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Box-drawing characters, also known as line-drawing characters, are a form of semigraphics widely used in text user interfaces to draw various geometric frames and boxes. These characters are characterized by being designed to be connected horizontally and/or vertically with adjacent characters, which requires proper alignment.
They are a concern in the process of typography and pagination, where it may be desirable to have a page break follow a section break for the sake of aesthetics or readability. [citation needed] In fiction, sections often represent scenes, and accordingly the space separating them is sometimes also called a scene break. [3]
{{{1}}} word to be used {{{2}}}, positioning of the line and label: Add top for the line to be above label; Add left for the label to be in the left margin, and no break in the text (recommended for continuous text) Leave blank or write bottom for the line to be below the label {{{label}}}, prefixing word, default word is page, or label= to ...
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Line art or line drawing is any image that consists of distinct straight lines or curved lines placed against a background (usually plain). Two-dimensional or three-dimensional objects are often represented through shade (darkness) or hue . Line art can use lines of different colors, although line art is usually monochromatic.
A soft return or soft wrap is the break resulting from line wrap or word wrap (whether automatic or manual), whereas a hard return or hard wrap is an intentional break, creating a new paragraph. With a hard return, paragraph-break formatting can (and should) be applied (either indenting or vertical whitespace).
A page break is a marker in an electronic document that tells the document interpreter the content which follows is part of a new page. A page break causes a form feed to be sent to the printer during spooling of the document to the printer. It is one of the elements that contributes to pagination.
The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. [43] Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images. [12]