size 354 transmission to the metrics reading means 240. Alternatively, the text stream detection means 352 may be a network-connected component or a system component in a computer 110. In one embodiment, the placement means 202 interacts with a interaction level 360 with a display environment. The level 360 of interaction with the display medium provides the transfer of size 362 of the display medium to the means 240 of reading indicators. One example of the size 362 of the display medium includes the display size of the computer 110 shown in FIG. one.

The metric reader 240 embodies embodiments of the present invention, as described in more detail below. In one embodiment, the metric reader 240 interacts with the text parameter generator 250 and the element module 230. The module 230 elements contains the input properties 346 for reading indicators. Element module 230 is connected to text stream detection means 352. In one embodiment, the element module 230 and the text stream detection means 352 together form one component that includes a structure located in the element module 230 and the arrangement located in the text stream detection means 252.

The metric reader 240 receives the input information and processes it to generate settings used to place text in the calling application. The settings can take into account user settings for sizing, or they can automatically determine the default sizes. Means 250 for generating text parameters is an important feature of the placement means 202. Means 250 for generating text parameters provides input to means 240 for reading metrics that add language-specific data related to text parameters. The text parameter generator 250 receives the font family identifier 364 and the language identifier 366. The output from the text parameter generator 250 includes the measured values calculated in the text parameter generator 250. More specifically, as explained in more detail below with reference to FIG. 5, the text parameter generating means 250 measures the ratio of black and white pixels in each row of pixels in a reproduced font string and provides the transmission of these measurement results using terms that identify the characteristics of a characteristic text string for a given font and a given language. These results 368 measuring the parameters of the text are transmitted to the means 240 reading indicators. The metric reader 240 uses equations and other calculations to the measurement results and input property values 346 to provide output properties 380, which include 382 page size, 384 page margins, 386 font family, 388 font size, 389 line height, 390 distance between columns, image width 392, column width 394, number 396 columns, and paragraph indent 398. These properties are returned to the calling application 200.

Turning to FIG. 4 in combination with FIG. 3, a method for determining text placement properties according to one embodiment is shown as a flowchart. The method relates to functions that are performed in the indicator reading means 240 and the text parameter generating means 250 shown in FIG. 2 and 3. More specifically, at block 410, data is received about the environment (equipment) to host the international text. As shown in FIG. 3, the data may include elements from one or more modules: statements 210 properties, means 252 detect text flow and means 250 for generating text parameters. Environment data includes data related to the visualization environment, such as screen size, paper size, etc., as well as user-defined or system-required parameters related to field sizes, font size, columns, line height, line length and language. At 420, data is processed using the measured characteristics of a normalized characteristic string of international text printed in a predetermined font and the measured environmental characteristics. The characteristics of the normalized characteristic line of the international text are measured in the tool 250 of the formation of the text parameters. The indicator reading means 240 may be connected to the text parameter generating means 250, or it may call the text parameter generating means 250 through an application program interface (API, API). The characteristics of the medium are measured in a means 240 reading indicators. After receiving the measured characteristics of the normalized characteristic line of the international text, the indicator reading means 240 performs many operations on the received calculated data. Information from the output of the indicator reader is transmitted to the calling application 200.

The operation of the text parameter generating means 250 is described in more detail as a sequence of operations in FIG. 5 and 6. As shown, the text parameter generating means 250 receives the font family identifier 364 and the language identifier 366 in step 510. At step 520, the text parameter generating means measures the characteristics of the characteristic font string in the identified language and for the identified font. Mostly, the measurement results provide the opportunity to objectively determine the font size and line height for any given language and font for any given location. At step 530, the text parameter generating means 250 normalizes measurements across a plurality of fonts and languages. The measurement results are then used to determine the appropriate sizes.

For the purposes of this disclosure, the term “dimensions” includes, but is not limited to, the height of the line, which is the distance from the reference line of one line of the font to the reference line of the next line. The reference line is the visible line on which the characters in the recording system are located or with which they “hang”. For English characters, the reference line is an imaginary line that contains upper and lower case characters. The height of the lowercase letters (x is the height) (without extension elements) refers to the standard height of the lowercase letter characters, approximately equivalent to the height of the lowercase character x (lowercase letter x) for a given font. The height of the uppercase letters refers to the height of the uppercase letters in the font line, it is not equal, but often equal to the height of the superscript element. In some fonts, superscripts are taller or shorter than the height of most capital letters. Superscript element - parts of lowercase letters l, t, f, b, d, h, k that are higher than the height of lowercase letters without extension elements. A subscript refers to parts of lowercase g, j, p, q, y that are below the reference line.

Skittle spacing is a unit of measure equal to the size of the font. Thus, skittle spacing is a variable unit of measure that changes whenever the font size changes. The skittle spacing for the purposes of this disclosure is the square (skittle pad) of the measured font. For example, if a font is represented by a font of 12 points, then skittle spacing is 12 points high and 12 points wide. If the font is represented by a font of 100 points, then skittle spacing is 100 points high and 100 points wide. Therefore, skittle spacing is used as an uncharacteristic unit for measuring font size, and it is based on how the font is designed, and not on its physical characteristics. As a result, some font drawings may have a printing ink that is outside the skittle spacing, for example in Thai, while others take up significantly less space than the skittle spelling, for example, in Korean. A “cluster”, for the purposes of this disclosure, refers to a playitem of an analyzed font and text. More precisely, a cluster is printed content between points where the cursor can be inserted in a line of electronic text. The term “cluster” is a general term because it is defined by the absence of spaces in the line, and not by keystrokes, symbols or special characters (glyphs) and therefore corresponds to all languages. In Latin fonts, a cluster and a character can be equivalent.

Turning now to FIG. 6, the method of FIG. 5 is described in more detail. The methods described in FIG. 6 provide a numerical characteristic of the gray level for any type of text printed in any font and in any language. The numerical characteristic is based on what the reader sees when the font is placed on the page, that is, on the set of dark lines along which the eye follows during reading. Since the size of these lines and the space between them vary with different recording systems, font types, languages and cultures, the measurement results should correspond to any recording system in any language. The place covered by ink (printing ink) in the line of text in this work is called the “black river”. More precisely, the concept of “black river” refers to the entire place occupied by ink in one line of text for a given font and language, which includes the highest and lowest points filled with ink in a characteristic line of the font.

The methods by which the “black river” is measured are described in FIG. 6. After receiving the font family identifier and the language identifier, as shown in step 610, the text parameter generating means 250 measures the ratio of the number of black pixels to the total number of pixels in a plurality of rows of pixels in a characteristic font string. In one embodiment, the text parameter generator 250 measures the ratio of the number of black pixels to the total number of pixels in each row of pixels in a rendered font string. After each row of pixels that contains black pixels has been measured, the row with the highest ratio of the number of black pixels to the total number of pixels is equal to black, as shown in step 620. The measurement results of all other rows are normalized to this black point . Rationing allows equivalent measurements of font parameters with different saturations, since the black dot of the font can be normalized for all fonts. For example, the darkest part of the “black river”, measured at 1/200 of the skittle, is believed to be 100% black, regardless of what degree of blackness it actually has. All other parts of the “black river” are measured for gray intensity as a value between 0% (white) and 100% (normalized black, or the darkest part of the “river”). The average ratio is calculated, and then the first row of pixels and the last row of pixels, which are darker than the average, are marked.

At step 630, font height is identified using the measured ratios of the number of black pixels to the total number of pixels. At 640, the text parameter generation tool uses the font height and identifies the first part, which is called the “channel” in this paper, which includes the row with the highest ratio of the number of black pixels to the total number of pixels, the first part including the first row of pixels and the last row of pixels, which are darker than the average value of the row of pixels. In Latin texts, the “channel” is equated to the height of lowercase letters without external elements. In other fonts, there is no direct equivalent to the height of lowercase letters without extension elements. Since each font in each language rendered in a line of text will have a dark part in a line of text, instead of the height of lowercase letters without external elements, in the embodiments, the term “channel” and the measurements relating to the “channel” are used. In many of the described embodiments, the “channel” is an important measurement for determining the appropriate line height and other settings for a line of text printed in a given font. At 650, the text parameter generating means 250 identifies a second part located above the first part. At step 660, the text parameter generating means 250 identifies a third part located below the first part. The second part and the third part in this work are called “pre-shallow” and “after-shallow”, respectively, and collectively - “shallow”. Most fonts usually have a “shallow” on either side of the identified “channel”. The “shallow” is the area where the font line has ink, but not every character extends (protrudes) into it. In the Latin script, there is a “shallow”, which is equivalent to the superscript element and the subscript element. A language with a large number of stress marks typically has a “shallow”, which includes stress marks. A language that is substantially homogeneous can be identified as having a “channel” with little or no “shallow”. Many Asian languages can be identified as having only a “channel” and not having a “shallow”. These languages have a “river” and a “channel,” which are the same size.

At step 670, the text parameter generation tool 250 creates a graph of the measurement results to identify alignment points, dark peaks, and the like.

After identifying the “channel” and “shallows”, step 680 determines the average value of the ratio of the number of black pixels to the total number of pixels in each part. These parts describe a line of text for a particular font, providing the results of three measurements, which include the width of the “channel”, the width of the “pre-shallow”, and the width of the “after shallow”.

In one embodiment, the means for generating text parameters 250 returns to the means 240 of reading indicators the measurement results, which include one or more of the measurement results: the ratio of the number of black pixels to the total number of pixels in the darkest row of pixels, the average value of the ratio of the number of black pixels to the total number of pixels in the entire characteristic line of the font, the width of the “pre-pitch”, the ratio of the number of black pixels to the total number of pixels in the “pre-pitch”, the width Channel ", the ratio of black pixels to total pixels in a" channel "width" posleotmeli "and the ratio of black pixels to the total number of pixels in the" posleotmeli ". When words in a certain language are written vertically, for example, as in some Asian languages, the same measurements can be made by measuring the columns of vertical text for a language with vertical spelling instead of measuring a row of horizontal text for languages with horizontal spelling. Professionals should understand with this disclosure that the principles for characterizing a language with vertical spelling are the same, so that, for example, the “channel” of a font with vertical spelling will be rotated 90 degrees for measurement compared to the “channel” of a font with horizontal spelling.

In other embodiments, optionally, metric reading 240 may return additional measurement results, such as alignment points at a different degree of accuracy than dividing into three parts, or measurement results that correspond to the darkest rows, or peaks, on any graphs created.

Turning to FIG. 7, a photograph of a simulated text with corresponding graphs is shown according to one embodiment. Photo 710 illustrates the serif typeface in Latin, showing that the peaks are generally at the top of the capital letter, at the height of the lowercase letter without extension elements, at the height of the crossbar of the lowercase letter “e”, on the reference (base) line and in the subscript. In comparison, photograph 720 illustrates the shape of a non-Latin font with other and more centered alignment points.

Other returned measurement results may be measured with respect to bowling. In the tool 250 for generating text parameters and in the tool 240 for reading indicators, skittle is used to determine both horizontal and vertical sizes. Another measurement result that can be returned by the text parameter generation tool 250 is the number of clusters per skittle, which may also be the measurement result returned to the metric reader 240. The number of clusters per bowling spacing is equivalent to the ratio of the font height to the average cluster width.

Other measurement results that may be used by the indicator reading means 240 include the width of the “black river” as a real number with respect to skittle; the width of the “pre-shallow” as a real number in relation to the skittle; the width of the “post shallow” as a real number in relation to the skittle; the width of the “channel” as a real number in relation to the skittle; the average saturation of the “channel” and each of the “shallows” as a real number, such that 0 = all white and 1 = all black after normalization; the average number of clusters per skittle and the total number of clusters in a characteristic line of text. The width values can be returned to the metric reader 240 as a real number with respect to the skittle, and the saturation values can be returned to the metric reader 240 as a real number with respect to the normalized black dot. In one embodiment, the total number of clusters is preliminarily stored in a language table, or in a means for reading indicators, or in a means 250 for generating text parameters.

FIG. 8 illustrates an example method in the form of a flowchart showing the interaction between the metric reader 240 and the actions in the text parameter generator 250. At 810, the user marks the document in the application program. This marking includes an indication of the language and font. Indication can occur through a system communication channel or through user input. An indication identifying the language and font is transmitted at 820 to the metric reader 240. In one embodiment, the text parameter generator 250 indicates a font object and an application program interface (API) that metric reader 240 may request for measurement. If the indicator reading means 240 processes the font object in step 822, then the language and font identifiers are transmitted to the text parameter generating means 250. Otherwise, the indicator reading means 240 operates with the language and font without calling the text parameter generating means 250. For example, if the indicator reading means 240 has a table with the necessary measurement results for this font and language, then the call to the text parameter generating means 250 is not needed. Step 828 ensures that, in the text parameter generating means 250, the language and font identifiers are used either by comparing the language and the font with a predetermined table to retrieve data related to the characteristics of the font, or by finding the characteristic text string in the language. If a characteristic text string is found, then the font, or font family, is applied to the language to create, at step 829, a characteristic text string, which may be one text string or another format suitable for measurement. Step 830 provides a means of forming text parameters of the “black river”, “channel” and “shallows”. Step 832 ensures that the text parameter generator 250 then normalizes the black dot and all percentages associated with it. Step 840 ensures that the text parameter generation tool 250 returns width and saturation measurements, the average number of clusters per font size, the total number of clusters in a characteristic font string, and similar measurement results, such as described above.

In one embodiment, the text parameter forming means 250 measures rows of pixels in a characteristic line of text at intervals of 1/200 of the skeleton spacing and writes the ratio of the number of black pixels to the total number of pixels in the characteristic line of text as the average gray level. The measurement results can be written either as a value of 0-255, or as two decimal places of percent. Step 850 ensures that after the measurement results are passed back to the metric reader 240, the metric reader 240 calculates the font size and line height. Step 860 ensures that the user sees the page on any viewing surface with a font that is appropriately sized and positioned. Turning to FIG. 9A, graph 900 illustrates the measured sample after the text parameter generator 250 calculates the measurement results described in FIG. 8. The graph shows the measurement results of the Times New Roman font, such as the normalized version of the graphs shown in FIG. 7. The measurement results shown include the average gray level for each row of pixels, which vary from 0 (black) to 255 (white). Graph 900 illustrates the normalized version, showing the gray level as a percentage. Raw data is normalized on the 900 plot so that the darkest line in the font is considered completely black or 100% and the lightest pixel is 0%. More specifically, graph 900 shows the height of a characteristic line of text along the X axis in 910 points, and the average gray level for each row of pixels along the Y axis 920 in percent. The normalized measurement results are shown by the solid line 902, the absolute measurements of the “channel” and “shallows” are shown by the dashed line 909, and the average value of the gray level is shown by the line 906. Comparison of the graph 710 shown in FIG. 7, with a graph 900, shows alignment points 930 and 940, and also shows a “channel” delineated by the height of lowercase letters without extension elements 940 and a reference line 930 for a given font and language. The intersection of line 906 with the font curve 902 is data point 24, and the next intersection is data point 71. The full height of the font is 91. If the size of the skittle is 100%, then the set of real numbers with respect to the size of the skeleton will be: “pre-pitch” = 0.24; “Channel” = 0.45; “Post shallow” = 0.20 and “black river” = 0.91.

FIG. 9B illustrates a graph 990 with the same font curve 902 with a dashed line 950 showing the average saturation value for each of the “shallows” and “channels”. Percent saturation average values can be used to determine line height values. The returned values may include the average value of the saturation “pre-crib” of 0.099; the average value of the “channel” saturation is 0.61 and the average value of the “post-shallow” saturation is 0.038.

The measurement results from the means 250 for generating text parameters are returned to the means 240 for reading indicators. The metric reader 240 uses measurement results for a variety of purposes.

One of the functions of the metric reader 240 is to determine the default font size for human-readable text in any language. The prior art default font sizes use hardcoded numbers in points or pixels based on Elite and Pica font sizes for typewriters, such as 12 steps or 10 points and 10 steps or 12 points. Means for measuring indicators 240 uses a new approach, determining the appropriate size for text in any given language and for any font based on reading characteristics for specific language and culture and characteristics of the physical visualization of the font, which are determined using the tool 250 of the formation of text parameters and environmental factors. The metric reader 240 may be automatic and scalable, and may use input-dependent culture information and user-configurable input information.

More specifically, the metric reader 240 employs a method according to one embodiment that determines font size as a function of characteristics associated with a display environment and language characteristics. Characteristics of the display environment include determining a default distance from the display environment, which can be changed by the user as necessary.

Turning to FIG. 10, the flowchart illustrates a method for calculating a font size for a default distance from a display. At 1010, the method determines the minimum and maximum default distance from the display using the exponential ratio of the minimum distance from any display. In one embodiment, the minimum distance is set to 12 "and the maximum distance is set to approximately 27". The exponential curve quickly tends to large distances as the display gets larger. The function ad ² + b = D is an exemplary exponential function suitable for determining the default distance. In this function, a represents a constant factor for d ² , and b represents a constant number that is added to ad ² so that a + b = 12, or the minimum distance that a reader can view a page with a font. D and d are the observed points on the graph of the actual reading distance and display size. The lowercase letter d represents the diagonal of the display surface in inches; and D represents the distance from the display surface in inches. In the example, if a and b were set to a = 0.02353 and b = 11.97647 and these points are converted by multiplying by 72, then the exponential curve is set, as shown in the graph in FIG. 11. As shown, the X axis represents the diagonal of the display in common units, and the Y axis 1102 is the distance from the display in common units. Although the graph in FIG. 11 is shown in inches, if the example were converted to other units, it would also be necessary to do a similar conversion of the constants a and b.

Once the default distance to the display is determined, this distance is used by the metric reader 240 to determine the default font size for the predefined font. More specifically, step 1020 enables the use of the trigonometric function of the foveal (central) angle and sign density to determine the font size for a predetermined font that is suitable for reading in any medium. In this paper, the font size for a predefined font is called the font size. The foveal angle is used to determine the area of sharpest focus on the display medium. To determine this area, the function uses the double tangent of half of the foveal angle times the distance from the surveyed surface. In a further embodiment, the foveal angle that is used to determine the distance is determined by entering information about one or more reader-specific foveal angle or using a standard foveal angle of approximately 0.75 degrees. A reader-specific foveal angle may reflect the reader’s visual acuity. For example, a reader-specific foveal angle may take into account one or more of the following: macular degeneration, blurred vision, visual acuity, sharply narrowed field of vision, eye disease, myopia, hyperopia, dyslexia, and astigmatism. To demonstrate foveal angles, in FIG. 12 and 13 are diagrams that show how the area of sharpest focusing is determined. As shown, eye 1202 sees the viewing surface 1204 from a viewing distance 1206. A study of the abilities of the human eye indicates that the foveal angle 1208 of 0.75 degrees is the maximum angle of sharpest focusing of the eye 1202. A demonstration of the method for determining the length 1210 of the sharpest focusing area is shown in FIG. 13. Using trigonometric functions, the length 1210 can be described as the double tangent 1302 from half of the foveal angle 1304. The foveal angle of the human eye, based on various studies, is approximately 0.75 °. The parafoveolar angle is 10-12 °. In addition to the range of vision in which data is perceived but not at the peak of focus, 12 ° is an approximate range of eye movement before inadvertent head movement begins to help position the eye. In one embodiment, it is believed that the optimal line length is a line length of approximately 16 foveal gaze fixations. The number of clusters is culturally dependent. The study of string length in the English language is well documented by both objective research and observations on the development of book design. Studies show that for English, approximately 4 characters of a font are instantly recognized in one foveal fixation of the gaze. Thus, one embodiment uses an estimate of the optimal string length in English of 4 × 16 = 64 clusters per row. Observations show that character sets, which include more information in a cluster than in English, require fewer clusters in commit to speed understanding. Since this is not an absolute relationship, one embodiment uses a table that indicates for languages located one after another how many clusters there will be in each foveal fixation for a given language. The table may be compiled for languages or groups of languages that include, but are not limited to, Latin, Arabic, Thai, Asian, and Indian. Such grouping is done in accordance with the characteristics of recording systems. Any single language may have a different or more optimal meaning.

Returning to FIG. 10, in step 1020, character density is used to determine font size. The density of characters can be determined by dividing the number of clusters per bowling, by the number of clusters in the fixation. The number of clusters per fixation for a font of a particular language is a constant associated with one of the many languages, each of the many languages having a constant representing the information density for reading text in that language. The number of clusters per skittle is a description of the graphic density. Therefore, the symbol density can be described as a function of the graphic density and information density of the symbol. In one embodiment, the implementation of the graphic density and information density depends on the language, which includes the characteristics of reading in this language depending on the amount of information in the area of the sharpest focus, visible to the reader.

In one embodiment, the metric reader 240 includes or has access to a linguistic table of the number of clusters per foveal fixation. The maximum number of clusters recognized in one foveal fixation can be determined by viewing the languages contained in the table or can be entered by a user with access to a metric reader 240. Each language determines how many clusters you can see in 1/16 of the paraveolar angle. Therefore, each language has a definable number of clusters in the paraveolar angle. This value can be used to determine row length, column width, etc. In one embodiment, it is assumed that the number of clusters that can be seen in the row is approximately ten for each language. The number of clusters defined for a given language can be multiplied by a number that represents a comparison between alphabets. Alternatively, determining the number of clusters in a line and dividing this number of clusters by a constant representing the number of fixations of the human eye in this line of text, determine the number of clusters for foveal fixation. The constant for one or more languages can be approximately 16. The number of clusters per fixation is multiplied by the number of foveal fixations in the paraveolar region, and this provides the optimal number of clusters per readable line. The parafoveolar angle is a wider angle than the foveal angle, and in general, this term refers to the angle at which the eye can see the most focused area. In one embodiment, the para-veolar angle is approximately 10 degrees. As described above, the foveal angle is approximately 0.75 degrees and corresponds to two to four characters in the foveal focus area. Using a constant of 16 for the number of fixations of the human eye in a line of text, and multiplying it by the number of clusters in foveal fixation, the number of clusters in a line for each language can be determined, as shown in the table.

The number of clusters in the fixation (C _pf ) The number of commits per row (16) The number of clusters per row (C _pl ) Language English four 16 64 French four 16 64 Deutsch four 16 64 Greek 3,7 16 59.2 Russian 3,5 16 56 Arab 4.4 16 70,4 Japanese 1.8 16 28.8 Chinese 1.8 16 28.8 Korean 2.2 16 35,2

Turning to FIG. 14, the method for determining the symbol density includes, at step 1410, determining the number of clusters per bowling for font. In an exemplary embodiment, the number of clusters per skittle is obtained from means 250 for generating text parameters. At step 1420, the ratio of the number of clusters per skittle spacing in the font to the number of visible clusters in a predetermined foveal angle is determined, hereinafter referred to as the number of clusters per fixation. As described above, the number of clusters per fixation can be obtained or can be found from a source such as the above table, and it is different for each font of each language. At step 1430, the ratio of the number of clusters per skittle to the number of clusters per fixation is multiplied by the width of the display means in points to obtain the optimal size in points for the font family at a given viewing distance. At block 1440, the resulting font size is further scaled in accordance with markup entered by a user or other source. At block 1450, a minimum font size and a maximum font size are determined. In more detail, in one embodiment, the minimum font size is based on the maximum number of clusters that the user can perceive in one foveal fixation. The maximum font size is based on the minimum number of clusters that the user can perceive in one foveal fixation and still understand the meaning from a line of text. In a further embodiment, the minimum and maximum font size can be determined based on a predetermined percentage of the optimal font size, for example, ± 25% of the number of clusters per commit (C _pf ) indicated in the table. This translates into a minimum font size that is 0.8 times the optimal font size and a maximum font size that is 1.33 times the optimal font size. In another alternative embodiment, the minimum font size may be determined using a visualization tool based on the resolution with which the information is output and the minimum number of pixels required to successfully render the font without breaking the integrity.

Turning to FIG. 15, there is shown a system 1500 for adjusting text in a specific font for optimal readability in a display environment. The system includes a client application 200, which includes a data structure containing one or more text parameters 1510. The system further includes metric reader 240 connected to the client application 200, wherein metric reader 240 is configured to accept one or more text parameters 1510. The metric reader 240 includes a module configured to determine the density of characters for the font 1520, and a module configured data to multiply the value of the symbol density by the size of the area of the sharpest eye focus to calculate the optimal font size for readability 1530. The shown symbol density determination module 1520 is connected via IPP 1540 to a text parameter generating tool 250 for determining the number of clusters per skittle 1550 for the font. The symbol density is calculated by dividing the number of clusters per bowling, by the number of clusters per fixation. In one embodiment, the metric reader 240 further includes a module configured to determine the optimal column width through a function to determine the optimal font size, the number of clusters in a fix for a font in a particular language, the number of clusters per skittle, and the distance from the tool display. System 1500 further includes a visualization device for a display medium 1560, the display medium may be a printed page, a computer screen, a personal digital assistant, a holographic image, a bulletin board, a movie screen, a theater, a pair of glasses, or the like.

For example, if the language is English (the number of clusters in the commit is 4.0), and the distance from the monitor is 1368 points (19 inches) and the font is Times New Roman (2.58 clusters per skittle), then the optimal font size for good readability it is set with the help of 2 × tangent from 0.006544 × 1368 × 2.58 / 4.0 = 11.5 points. For another font, Linotype Palatino, which has only 2.33 clusters per pin size, the optimal font size is set using 2 × tangent from 0.006544 × 1368 × 2.33 / 4.0 = 10.4 points.

Using a different language and font form would lead to significantly different results. For example, the Times New Roman font for Arabic. The culturological measurement of the number of clusters in fixation is 4.4, not 4.0, and the number of clusters per bowling is 2.97. Therefore, the optimal size of a readable font in Times New Roman in Arabic is 2 × tangent from 0.006544 × 1368 × 2.97 / 4.4 = 12.1 points.

Although the value for the number of clusters per fixation should be determined for each language group, the formula remains open for possible adjustment for a sharply narrowed field of view, distance from the observed surface, and other visual impairment. They will work on the input for the formula to change the default value obtained. Some of these special settings can also be automated to help adapt the text to various viewing conditions, such as multiple speakers, small devices and a large screen, high resolution monitors. In a further embodiment, the para -veolar angle, foveal angle, and symbol density may vary depending on the information entered by the user to help physically disabled users, users with physical defects, or users with sharper vision. A narrower paraffoveolar angle refers to users with a sharply narrowed field of view. The wider paraveovolar angle refers to users with acute peripheral vision. A narrower foveal angle refers to users with weakened macular properties.

Turning to FIG. 16 in combination with FIG. 7 and 8, step 850 of FIG. 8 provides calculation of font size and line height. As is known from the prior art, requirements for line height vary depending on line length and for different font sizes. Even for a single font size, line height requirements vary depending on the number of columns on a page. According to the presented embodiments, the row height is determined using various methods, depending on the information available. In one embodiment, after the font size is determined as described above, the line height for a given font size is determined. This row height can then be changed based on the number of clusters per row, row length, and the like.

One method for calculating row height is described with reference to FIG. 16. As shown, the indicator reading means 240 receives from the text parameter generating means 250 a plurality of calculation results regarding a gray level for a given font in a given language. These calculation results include the number of 1610 clusters per bowling, the width of 1620 “rivers”, the saturation of 1630 “rivers”, the width of the “channel” 1640 and the saturation of the “channel” 1650. In the indicator reading means 240, the coefficient determination module 1660 for optimally readable line height calculates line height.

More specifically, one embodiment is directed to calculating a line height based on a contrast ratio between an entire “river” and a “channel” for text printed in a given font with a given font density. Five outputs from the text parameter generating means 250, which are used to determine the line height, 1610, 1620, 1630, 1640 and 1650, are described in more detail. The first is the width of the "river." The width of the “river” (R _w ) is measured from the topmost ink-covered sections in the font to the lowest ink-covered sections in the font as a ratio to the size of the skittle. The width of the “river” is usually not equal to the size of the spacing, since most Latin and Asian fonts have a much smaller width of the “river” than the size of the spacing, and most Arabic and Thai fonts have a much larger width of the “river” than the size of the spacing. For example, referring to FIG. 17, a reproduction of the English Tahoma 1710 font is shown next to the Arabic Tahoma 1720 font. The font size for the skittle spacing in Tahoma is shown between lines 1730 and 1740. The illustration shows that the English Tahoma font is smaller than the skeleton spelling, and the Arabic Tahoma font on the right is significantly larger than the skeleton spelling.

Returning to FIG. 16, the normalized “river” saturation (R _d ) 1620 refers to calculations in the text parameter generating means 250 that are normalized to the darkest row of pixels in the characteristic row of the measured text, as described above in FIG. 8. All saturation values can be expressed relative to the darkest row of pixels. Saturation is the average value of black and represents the full ratio between the pixels in a given range of text. R _w × P _a provides the full gray level for the “river”.

The gray level for the “channel” is calculated using the same formula, but based on the width of the “river channel” (RC _w ) and the saturation of the “river channel” (RC _d ). Dividing the gray level for the “channel” by the total gray level for the “river” provides a contrast ratio between the “channel” and the rest of the “river”.

The optimum readable line height in coefficient determination module 1660 is determined by dividing the gray level ratio for the “channel” to the total gray level by font character density (C _pm ) to obtain the portion of the font size that needs to be added to the font size to determine the line height. By adding 1 to this value, the system generates the optimal line height (LO) as a ratio to the font size. The following equation summarizes the actions in the coefficient determination module to obtain an optimally readable row height: (1 + ((RC _w × RC _d ) / (R _w × R _d ) / C _pm )) × LΔ = LO.

In one embodiment, the line height is multiplied by the line height markup scale (LΔ) before the determination of the optimal line height is completed.

In another embodiment, the results of the calculations in the text parameter generating means 250 include: In _h = width of the “pre-submultiple”; B _d = saturation “pre-shallow”; And _h = the width of the “post shallow”; A _d = “post shallow” saturation; RC _w = “channel” width; RC _d = “channel” saturation; C _pm = the number of clusters in skittle; FS = the default font size, and the following equation is defined in the coefficient determination module 1660 for an optimally read line height in order to calculate the line height: FS / (((B _h × B _d ) + (A _h × A _d ) + (RC _w × RC _d )) × C _pm ) = line height.

In other words, the sum of the sizes multiplied by the saturation for the three parts of the “river”, multiplied by the number of clusters in the skittle, divided by the font size, provides the line height. Thus, the line height for the Times New Roman font of ten points will be calculated as follows:

10 / ((((0.24 × 0.09) + (0.45 × 0.6) + (0.22 × 0.04)) × 2.58) = 10 / (0.0216 + 0.27 × 0.0088) × 2.58 = 10 / 0.775032 = 12.9.

A simpler version of the same method uses the following formula: FS / (R _d × Rw × C _pm ) = L.

In other embodiments, the setting of a typical line height, which is used for English text equal to 120% of the font height, is used to summarize the line height for other languages. Using these known 120%, another method for determining line height is described in FIG. 18. At block 1810, a default line height is determined by calculating 120% of the font size. At block 1820, the default line height is changed using an exponential function of the average width of one cluster in a predefined font. More specifically, this function can be the square root of the sum of the average width of any one cluster in any font and the square of the actual font height. The actual height and width of any cluster can be determined in the tool 250 formation of the parameters of the text for any international font. One embodiment uses the following equation: 1.2 FS (R _w ² + (l / C _pm ² )) ^1/2 , which is an exponential function using FS as the default font size; 1,2 - user input or standard default line height factor; R _w is the actual height of the font from its upper ink-coated dots to its lower ink-dots per pin size; and C _pm is the number of clusters per bowling.

As an example, for the default font size of 10 points, the line height is calculated as follows: 10 × 1.2 × (0.912 + 1 / 2.58 ² ) ^1/2 = 12 × 0.9891 = 11.9.

Therefore, the optimal line height for a Times New Roman font of 10 points would be 11.9 points. The same formula, however, when applied to the 10-point Linotype Palatino font, which has an ink-filled height of 1.01 per skittle and 2.33 in clusters per skittle, would result in an optimal line height equal to 13.2 points.

In another embodiment, the line height changes as the line length increases. Turning to FIG. 19, the flowchart illustrates such a line height adjustment based on a function of the physical length of a line of text or the number of clusters per line. Step 1910 provides a determination of the required number of clusters per font line and the optimal default string length in clusters per font line. Step 1920 scales the original font size by dividing using the required number of clusters per line and the optimal default string length in clusters per line.

Once the line height is determined according to the font size, changing the font size will change the line height. If the font size is made larger, then the determined line height is not in a linear relationship. In one embodiment, a change in line height with a change in font size can be found using an exponential function. An exponential function may include considering the number of clusters per bowling. The initial font size is known, and the line height for this font size is known. There is a limit to increasing the font size. The line height cannot be less than the font itself. Therefore, referring to FIG. 20, graph 2000 illustrates empirically determined possible line heights for a given font size in points. The X axis 2002 represents the font size in points; the Y axis 2004 represents the length of the string in points. Line 2010 at an angle of 45 degrees illustrates the height of the line, which is equal to the size of the font. As the font size increases, the line height size does not increase linearly. Instead, as the font size increases, the required line height decreases, as shown by line 2020. In one embodiment, the automatic method for determining line height uses an exponential function. This function uses a variable number of clusters per bowling spacing. In particular, it has been found that a power function can be used instead of using a constant varying between 1.02 and 1.07 as an exponent. Additionally, it was found that the constant that can be used for all fonts is 1.047. This constant is approximately equal to pi divided by 3. Another variable refers to down compatibility. If a 20% line height for a font with a height of 30 points is applied, then a different font size must be associated with it and give the same line height for the same font size, regardless of where the method starts.

Turning to FIG. 21, the flowchart illustrates a method for determining font size. Step 2110 provides a determination of the corresponding default line height specification in relation to a predetermined readable line length and font size. Step 2120 provides a determination of any changes in the font size value that are caused by the font size being different from the default value. As you know, a larger font requires a smaller line height factor from the font size. Prior art methods require designers to customize with the eye. If the font seems dense, then the designers add line spacing. If the font seems to be free, then the designers reduce the line spacing. Step 2130 automatically detects the new line height by determining the difference between the optimal default font size and the actual font size, and at step 2140, the line height is adjusted using a constant raised to the power of the difference between the font sizes. The equation (1,047 ^{(FSO - FSA)} × (L _O / FS _O - 1)) × FS _A = LA represents one embodiment of determining the new row height according to this method. In the FS equation, _O represents the initial default font size; FS _A introduces a new font size; L _O represents the initial default row height; and L _A represents the new row height.

For example, to find a new line height for a Times New Roman font with a font size of 14 points, its default value can be determined according to the method described above for a font size of 10 points and with a line height of 12.9 points, and the equation will provide the following results: (1,047 ^(10-14) × (12.9 / 10 - 1) +1) × 14 = ((1,047 ^-4 × 0.29) + 1) × 14 = ((0.8321 × 0, 29) + l) × 14 = 1.2413 × 14 = 17.38.

Meter Reading Operation Chart

Turning to FIG. 22, the flowchart shows how the metric reader 240 processes the input information and how each calculated measurement result is shared to determine other measurement results. FIG. 22 shows how the embodiments described above fit into the entire system of a metric reader 240.

Step 2202 represents the number of clusters in the commit entered from a language table or other source. From step 2202, the method proceeds to step 2204, which displays the number of clusters in the row with the best readability. These calculations can be performed in accordance with the table above.

Step 2206 represents inputting information from a computer system that provides a size value for the display medium, such as diagonal screen size. Step 2208 presents calculations to determine a default distance from the display medium. One way to determine the default distance is described above with reference to FIG. 10. Step 2210 provides a “font size” input that can be entered by the user or by page layout. Such markup may resemble the <Element font size = “1.2”> element content. </ Item>.

Each of steps 2210, 2212, 2208, and 2202 provides input for calculating an optimum readable font size. Step 2214 represents calculating an optimum readable font size. As described above, finding the optimal font size includes first determining the horizontal area on the surface of the display medium within a predetermined foveal angle for a predetermined distance and determining the font size according to the number of clusters per fixation corresponding to a given language and font. In one embodiment, the font scale 2210 provides a multiplier for determining the font size.

Step 2214 provides input to step 2216, which provides for determining a minimum and maximum font size. In one embodiment, the minimum and maximum font size is based on a 25% deviation from the optimal font size, or 0.8 times the predetermined font size for the minimum font size, or 1.33 times the predetermined font size for the maximum size font. In another embodiment, the maximum number of clusters contained in one foveal fixation determines the minimum font size and maximum font size for a given language.

Steps 2218, 2220, 2222, 2224 may provide output from text parameter generating means 250, which provides input to determine a height factor 2228 of an optimally read line, as described above with reference to FIG. 15. In one embodiment, step 2228 receives input information 2226 of a line height scale from markup or user input, such as <Element line height scale = “1.5”> element content. </ Item>.

Step 2230 provides a determination of the minimum and maximum number of clusters per row. The determination of the minimum and maximum values of the line length helps the possible values of the preferred number of columns that can be entered by the user. In one embodiment, the preferred number of “low” columns provides optimal readable row length in clusters for immersive reading. The “average”, “high” and “maximum” values of the preferred number of columns provide progressively shorter rows with fewer clusters. The preferred number of “one” columns extends the length of the string in clusters outside the standard immersive read.

In one embodiment, the increments are uniform, ranging from half the optimal number of clusters per row to 1.167 times the optimal number of clusters per row. To determine the minimum row length in clusters (C _m ) and the maximum row length in clusters (C _M ), the following equations are used: C _m = 0.5 × C _pl ; With _M = 1,167 × C _pl .

The minimum and maximum number of clusters per line 2232, along with the number of clusters in the best-read line 2204, the minimum and maximum readable font size 2216, and the number of clusters per skittle 2212 is entered at step 2232, where the optimal, minimum, and maximum column width.

The minimum column width can be determined by calculating the minimum font size and multiplying the minimum font size by the smallest number of clusters that are allowed per row. The following equations determine the optimal, minimum, and maximum column widths, respectively: CW _O = FS _O × C _pm × C _pl (the number of clusters per best-read row); CW _m = FS _m × C _pm × C _m (minimum number of clusters per best-read row) CW _M = FS _M × C _pm × C _M (maximum number of clusters per best-read row).

Prior to determining the optimal, minimum, and maximum number of 2242 columns, the metric reader 240 first determines the potential actual width 2240 of the set. The real set area is the placement area that is between the margins of the page. The potential real set area is the difference between the window size and the user-defined field value settings. Fields are calculated based on the shortest page size (dimension). Therefore, the evaluation step is first performed to determine which of the vertical size or horizontal page size is smaller. The size value of the fields that are required in the markup is then subtracted from the smallest size. The result is a real set area. Input information for determining the potential real set width includes window width (WW) 2234, window height (WH) 2236, and user preferred field values (MP) 2238. Thus, to determine the width of the potential real set area (LW _O ), the preferred value the sizes of the fields in the markup (MP) are subtracted from the window width (in points) and multiplied by the shortest window dimension: WW - (the minimum value from (WW, WH) × MP × 2).

The size of the potential real set area 2240 and the optimal, minimum and maximum column width (CW _O ; CW _m and CW _M, respectively) 2232 provide input to determine the optimal, minimum and maximum number of columns (CC _O ; CC _m and CC _m, respectively) 2242 In one embodiment, for determining the number of columns, the equation allows you to calculate the number of columns of each of the sizes that can fit in the potential real width of the set. The output provides a range of the minimum, maximum, and optimal number of columns that will fit on the page. The possible number of columns (CC) can be calculated for the minimum, maximum, and optimal column widths by dividing the potential actual set width by the column width. The following equations are used to determine the optimal, minimum, and maximum number of columns: CC _O = LW _O × CW _O ; CC _m = LW _O × CW _M ; CC _M = LW _O × CW _m .

Turning to steps 2244 and 2246, after the number of columns is determined, the metric reader 240 uses the number of columns and a user-entered preferred value for the number of columns to determine the actual number of selected columns.

The calculation of the actual quantity is necessary because the equations of step 2242 do not produce the total number of columns. At step 2242, the actual number of columns that is required through the preferred number of columns 2244 is rounded to the corresponding integer number of columns (CC _A ). In one embodiment, step 2246 is evaluative. If the preferred number of columns = “one” (“0”), then CC _A will automatically be 1. If the preferred number of columns = “low” (“1”), then CC _A will be rounded to CC _O.

If the preferred number of columns (CP) = “average | high ”(“ 2 | 3 ”), then CC _A must be calculated.

If the preferred number of columns (CP) = “maximum” (“4”), then CC _A will be rounded to CC _M.

The calculations for the mean and high values will be one third and two thirds between the optimal value and the maximum value. Therefore, CC _A can be rounded to:

((CC _M - CC _O ) × 3) + CC ₀ (average),

(((CC _M - CC _O ) × 3) × 2) + CC ₀ (high).

The following pseudo code demonstrates the operation of step 2246:

IF CP = 0,

THEN CC _A = 1,

ELSE CC _A = (((CC _M - CC _O ) / 3) × (CP - 1)) + CC _O.

Rounding for the total number of columns will be to a larger value if the required number of columns has a fractional part greater than 0.75, otherwise it will be rounded to a lower value. This 0.75 value can be changed by entering another value by the author or user.

Step 2248 provides a determination of the actual width of the set and receives from step 2232 input information for the optimal, minimum, and maximum column widths, from step 2240, information about the potential real width of the set, from step 2244 the preferred values for the number of columns, and from step 2246, the number of columns.

Since the actual actual set width (LW _A ) can never be larger than the previously calculated potential real set width (LW _O ), when calculating, the calculated value is compared with the potential width to check which one is smaller. A lower value is decisive. The calculation of the actual width of the set also has special cases based on user markup for the preferred number of columns. If the preferred number of columns = “one,” then step 2248 provides a column on the page with such a large size that will fit within the number of clusters in line 2204 and font size 2214. If the preferred number of columns is “low”, then the indicator reading engine 240 tries to find the optimal value experimentally. A larger preferred number of columns may be limited by the width of the available space. The following pseudo-code demonstrates the determination of the actual width of the set.

IF CP = 0,

THEN minimum value from СW _M or LW _O = LW _A ,

ELSE

IF CP = 1,

THEN minimum value from (CC _A × CW _O ) + (CW _O × (CP - 1) × 0.075) or LW _O = LW _A ,

ELSE LW ₀ = LW _A.

The number of 2246 columns and the actual width of the set 2248 are introduced to step 2250, which calculates the actual width of the gap between the columns (CG _A ). The gap between the columns may be equivalent to the gap between the columns in the layout properties. One of the differences is that the unit of measure for step 2250 may be based on a portion of a column, but may slightly affect the measurement result of a column when distributed to multiple columns. Using the following equation, you can determine the actual column spacing width (CG _A ): LW _A × CC _A × 0.075 = CG _A.

Turning to step 2252, when the actual width of the column spacing is determined, the actual column width (CW _A ) can be determined, which may be equal to the “column width” markup property. When the number of columns, the actual width and the distance between the columns are known, the exact column size can be calculated using the following equation: (LW _A × CC _A ) - ((CC _A -1) × CG _A ) = CW _A.

Turning to step 2254, the metric reader 240 calculates the actual font size (FS _A ) using input from steps 2252, 2212, 2214, and 2230. In one embodiment, determining the actual font size is performed by calculating the recommended font size (FS _r ) to place and then compare the recommended font size with the minimum font size and maximum font size to ensure that the font size is within the range that was previously calculated. The previous process is also a conditional process that determines whether the string is too long or too short to use the optimal font size, due to the number of clusters that appear in the string. Only when the range of the number of clusters exceeds the optimum value, the font size is changed. The following pseudo code can perform the calculations for step 2254:

IF: CW _A > FS _O × C _M ÷ C _pm ,

THEN CW _A - C _M × C _pm ,

ELSE IF: CW _A <FS _O × C _m ÷ C _pm ,

THEN: CW _A ÷ C _m × C _pm ,

ELSE: FS _O = FS _r .

Border checking to complete the determination of the actual font size (FS _A ) is also a conditional expression:

IF: FS _r > FS _M ,

THEN: FS _M ,

ELSE IF: FS _r <FS _m ,

THEN: FS _m ,

ELSE: FS _r = FS _A.

Turning to step 2256, it computes the actual number of clusters per line using the values determined in step 2254 and step 2252. The actual number of clusters per line is determined before determining the corresponding line height for a particular font. The following equation determines the number of clusters per row: CW _A × FS _A × C _pm .

Turning to step 2258, the metric reader 240 determines the actual line height (L _A ) as a ratio to the font size that will be applied to the best-read text for a given environment. A method for determining the actual row height includes at least two settings. First, a metric reader 240 optimizes line height for font size if FS _A ≠ FS _O. Optimization ensures that larger fonts do not accept proportionally the same large line height as smaller fonts. Then, the metric reader 240 adjusts the row height for the row length. The following equation performs optimization and tuning: ((C _A × (1,047 (FS _O - FS _A ) × (Lo - 1))) × C _pl ) + 1.

The following part of the equation: (1,047 (FS _O - FS _A ) × (L _O - 1), sets the font size. The rest of the sets the line length. The result is the optimal line height for the actual font size for the actual column width.

Returning to FIG. 20, in FIG. 20 shows a portion of the equation for font size. FIG. 23 illustrates a graph 2300 for adjusting line length. As shown, the X axis 2302 represents the font size, and the Y axis 2304 represents the height of the line. The setting is made for line 2320 (it is the same as 2020) for a line that is half the length shown by line 2323, and for line 2330 for a line 1.5 times longer.

Returning to FIG. 22, in step 2260, the actual default font size is calculated in substantially the same way as the font size of the main text, but based on a user-defined value of 0.75 times the minimum number of clusters per row (C _m ) and the actual set width (LW _A ). The following equation applies: HFSA = LW _A × (0.75 × C _m ) x C _pm .

In one embodiment, the header font size is fixed at the top of the range for reading the output of the metric reader 240.

Turning to step 2262, the default row height for the header font size (HL _A ) is determined according to the same equation as for the actual row height for the font size of the main text 2258. The coefficient is generated based on HFS _A , not FS _A , and a user-set value of 0.75 is multiplied by the minimum number of clusters per row (C _m ) as follows:

((0.75 × C _m × (1,047 (FS _O - HFSA) × (L ₀ - 1))) × C _pl ) + 1 = HL _A.

Line height is not strictly based on line length and font size and lies between the size of the main text and the title size for a given font size factor. Thus, when the body text has a line height of 1.16 times the font size, the line height for the title font can only be 1.05 times the font size of the title.

Turning to step 2264, the metric reader item is determined using input from the actual font size of the header 2260 and the actual font size 2254. The metric reader item refers to two typographically-related items: the optimal readable font size for placement and the optimal top-level font size for the title in the window. Depending on them, the indicator reading means 240 constructs a graph using increment elements, each of which is defined at the intersection as a typographic point for a given font family. In order to perform the conversion between “real” points equal to 1/72 ”and the points of the reading means, equivalents can be created for the upper and lower bounds.

The value of the metric reader item is based on a comparison of the default window font size and the actual font size that was calculated. The default value is 12 points. Therefore, when converting to the values of the indicator reading means 240, any link to the font 12 of the paragraph is interpreted as a link to FS _A. Sizes below 12 points are interpreted using a factor that divides FS _A into 12 parts. Hence:

Parameter Reading Point = FS _A × 12.

However, the value of the item readout indicators for a font above 12 points is not necessarily equal to the value of the point for the font size equal to or below 12 points. The reading point value for a font size above 12 points is based on dividing the gap between the preset optimal header font size of 48 points (the default value because it is the default value for the Windows® text font size) and the body size of 12 points. Therefore, for a size greater than 12 points, the following formula is used:

Meter Reading Points = (HFSA - FS _A ) - 36.

To calculate the output size for any markup font (FSµ), the following compound formula is used:

IF, FSµ ≤12,

THEN, FSµ × FS _A ÷ 12,

ELSE, ((FSµ - 12) × ((HFSA - FS _A ) ÷ 36)) + FS _A.

As a result of this operation, any markup font size can be interpreted in accordance with the value of the metric reader item, which includes the default measurement results for the font size, which are generated from standards such as the hierarchical (cascading) style sheet (CSS) standard like “very very small”, “very small”, “small”, “medium”, “large”, “very large”, “very very very large”, and the default values for all levels of headings are from 1 to 7. Following so pay attention to what items are indicators of reading means apply only to items font size, and not to any other outcome measurement in points.

Turning to step 2266, the actual value of the fields is a block called “fields” that can be used in combinations that adjust the values of some other blocks (especially the size of the column). The actual margin size (MA) is determined using the smaller of the actual set width (LW _A ) or window height, and the preferred margin value (MP). If the window height is less than the actual width of the set, then the actual value of the fields is defined as the window height multiplied by the preferred field value. However, if the actual set width is smaller than the window height, then the metric reader is calculated using the real set width divided by 1, minus the preferred margin value. Therefore, the formula for the fields (M _A ) is as follows:

IF, W _H <LW _A ,

THEN, W _H × M _P ,

ELSE, ((LW _A ÷ (1 - 2M _P )) - LW _A ) ÷ 2.

Turning to step 2268, the grid has been a fundamental tool for placement designers for the past 500 years. But it has always been considered as a static tool, relevant in only one publication for a given page size. Allen Hurlburt, in his 1977 book Layout: The Design of the Printed Page (Watson-Guptill Publications, NY, NY 1977, 1989. Page 82), cited for reference, states:

“The designer grid organizes specific content relative to the exact place it will occupy. When a grid is used, it enables the designer to create many different arrangements containing a variety of elements within the grid structure. When used in the development of a publication, advertising campaign or series, it gives a sense of continuous consistency, even when there are significant changes in the content of each individual module. ”

Although Hurlburt emphasizes the importance and necessity of a good mesh in both this and his later publications, The Grid (1978) and The Design Concept (1981), he repeatedly emphasizes that the static nature of the mesh is exactly what makes it useful to use. by designers. However, designers in the electronic world, in which the size of the display and its shape are not known in advance, were unable to create reliable grids if the size and shape of the display were not fixed in advance.

The metric reader 240 outputs a grid element size (G _U ) of 2268, which is dynamic and scalable for a wide variety of font sizes and displays. The input for step 2268 includes the actual column width (CW _A ) 2252, the actual font size (FS _A ) 2254, the number of clusters in the best-read row (C _pl ), and the number of clusters per skittle (C _pm ). The width of each grid element is based on determining the optimal number of clusters per row (C _pl ), balanced by the actual column width (CW _A ). The equation divides C _pl by 6 and then determines how many elements of this length will fit in CW _A for a given FS _A. Fractional parts are distributed evenly across the width of the whole grid element. Hence:

G _UA = CW _A ÷ truncated value from ((6 × CW _A × C _pm ) ÷ (FS _A × C _pl )).

The grid has one dimension based on its width. If a mesh element is used as a height measurement value, it has the same value as a width value. In one embodiment, the mesh element size can change dynamically, dividing the vertical space differently than the horizontal space.

Turning to step 2270, the column has various dimensions when used in height and width. Thus, when used in the layout, the width of 1 column has other dimensions than the height of 1 column. In one embodiment, the actual column height is divided without a remainder by the total number of rows. Therefore, the window height (W _H ), which is less than the margins, is equal to the potential real set height (L _HO ). The truncated value from L _HO divided by the length of the line (L _A ) times the length of the line equals the height of the column (C _H ). In other words, W _H - (2 × MA) (or another factor if the markup indicates fields larger than 1MA) = L _HO.

(Truncated value from (L _HO / L _A )) × L _A = C _H.

Turning to step 2272, the actual page height (P _H ) is equal to the height of the column plus the top and bottom margins. Therefore, P _H = C _H + 2MA (or another factor if the markup indicates fields larger than 1MA either above or below).

Turning to step 2274, the actual page width (P _W ) is the sum of the width of the columns, the width of the distances between the columns and the left and right margins. Therefore, P _W = (CW _A × C _CA ) + (CG _A × (C _CA - 1)) + (2 × MA).

Thus, the page has different sizes in height compared to the width, and they may not be equal to the size of the window.

Turning back to FIG. 2 in combination with FIG. 22, the methods described above for determining line height, column size, and font size allow automatic determination of optimum sizes for readability in a display environment when reproducing text. In each case, an appropriate device for implementing the presented methods may include computer components, such as a microprocessor, for implementing these methods. For example, for the purpose of determining the optimal font size, a device such as a printer or PDA capable of communicating with a printer or display device may accept the width of the display medium and the distance from the display medium as input information and provide the optimal size and height as output information lines for the font or automatically display information using certain optimal font size and line height. The device may also be a computer, laptop computer, or microprocessor unit installed in a printer or the like. Information from a printer or device can be displayed on a display environment. Also, the display medium may be a printed page, a screen, a wirelessly connected display means, a message board, an image, or any font reproducing means. In some embodiments, the input of the dimensions of the display medium and the distance from the display medium may be carried out over a network or in a network client environment, through an Internet connection or the like.

FIG. 22 described above illustrates operations that occur in the placement means 202 shown in FIG. 2. Turning now to FIG. 24, one embodiment is directed to using a placement tool 202 together with an international transfer tool. The means of international translation in this context includes translator programs known from the prior art that are capable of translating text in a given language and provide text output in another language. For example, translation tools available on the Internet are known that correspond to these embodiments, which translate text located at a specific location indicated by a unified index of an information resource (URL) into another language. Turning to FIG. 24, there is shown a block diagram that includes placement means 202 and an application program 200. The application program 200 could be any application program running on the computer 110 shown in FIG. 1. For this embodiment, appropriate applications include, for example, Microsoft Word, Microsoft Publisher, QuarkXPress, Adobe InDesign, as well as other applications that are executed using or through a server connection, for example, together with a browser or application mail type, such as Microsoft Outlook®, etc. The illustrated application 200 includes a property sheet 210 and property values 220, although in one embodiment, the property sheet 210 and property values 220 may be accessible components, so that application 200 only communicates with another application program that provides the contents of the components of the sheet 210 properties and components values 220 properties. FIG. 24 further shows an external international translation tool 2460 connected to the application 200 through a connection 2470. The external international translation tool is shown as an external component for those embodiments in which the application 200 is able to access the translation tool via the Internet or through a server connection to a network tool translation, for example, on a local network. Alternatively, the external international translation tool may be external only to the application 200, for example, accessible via a central bus of a computer system. Turning to FIG. 25 in combination with FIG. 24, in one embodiment, the external international translation tool translates the text created or received in the application 200, step 2510. Changes in the properties that occur due to the external international translation tool 2460 are either transferred to the application 200 for changing items in the statement 210 properties or property values 220, or directly to the placement means 202, as shown in step 2520. The placement means 202 may have, but this is not required for this embodiment, the means 2462 is international th translation. If present, then the external means of international translation 2460 can send and receive data from means 2462 of the international translation, which provides compatibility components for the implementation of good interaction between external means 2460 of international translation and means of accommodation 202 (step 2520). In one embodiment, an external means of international translation 2460 provides the translation of the translated text in the language and the main text to the application 200, which then sends the corresponding data via connection 280 to the placement means 202 (step 2520). The host means 202 then processes the data as described above to enable the readability parameters to be transmitted to the application 200 (block 2530). More specifically, the placement means 202, using the indicator reading means 240 and the text parameter generating means 250, processes the data received by the application program 200 and / or directly from the external international translation tool 2460 to ensure that the translated text is transmitted in a human-readable format with columns, line height , font size, etc. that are configured for the language into which the text is translated.

In an alternative embodiment, as shown in FIG. 26, the application program 200 does not interact with an external means of international translation, but delivers text data to the host 202 along with language identifiers indicating the language in which the text is delivered and one or more languages into which the text is to be output (step 2610). The international translation tool 2462 then receives the language identifiers and the text and provides the translation of the translated text to determine the parameters needed for the input filter 230 layout and structure to create data structures that are processed using the tool 240 read indicators and tool 250 generate text parameters (step 2620 ) Thus, in one embodiment, the application program 200 can provide data to the host in one language and receive back data allowing the application program 200 to display text in multiple languages, which is formatted for a given display for each desired language and corresponds to these specific languages ( step 2630). The application program 200 may also be executed on different displays, with each display having a different size and each display receiving the same text in different languages formatted for a given display and a given language. Thus, for example, if the application program 200 is running on a server that serves client machines in several countries, then each display in the server group receives text in a corresponding language, having a size and formatted accordingly for that language and for the corresponding display. For a server running a mail program or the like, application 200 can run on each client machine, such as Microsoft Outlook®, or it can run on a server such as a Unix® server, and the server can be, for example, an exchange type server. The server or client machine running the application program 200 receives text in any language and can be configured to interact with a host means 202 to automatically translate text in any accepted language into a local language or a language of choice that is programmed in the 2462 international translation tool, and output to the application program 200 parameters necessary for such formatting of the translated text, which corresponds to the given language and this display (s).

All of these references, which include any patents, patent applications and publications, are presented in their entirety for reference.

In view of the many possible embodiments to which the principles of this invention can be applied, it should be recognized that the embodiment described with reference to the drawings is illustrative only and should not be used as limiting the scope of the invention. For example, those skilled in the art will recognize that the elements of the illustrated embodiment embodied in software can be implemented using hardware and vice versa, or that the illustrated embodiment can vary in arrangement and details without departing from the scope of the invention. Therefore, the described invention considers all such embodiments as being within the scope of the following claims and equivalent to it.

Claims (7)

1. A method of visualizing text in a display environment when changing the font size, and this method comprises the steps of:
determine the density of characters in the font,
divide the font contrast value by the density of characters to obtain a fraction of the font size and
add this fraction to the font size to adjust the line height, render the text with the adjusted line height in the mentioned display environment. 2. A method for rendering text in a display environment, this method comprising the steps of:
determine the height of the line as a function of a predetermined readable line length and a predetermined font size with which the text should be rendered,
determining a difference between a predetermined font size and an actual font size, and
adjust the height of the line by raising the constant to the power of this difference,
render text with a customized line height in said display medium. 3. The method according to claim 2, further comprising the step of:
change the predefined font size if the actual font size is different from the predetermined font size. 4. The method according to claim 2, in which the constant is a size smaller than 1.07 and greater than 1.02. 5. A computer-readable medium comprising computer-executable instructions stored thereon for executing a method for rendering text in a display medium, the method comprising the steps of:
determining a line height as a function of a predetermined readable line length and a predetermined font size,
determining a difference between a predetermined font size and an actual font size, and
adjust the height of the line by raising the constant to the power of this difference,
render text with a customized line height in said display medium. 6. The computer-readable medium of claim 5, wherein the computer-executable instructions further perform the step of changing a predetermined font size if the actual font size differs from the predetermined font size. 7. The computer-readable medium of claim 5, wherein the constant is a size smaller than 1.07 and greater than 1.02.

Images