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For Developers



To check if you have a compatible version of Java installed, use the following command:

java -version

If you don't have a compatible version, you can download either Oracle JDK or OpenJDK


To check if you have Maven installed, use the following command:

mvn --version

To install Maven, you can follow the instructions here.


Install the latest version of Git.

Download Code

In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:

git clone <your-fork-git-link>

A directory called Deasciifier will be created. Or you can use below link for exploring the code:

git clone

Open project with IntelliJ IDEA

Steps for opening the cloned project:

  • Start IDE
  • Select File | Open from main menu
  • Choose Deasciifier/pom.xml file
  • Select open as project option
  • Couple of seconds, dependencies with Maven will be downloaded.


From IDE

After being done with the downloading and Maven indexing, select Build Project option from Build menu. After compilation process, user can run Deasciifier.

From Console

Use below line to generate jar file:

 mvn install


Maven Usage


Using Asciifier

Asciifier converts text to a format containing only ASCII letters. This can be instantiated and used as follows:

  Asciifier asciifier = new SimpleAsciifier();
  Sentence sentence = new Sentence("çocuk"");
  Sentence asciified = asciifier.asciify(sentence);



Using Deasciifier

Deasciifier converts text written with only ASCII letters to its correct form using corresponding letters in Turkish alphabet. There are two types of Deasciifier:

  • SimpleDeasciifier

    The instantiation can be done as follows:

      FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer();
      Deasciifier deasciifier = new SimpleDeasciifier(fsm);
  • NGramDeasciifier

    • To create an instance of this, both a FsmMorphologicalAnalyzer and a NGram is required.

    • FsmMorphologicalAnalyzer can be instantiated as follows:

        FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer();
    • NGram can be either trained from scratch or loaded from an existing model.

      • Training from scratch:

          Corpus corpus = new Corpus("corpus.txt"); 
          NGram ngram = new NGram(corpus.getAllWordsAsArrayList(), 1);
          ngram.calculateNGramProbabilities(new LaplaceSmoothing());

      There are many smoothing methods available. For other smoothing methods, check here.

      • Loading from an existing model:

          try {
              FileInputStream inFile = new FileInputStream("ngram.model");  
              ObjectInputStream inObject = new ObjectInputStream(inFile);
              NGram ngram = (NGram<Word>) inObject.readObject();
          }catch (IOException | ClassNotFoundException e) {

      For further details, please check here.

    • Afterwards, NGramDeasciifier can be created as below:

        Deasciifier deasciifier = new NGramDeasciifier(fsm, ngram);

A text can be deasciified as follows:

Sentence sentence = new Sentence("cocuk");
Sentence deasciified = deasciifier.deasciify(sentence);


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