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Image translation is the machine translation of images of printed text (posters, banners, menus, screenshots etc.). This is done by applying optical character recognition (OCR) technology to an image to extract any text contained in the image, and then have this text translated into a language of their choice, and the applying digital image processing on the original image to get the ...
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. [3]
Word Lens was an augmented reality translation application from Quest Visual. [1] Word Lens used the built-in cameras on smartphones and similar devices to quickly scan and identify foreign text (such as that found in a sign or a menu), and then translated and displayed the words in another language on the device's display.
Reference Organizer presents all references in graphical user interface, where you can choose whether the references should be defined in the body of article or in the reference list template(s) (list-defined format). You can also sort the references in various ways (and optionally keep the sort order), and rename the references.
In practice, SubRip is configured with the correct codec for the video source, then trained by the user on the specific text area, fonts, styles, [15] colors and video processing requirements [16] to recognize subtitles. After trial and fine tuning, SubRip can automatically extract subtitles for the whole video source file during its playback.
If the English article already exists (but a translation would be useful because the article in the other language is of higher quality, or includes additional information): simply place a translation template from the category Category:Expand by language Wikipedia templates on the English article page.
GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. [2] With the large end-to-end framework, the system learns over time to create better, more natural translations. [1] GNMT attempts to translate whole sentences at a time, rather than just piece by piece. [1]
The system constructs the dictionary of single-word translations based on the analysis of millions of translated texts. In order to translate the text, the computer first compares it to a database of words. The computer then compares the text to the base language models, trying to determine the meaning of an expression in the context of the text.
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