src/Lucene.Net.Analysis.ICU/overview.md - lucenenet - Git at Google

 ---
 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. ICU4N is a .NET
 library that enhances .NET's internationalization support by improving
 performance, keeping current with the Unicode Standard, and providing richer
 APIs.

 > [!NOTE]
 > The <xref:Lucene.Net.Analysis.Icu> namespace was ported from Lucene 7.1.0 to get a more up-to-date version of Unicode than what shipped with Lucene 4.8.0.

 > [!NOTE]
 > Since the .NET platform doesn't provide a BreakIterator class (or similar), the functionality that utilizes it was consolidated from Java Lucene's analyzers-icu package, <xref:Lucene.Net.Analysis.Common> and <xref:Lucene.Net.Highlighter> into this unified package.
 > [!WARNING]
 > While ICU4N's BreakIterator has customizable rules, its default behavior is not the same as the one in the JDK. When using any features of this package outside of the <xref:Lucene.Net.Analysis.Icu> namespace, they will behave differently than they do in Java Lucene and the rules may need some tweaking to fit your needs. See the [Break Rules](http://userguide.icu-project.org/boundaryanalysis/break-rules) ICU documentation for details on how to customize `ICU4N.Text.RuleBaseBreakIterator`.


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

  This module exposes the following functionality:

 *   [Text Segmentation](#text-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](#case-folding): Removes case distinctions with
   Unicode's Default Caseless Matching algorithm.

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

 *   [Text Transformation](#text-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

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

 * * *

 # Collation

  <xref:Lucene.Net.Collation.ICUCollationKeyAnalyzer> converts each token into its binary `CollationKey` using the provided `Collator`, allowing it to be stored as an index term.

  <xref:Lucene.Net.Collation.ICUCollationKeyAnalyzer> depends on ICU4N to produce the `CollationKey`s.

 ## Use Cases

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

 *   Efficient range queries over fields that contain terms in languages that
     use non-Unicode character orderings.  (Range queries using a CultureInfo 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

 ```cs
 const LuceneVersion matchVersion = LuceneVersion.LUCENE_48;
 Collator collator = Collator.GetInstance(new UCultureInfo("ar"));
 ICUCollationKeyAnalyzer analyzer = new ICUCollationKeyAnalyzer(matchVersion, collator);
 RAMDirectory ramDir = new RAMDirectory();
 using IndexWriter writer = new IndexWriter(ramDir, new IndexWriterConfig(matchVersion, analyzer));
 writer.AddDocument(new Document {
     new TextField("content", "\u0633\u0627\u0628", Field.Store.YES)
 });
 using IndexReader reader = writer.GetReader(applyAllDeletes: true);
 writer.Dispose();
 IndexSearcher searcher = new IndexSearcher(reader);

 QueryParser queryParser = new QueryParser(matchVersion, "content", analyzer)
 {
     AnalyzeRangeTerms = 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 = searcher.Search(queryParser.Parse("[ \u062F TO \u0698 ]"), null, 1000).ScoreDocs;
 Assert.AreEqual(0, result.Length, "The index Term should not be included.");
 ```

 ### Danish Sorting

 ```cs
 const LuceneVersion matchVersion = LuceneVersion.LUCENE_48;
 Analyzer analyzer = new ICUCollationKeyAnalyzer(matchVersion, Collator.GetInstance(new UCultureInfo("da-dk")));
 string indexPath = Path.Combine(Path.GetTempPath(), Path.GetFileNameWithoutExtension(Path.GetTempFileName()));
 Directory dir = FSDirectory.Open(indexPath);
 using IndexWriter writer = new IndexWriter(dir, new IndexWriterConfig(matchVersion, 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)
 {
     writer.AddDocument(new Document
     {
         new StringField("tracer", tracer[i], Field.Store.YES),
         new TextField("contents", data[i], Field.Store.NO)
     });
 }
 using IndexReader reader = writer.GetReader(applyAllDeletes: true);
 writer.Dispose();
 IndexSearcher searcher = new IndexSearcher(reader);
 Sort sort = new Sort();
 sort.SetSort(new SortField("contents",  SortFieldType.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);
     Assert.AreEqual(sortedTracerOrder[i], doc.GetValues("tracer")[0]);
 }
 ```

 ### Turkish Case Normalization

 ```cs
 const LuceneVersion matchVersion = LuceneVersion.LUCENE_48;
 Collator collator = Collator.GetInstance(new UCultureInfo("tr-TR"));
 collator.Strength = CollationStrength.Primary;
 Analyzer analyzer = new ICUCollationKeyAnalyzer(matchVersion, collator);
 string indexPath = Path.Combine(Path.GetTempPath(), Path.GetFileNameWithoutExtension(Path.GetTempFileName()));
 Directory dir = FSDirectory.Open(indexPath);
 using IndexWriter writer = new IndexWriter(dir, new IndexWriterConfig(matchVersion, analyzer));
 writer.AddDocument(new Document {
     new TextField("contents", "DIGY", Field.Store.NO)
 });
 using IndexReader reader = writer.GetReader(applyAllDeletes: true);
 writer.Dispose();
 IndexSearcher searcher = new IndexSearcher(reader);
 QueryParser parser = new QueryParser(matchVersion, "contents", analyzer);
 Query query = parser.Parse("d\u0131gy");   // U+0131: dotless i
 ScoreDoc[] result = searcher.Search(query, null, 1000).ScoreDocs;
 Assert.AreEqual(1, result.Length, "The index Term should be included.");
 ```

 ## Caveats and Comparisons

  `ICUCollationKeyAnalyzer` uses ICU4N's `Collator`, which makes its version available, thus allowing collation to be versioned independently from the .NET target framework. `ICUCollationKeyAnalyzer` is also fast.

  `SortKey`s generated by `CompareInfo`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

  <xref:Lucene.Net.Analysis.Icu.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

 ```cs
 // Normalizer2 objects are unmodifiable and immutable.
 Normalizer2 normalizer = Normalizer2.GetInstance(null, "nfc", Normalizer2Mode.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

 ```cs
 // 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

 ```cs
 // 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

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

 ### Transliterate Serbian Cyrillic to Serbian Latin

 ```cs
 // 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 8.0). 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 FilteredNormalizer2.

 ## Example Usages

 ### Restricting normalization to Unicode 5.0

 ```cs
 // 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", Normalizer2Mode.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);
 ```
	---
	uid: Lucene.Net.Analysis.Icu
	summary: *content
	---

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	this work for additional information regarding copyright ownership.
	The ASF licenses this file to You under the Apache License, Version 2.0
	(the "License"); you may not use this file except in compliance with
	the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
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	See the License for the specific language governing permissions and
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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. ICU4N is a .NET
	library that enhances .NET's internationalization support by improving
	performance, keeping current with the Unicode Standard, and providing richer
	APIs.

	> [!NOTE]
	> The <xref:Lucene.Net.Analysis.Icu> namespace was ported from Lucene 7.1.0 to get a more up-to-date version of Unicode than what shipped with Lucene 4.8.0.

	> [!NOTE]
	> Since the .NET platform doesn't provide a BreakIterator class (or similar), the functionality that utilizes it was consolidated from Java Lucene's analyzers-icu package, <xref:Lucene.Net.Analysis.Common> and <xref:Lucene.Net.Highlighter> into this unified package.
	> [!WARNING]
	> While ICU4N's BreakIterator has customizable rules, its default behavior is not the same as the one in the JDK. When using any features of this package outside of the <xref:Lucene.Net.Analysis.Icu> namespace, they will behave differently than they do in Java Lucene and the rules may need some tweaking to fit your needs. See the [Break Rules](http://userguide.icu-project.org/boundaryanalysis/break-rules) ICU documentation for details on how to customize `ICU4N.Text.RuleBaseBreakIterator`.



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

	This module exposes the following functionality:

	* [Text Segmentation](#text-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](#case-folding): Removes case distinctions with
	Unicode's Default Caseless Matching algorithm.

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

	* [Text Transformation](#text-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

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

	* * *

	# Collation

	<xref:Lucene.Net.Collation.ICUCollationKeyAnalyzer> converts each token into its binary `CollationKey` using the provided `Collator`, allowing it to be stored as an index term.

	<xref:Lucene.Net.Collation.ICUCollationKeyAnalyzer> depends on ICU4N to produce the `CollationKey`s.

	## Use Cases

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

	* Efficient range queries over fields that contain terms in languages that
	use non-Unicode character orderings. (Range queries using a CultureInfo 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

	```cs
	const LuceneVersion matchVersion = LuceneVersion.LUCENE_48;
	Collator collator = Collator.GetInstance(new UCultureInfo("ar"));
	ICUCollationKeyAnalyzer analyzer = new ICUCollationKeyAnalyzer(matchVersion, collator);
	RAMDirectory ramDir = new RAMDirectory();
	using IndexWriter writer = new IndexWriter(ramDir, new IndexWriterConfig(matchVersion, analyzer));
	writer.AddDocument(new Document {
	new TextField("content", "\u0633\u0627\u0628", Field.Store.YES)
	});
	using IndexReader reader = writer.GetReader(applyAllDeletes: true);
	writer.Dispose();
	IndexSearcher searcher = new IndexSearcher(reader);

	QueryParser queryParser = new QueryParser(matchVersion, "content", analyzer)
	{
	AnalyzeRangeTerms = 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 = searcher.Search(queryParser.Parse("[ \u062F TO \u0698 ]"), null, 1000).ScoreDocs;
	Assert.AreEqual(0, result.Length, "The index Term should not be included.");
	```

	### Danish Sorting

	```cs
	const LuceneVersion matchVersion = LuceneVersion.LUCENE_48;
	Analyzer analyzer = new ICUCollationKeyAnalyzer(matchVersion, Collator.GetInstance(new UCultureInfo("da-dk")));
	string indexPath = Path.Combine(Path.GetTempPath(), Path.GetFileNameWithoutExtension(Path.GetTempFileName()));
	Directory dir = FSDirectory.Open(indexPath);
	using IndexWriter writer = new IndexWriter(dir, new IndexWriterConfig(matchVersion, 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)
	{
	writer.AddDocument(new Document
	{
	new StringField("tracer", tracer[i], Field.Store.YES),
	new TextField("contents", data[i], Field.Store.NO)
	});
	}
	using IndexReader reader = writer.GetReader(applyAllDeletes: true);
	writer.Dispose();
	IndexSearcher searcher = new IndexSearcher(reader);
	Sort sort = new Sort();
	sort.SetSort(new SortField("contents", SortFieldType.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);
	Assert.AreEqual(sortedTracerOrder[i], doc.GetValues("tracer")[0]);
	}
	```

	### Turkish Case Normalization

	```cs
	const LuceneVersion matchVersion = LuceneVersion.LUCENE_48;
	Collator collator = Collator.GetInstance(new UCultureInfo("tr-TR"));
	collator.Strength = CollationStrength.Primary;
	Analyzer analyzer = new ICUCollationKeyAnalyzer(matchVersion, collator);
	string indexPath = Path.Combine(Path.GetTempPath(), Path.GetFileNameWithoutExtension(Path.GetTempFileName()));
	Directory dir = FSDirectory.Open(indexPath);
	using IndexWriter writer = new IndexWriter(dir, new IndexWriterConfig(matchVersion, analyzer));
	writer.AddDocument(new Document {
	new TextField("contents", "DIGY", Field.Store.NO)
	});
	using IndexReader reader = writer.GetReader(applyAllDeletes: true);
	writer.Dispose();
	IndexSearcher searcher = new IndexSearcher(reader);
	QueryParser parser = new QueryParser(matchVersion, "contents", analyzer);
	Query query = parser.Parse("d\u0131gy"); // U+0131: dotless i
	ScoreDoc[] result = searcher.Search(query, null, 1000).ScoreDocs;
	Assert.AreEqual(1, result.Length, "The index Term should be included.");
	```

	## Caveats and Comparisons

	`ICUCollationKeyAnalyzer` uses ICU4N's `Collator`, which makes its version available, thus allowing collation to be versioned independently from the .NET target framework. `ICUCollationKeyAnalyzer` is also fast.

	`SortKey`s generated by `CompareInfo`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

	<xref:Lucene.Net.Analysis.Icu.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

	```cs
	// Normalizer2 objects are unmodifiable and immutable.
	Normalizer2 normalizer = Normalizer2.GetInstance(null, "nfc", Normalizer2Mode.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

	```cs
	// 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

	```cs
	// 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

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

	### Transliterate Serbian Cyrillic to Serbian Latin

	```cs
	// 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 8.0). 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 FilteredNormalizer2.

	## Example Usages

	### Restricting normalization to Unicode 5.0

	```cs
	// 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", Normalizer2Mode.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);
	```