---
uid: Lucene.Net.Analysis.Icu
summary: *content
---

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<!-- :Post-Release-Update-Version.LUCENE_XY: - several mentions in this file -->

This module exposes functionality from 
[ICU](http://site.icu-project.org/) to Apache Lucene. ICU4J is a Java
library that enhances Java's internationalization support by improving 
performance, keeping current with the Unicode Standard, and providing richer
APIs. 

For an introduction to Lucene's analysis API, see the <xref:Lucene.Net.Analysis> package documentation.

 This module exposes the following functionality: 

*   [Text Segmentation](#segmentation): Tokenizes text based on 
  properties and rules defined in Unicode.

*   [Collation](#collation): Compare strings according to the 
  conventions and standards of a particular language, region or country.

*   [Normalization](#normalization): Converts text to a unique,
  equivalent form.

*   [Case Folding](#casefolding): Removes case distinctions with
  Unicode's Default Caseless Matching algorithm.

*   [Search Term Folding](#searchfolding): Removes distinctions
  (such as accent marks) between similar characters for a loose or fuzzy search.

*   [Text Transformation](#transform): Transforms Unicode text in
  a context-sensitive fashion: e.g. mapping Traditional to Simplified Chinese

* * *

# [Text Segmentation]()

 Text Segmentation (Tokenization) divides document and query text into index terms (typically words). Unicode provides special properties and rules so that this can be done in a manner that works well with most languages. 

 Text Segmentation implements the word segmentation specified in [Unicode Text Segmentation](http://unicode.org/reports/tr29/). Additionally the algorithm can be tailored based on writing system, for example text in the Thai script is automatically delegated to a dictionary-based segmentation algorithm. 

## Use Cases

*   As a more thorough replacement for StandardTokenizer that works well for
    most languages. 

## Example Usages

### Tokenizing multilanguage text

      /**
       * This tokenizer will work well in general for most languages.
       */
      Tokenizer tokenizer = new ICUTokenizer(reader);

* * *

# [Collation]()

 `ICUCollationKeyAnalyzer` converts each token into its binary `CollationKey` using the provided `Collator`, allowing it to be stored as an index term. 

 `ICUCollationKeyAnalyzer` depends on ICU4J to produce the `CollationKey`s. 

## Use Cases

*   Efficient sorting of terms in languages that use non-Unicode character 
    orderings.  (Lucene Sort using a Locale can be very slow.) 

*   Efficient range queries over fields that contain terms in languages that 
    use non-Unicode character orderings.  (Range queries using a Locale can be
    very slow.)

*   Effective Locale-specific normalization (case differences, diacritics, etc.).
    (<xref:Lucene.Net.Analysis.Core.LowerCaseFilter> and 
    <xref:Lucene.Net.Analysis.Miscellaneous.ASCIIFoldingFilter> provide these services
    in a generic way that doesn't take into account locale-specific needs.)

## Example Usages

### Farsi Range Queries

      Collator collator = Collator.getInstance(new ULocale("ar"));
      ICUCollationKeyAnalyzer analyzer = new ICUCollationKeyAnalyzer(Version.LUCENE_48, collator);
      RAMDirectory ramDir = new RAMDirectory();
      IndexWriter writer = new IndexWriter(ramDir, new IndexWriterConfig(Version.LUCENE_48, analyzer));
      Document doc = new Document();
      doc.add(new Field("content", "\u0633\u0627\u0628", 
                        Field.Store.YES, Field.Index.ANALYZED));
      writer.addDocument(doc);
      writer.close();
      IndexSearcher is = new IndexSearcher(ramDir, true);
    
  QueryParser aqp = new QueryParser(Version.LUCENE_48, "content", analyzer);
      aqp.setAnalyzeRangeTerms(true);

      // Unicode order would include U+0633 in [ U+062F - U+0698 ], but Farsi
      // orders the U+0698 character before the U+0633 character, so the single
      // indexed Term above should NOT be returned by a ConstantScoreRangeQuery
      // with a Farsi Collator (or an Arabic one for the case when Farsi is not
      // supported).
      ScoreDoc[] result
        = is.search(aqp.parse("[ \u062F TO \u0698 ]"), null, 1000).scoreDocs;
      assertEquals("The index Term should not be included.", 0, result.length);

### Danish Sorting

      Analyzer analyzer 
        = new ICUCollationKeyAnalyzer(Version.LUCENE_48, Collator.getInstance(new ULocale("da", "dk")));
      RAMDirectory indexStore = new RAMDirectory();
      IndexWriter writer = new IndexWriter(indexStore, new IndexWriterConfig(Version.LUCENE_48, analyzer));
      String[] tracer = new String[] { "A", "B", "C", "D", "E" };
      String[] data = new String[] { "HAT", "HUT", "H\u00C5T", "H\u00D8T", "HOT" };
      String[] sortedTracerOrder = new String[] { "A", "E", "B", "D", "C" };
      for (int i = 0 ; i < data.length="" ;="" ++i)="" {="" document="" doc="new" document();="" doc.add(new="" field("tracer",="" tracer[i],="" field.store.yes,="" field.index.no));="" doc.add(new="" field("contents",="" data[i],="" field.store.no,="" field.index.analyzed));="" writer.adddocument(doc);="" }="" writer.close();="" indexsearcher="" searcher="new" indexsearcher(indexstore,="" true);="" sort="" sort="new" sort();="" sort.setsort(new="" sortfield("contents",="" sortfield.string));="" query="" query="new" matchalldocsquery();="" scoredoc[]="" result="searcher.search(query," null,="" 1000,="" sort).scoredocs;="" for="" (int="" i="0" ;="" i="">< result.length="" ;="" ++i)="" {="" document="" doc="searcher.doc(result[i].doc);" assertequals(sortedtracerorder[i],="" doc.getvalues("tracer")[0]);="" }="">

### Turkish Case Normalization

      Collator collator = Collator.getInstance(new ULocale("tr", "TR"));
      collator.setStrength(Collator.PRIMARY);
      Analyzer analyzer = new ICUCollationKeyAnalyzer(Version.LUCENE_48, collator);
      RAMDirectory ramDir = new RAMDirectory();
      IndexWriter writer = new IndexWriter(ramDir, new IndexWriterConfig(Version.LUCENE_48, analyzer));
      Document doc = new Document();
      doc.add(new Field("contents", "DIGY", Field.Store.NO, Field.Index.ANALYZED));
      writer.addDocument(doc);
      writer.close();
      IndexSearcher is = new IndexSearcher(ramDir, true);
      QueryParser parser = new QueryParser(Version.LUCENE_48, "contents", analyzer);
      Query query = parser.parse("d\u0131gy");   // U+0131: dotless i
      ScoreDoc[] result = is.search(query, null, 1000).scoreDocs;
      assertEquals("The index Term should be included.", 1, result.length);

## Caveats and Comparisons

 __WARNING:__ Make sure you use exactly the same `Collator` at index and query time -- `CollationKey`s are only comparable when produced by the same `Collator`. Since {@link java.text.RuleBasedCollator}s are not independently versioned, it is unsafe to search against stored `CollationKey`s unless the following are exactly the same (best practice is to store this information with the index and check that they remain the same at query time): 

1.  JVM vendor

2.  JVM version, including patch version

3.  The language (and country and variant, if specified) of the Locale
    used when constructing the collator via
    {@link java.text.Collator#getInstance(java.util.Locale)}.

4.  The collation strength used - see {@link java.text.Collator#setStrength(int)}

 `ICUCollationKeyAnalyzer` uses ICU4J's `Collator`, which makes its version available, thus allowing collation to be versioned independently from the JVM. `ICUCollationKeyAnalyzer` is also significantly faster and generates significantly shorter keys than `CollationKeyAnalyzer`. See [http://site.icu-project.org/charts/collation-icu4j-sun](http://site.icu-project.org/charts/collation-icu4j-sun) for key generation timing and key length comparisons between ICU4J and `java.text.Collator` over several languages. 

 `CollationKey`s generated by `java.text.Collator`s are not compatible with those those generated by ICU Collators. Specifically, if you use `CollationKeyAnalyzer` to generate index terms, do not use `ICUCollationKeyAnalyzer` on the query side, or vice versa. 

* * *

# [Normalization]()

 `ICUNormalizer2Filter` normalizes term text to a [Unicode Normalization Form](http://unicode.org/reports/tr15/), so that [equivalent](http://en.wikipedia.org/wiki/Unicode_equivalence) forms are standardized to a unique form. 

## Use Cases

*   Removing differences in width for Asian-language text. 

*   Standardizing complex text with non-spacing marks so that characters are 
  ordered consistently.

## Example Usages

### Normalizing text to NFC

      /**
       * Normalizer2 objects are unmodifiable and immutable.
       */
      Normalizer2 normalizer = Normalizer2.getInstance(null, "nfc", Normalizer2.Mode.COMPOSE);
      /**
       * This filter will normalize to NFC.
       */
      TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, normalizer);

* * *

# [Case Folding]()

 Default caseless matching, or case-folding is more than just conversion to lowercase. For example, it handles cases such as the Greek sigma, so that "Μάϊος" and "ΜΆΪΟΣ" will match correctly. 

 Case-folding is still only an approximation of the language-specific rules governing case. If the specific language is known, consider using ICUCollationKeyFilter and indexing collation keys instead. This implementation performs the "full" case-folding specified in the Unicode standard, and this may change the length of the term. For example, the German ß is case-folded to the string 'ss'. 

 Case folding is related to normalization, and as such is coupled with it in this integration. To perform case-folding, you use normalization with the form "nfkc_cf" (which is the default). 

## Use Cases

*   As a more thorough replacement for LowerCaseFilter that has good behavior
    for most languages.

## Example Usages

### Lowercasing text

      /**
       * This filter will case-fold and normalize to NFKC.
       */
      TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer);

* * *

# [Search Term Folding]()

 Search term folding removes distinctions (such as accent marks) between similar characters. It is useful for a fuzzy or loose search. 

 Search term folding implements many of the foldings specified in [Character Foldings](http://www.unicode.org/reports/tr30/tr30-4.html) as a special normalization form. This folding applies NFKC, Case Folding, and many character foldings recursively. 

## Use Cases

*   As a more thorough replacement for ASCIIFoldingFilter and LowerCaseFilter 
    that applies the same ideas to many more languages. 

## Example Usages

### Removing accents

      /**
       * This filter will case-fold, remove accents and other distinctions, and
       * normalize to NFKC.
       */
      TokenStream tokenstream = new ICUFoldingFilter(tokenizer);

* * *

# [Text Transformation]()

 ICU provides text-transformation functionality via its Transliteration API. This allows you to transform text in a variety of ways, taking context into account. 

 For more information, see the [User's Guide](http://userguide.icu-project.org/transforms/general) and [Rule Tutorial](http://userguide.icu-project.org/transforms/general/rules). 

## Use Cases

*   Convert Traditional to Simplified 

*   Transliterate between different writing systems: e.g. Romanization

## Example Usages

### Convert Traditional to Simplified

      /**
       * This filter will map Traditional Chinese to Simplified Chinese
       */
      TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Traditional-Simplified"));

### Transliterate Serbian Cyrillic to Serbian Latin

      /**
       * This filter will map Serbian Cyrillic to Serbian Latin according to BGN rules
       */
      TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Serbian-Latin/BGN"));

* * *

# [Backwards Compatibility]()

 This module exists to provide up-to-date Unicode functionality that supports the most recent version of Unicode (currently 6.3). However, some users who wish for stronger backwards compatibility can restrict <xref:Lucene.Net.Analysis.Icu.ICUNormalizer2Filter> to operate on only a specific Unicode Version by using a {@link com.ibm.icu.text.FilteredNormalizer2}. 

## Example Usages

### Restricting normalization to Unicode 5.0

      /**
       * This filter will do NFC normalization, but will ignore any characters that
       * did not exist as of Unicode 5.0. Because of the normalization stability policy
       * of Unicode, this is an easy way to force normalization to a specific version.
       */
        Normalizer2 normalizer = Normalizer2.getInstance(null, "nfc", Normalizer2.Mode.COMPOSE);
        UnicodeSet set = new UnicodeSet("[:age=5.0:]");
        // see FilteredNormalizer2 docs, the set should be frozen or performance will suffer
        set.freeze(); 
        FilteredNormalizer2 unicode50 = new FilteredNormalizer2(normalizer, set);
        TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, unicode50);