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  2. Help:WordToWiki - Wikipedia

    en.wikipedia.org/wiki/Help:WordToWiki

    Open your document in Word, and "save as" an HTML file. Open the HTML file in a text editor and copy the HTML source code to the clipboard. Paste the HTML source into the large text box labeled "HTML markup:" on the html to wiki page. Click the blue Convert button at the bottom of the page.

  3. Gradient-domain image processing - Wikipedia

    en.wikipedia.org/wiki/Gradient-domain_image...

    This gradient can then be manipulated directly to produce several different effects when the resulting image is solved for. For example, if a uniform constant scales the gradient, it results in a simple sharpening filter. A better sharpening filter can be made by only scaling the gradient in areas deemed important. [2]

  4. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.

  5. Color gradient - Wikipedia

    en.wikipedia.org/wiki/Color_gradient

    A linear, or axial, color gradient. In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to fill a region. In assigning colors to a set of values, a gradient is a continuous colormap, a type of color scheme.

  6. Image gradient - Wikipedia

    en.wikipedia.org/wiki/Image_gradient

    The pixels with the largest gradient values in the direction of the gradient become edge pixels, and edges may be traced in the direction perpendicular to the gradient direction. One example of an edge detection algorithm that uses gradients is the Canny edge detector. Image gradients can also be used for robust feature and texture matching.

  7. Document AI - Wikipedia

    en.wikipedia.org/wiki/Document_ai

    Data is typically distinguished in spatial data and time-series data, the former can be things like images, maps, graphs, etc. the latter can be e.g. stock-price or a voice recording. Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial.

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