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XUnity.AutoTranslator is an advanced plugin framework designed to automatically translate text in Unity-based games in real time by intercepting and replacing in-game text during rendering. It works by injecting itself into the game process and leveraging external translation services such as Google Translate, DeepL, or custom APIs to dynamically convert text into a target language. The tool supports both automatic translation and manual translation workflows, allowing users to refine or...

...MisakaPatcher added support for plug-in Chinese patch, so this tool is more suitable for players who like manual translation, and also provides another way for Chinese personnel who have difficulty unpacking packets to release Chinese patch. MisakaHookFinder is suitable for some games where the translator can not be used to directly get the text hook method. Users can search for the hook special code by themselves or directly use it to get the source text. At the same time, it also supports the clipboard to output the original text.

A small parser for the Esperanto language, intended as an API for other programs. The blog entry describing this program's function and motivation can be found here: http://coder32768.blogspot.com/2014/04/gort-klaatu-barada-esperanto.html The program takes text in Esperanto, and returns an object graph describing that utterance. The program has expansion points for transforms of this graph, in the spirit of transformational grammars.

NumberTranslation is an example of Windows application for number translation web service access. You can use it in your language translation software.

This substitution cipher toolkit enables you to en- and decrypt texts with substitution cipher, to gather language statistics of a specific language and to crack encrypted texts both manually and automatically. All functions can be accessed via an easy-to-use graphical user interface.

...Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.

The initial goal on Zigoora has been enabling people from different languages to communicate together. In other words, if you know another language additional to your mother tongue, then you can help somebody else understand and read in another language by translating just one sentence.