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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 ...
Image Translation: a function that identifies text in a picture taken by the users and translates text on the screen instantly by images. [ 29 ] Handwritten Translation: a function that translates language that are handwritten on the phone screen or drawn on a virtual keyboard without the support of a keyboard.
A 2018 paper by the University of Bologna evaluated the Italian-to-German translation capabilities and found the preliminary results to be similar in quality to Google Translate. [42] In September 2021, Slator remarked that the language industry response was more measured than the press and noted that DeepL is still highly regarded by users. [43]
Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation.
Cyrillization of Arabic is the conversion of text written in Arabic script into Cyrillic script. Because the Arabic script is an abjad (a writing system without vowels), an accurate transliteration into Cyrillic, an alphabet , would still require prior knowledge of the subject language to read.
MediaWiki translation on translatewiki.net, a localisation platform for translation communities, language communities, and open source projects; This page serves as a reference for anyone, but especially for new contributors, interested in assisting in the translation of articles "from" the English Wikipedia "into" other languages.
Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical. These methods have since been superseded by neural machine translation [1] and large language models ...
A generative LLM can be prompted in a zero-shot fashion by just asking it to translate a text into another language without giving any further examples in the prompt. Or one can include one or several example translations in the prompt before asking to translate the text in question. This is then called one-shot or few-shot learning, respectively.
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