LangDetect.Net 1.0.1

LangDetect.Net

Lightweight, self-contained language detection for .NET — no cloud, no Python, no runtime dependencies.

LangDetect is a .NET 8 class library for detecting the language of a given text string. It uses a multi-stage detection pipeline — Unicode script analysis, common word frequency matching, and character trigram profiling — to identify languages accurately across both native scripts and romanized Latin representations.

Supported Languages

Installation

dotnet add package LangDetect.Net

Or via the NuGet Package Manager in Visual Studio — search for LangDetect.Net.

Quick Start

using LangDetect;
using LangDetect.Models;

// create a detector with default options
var factory  = new LanguageDetectorFactory();
var detector = factory.Create();

var result = detector.Detect("The quick brown fox jumps over the lazy dog");

Console.WriteLine(result.Language);    // English
Console.WriteLine(result.IsoCode);     // en
Console.WriteLine(result.Confidence);  // 1.00
Console.WriteLine(result.IsReliable);  // True
Console.WriteLine(result.DetectedBy);  // CommonWordDetectionStage

Detection Result

Every call to Detect() returns a DetectionResult record:

public record DetectionResult
{
    public Language Language     { get; init; }  // detected language or Unknown
    public float    Confidence   { get; init; }  // 0.0 – 1.0
    public bool     IsReliable   { get; init; }  // confidence >= configured threshold
    public string   DetectedBy   { get; init; }  // which pipeline stage fired
    public string   IsoCode      { get; init; }  // ISO 639-1 code e.g. "en", "si"
}

When detection fails or input is too short, DetectionResult.Unknown is returned — Detect() never throws for valid string input.

Configuration

var detector = new LanguageDetectorFactory().Create(new DetectorOptions
{
    ConfidenceThreshold = 0.80f,          // minimum score to be considered reliable
    EnableEarlyExit     = true,           // stop pipeline once confident result found
    WordListSize        = WordListSize.Large, // Small (200) | Medium (500) | Large (1000)
    MinInputLength      = 3,              // inputs shorter than this return Unknown
    MaxTokens           = 500,            // truncate long inputs before analysis
    MinNonLatinRatio    = 0.25f,          // minimum non-Latin ratio to trigger Unicode path
    Logger              = Console.WriteLine, // optional diagnostic logger
});

Word list sizes

Detection Pipeline

LangDetect uses a three-stage pipeline. Each stage runs in priority order and the result is returned as soon as a confident detection is made (early exit).

Input text
    │
    ▼
TextPreprocessor
    │  normalize → tokenize → compute HasNonLatinUnicode + NonLatinRatio
    ▼
Does text contain non-Latin Unicode above MinNonLatinRatio threshold?
    │
    ├── YES → Stage 1: UnicodeDetectionStage
    │              Checks script coverage against 7 Unicode range profiles
    │              Confident result → return early
    │              Not confident   → fall through to Stage 2 + 3
    │
    └── NO  → Stage 2: CommonWordDetectionStage
                   Matches tokens against romanized word lists
                   Confident result → return early
                   Not confident   → Stage 3: NGramDetectionStage
                                         Scores character trigram profiles
                                         Returns best match or Unknown

Stage details

Examples

Native script detection

detector.Detect("مرحبا كيف حالك اليوم");
// → { Language: Arabic, Confidence: 1.00, IsReliable: true, IsoCode: "ar" }

detector.Detect("नमस्ते आप कैसे हैं");
// → { Language: Hindi, Confidence: 1.00, IsReliable: true, IsoCode: "hi" }

detector.Detect("こんにちは世界");
// → { Language: Japanese, Confidence: 0.81, IsReliable: true, IsoCode: "ja" }

Romanized script detection

detector.Detect("mama giye koheda kiyala amma");
// → { Language: Sinhala, Confidence: 0.85, IsReliable: true, IsoCode: "si" }

detector.Detect("naan pogiren enna romba thanks");
// → { Language: Tamil, Confidence: 0.80, IsReliable: true, IsoCode: "ta" }

Graceful unknown handling

detector.Detect("");          // → DetectionResult.Unknown
detector.Detect("123456");   // → DetectionResult.Unknown
detector.Detect(null);       // → DetectionResult.Unknown (never throws)

Diagnostic logging

var detector = new LanguageDetectorFactory().Create(new DetectorOptions
{
    Logger = msg => Debug.WriteLine(msg),
});

Output:

[LangDetect] Attempting to load resource: 'LangDetect.Resources.Wordlists.English-1000-Wordlist.txt'
[LangDetect] SUCCESS: Loaded 'LangDetect.Resources.Wordlists.English-1000-Wordlist.txt'
[LangDetect] Loaded 1000 words for 'English'

ISO Language Codes

using LangDetect.Utility;

LanguageCode.ToIso(Language.English);   // "en"
LanguageCode.ToIso(Language.Sinhala);   // "si"
LanguageCode.ToIso(Language.Unknown);   // "und"

LanguageCode.FromIso("ar");             // Language.Arabic
LanguageCode.FromIso("zh");             // Language.Mandarin
LanguageCode.FromIso("xyz");            // Language.Unknown

Known Limitations

• Mixed-script text
• Very short inputs
• Romanized Mandarin (Pinyin)
• No multi-language detection — a single Detect() call returns one language. Mixed documents are planned for v2.
• N-gram profiles are derived from word lists — trigram quality is directly proportional to word list quality and size.

Roadmap

Planned features in V2

• Multi-language detection — ranked candidate list with per-language confidence scores
• Code-switching support — detect language changes within a single document
• Expanded language support — French, Spanish, Portuguese, German, Russian
• Compact ONNX model for Latin-script disambiguation
• Dialect identification — Mandarin vs Cantonese, Indian English vs British English
• Streaming / span detection over long documents
• Calibrated confidence scores via isotonic regression
• Proper benchmark suite with labeled test dataset

Contributing

Contributions are welcome. Please open an issue before submitting a pull request for non-trivial changes.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Commit your changes
4. Push to the branch and open a Pull Request

Generating trigram data

If you update the word lists, regenerate the trigram JSON files using the included tool:

cd LangDetect.TrigramGenerator
dotnet run

Then copy the output from Resources/Trigrams/ into LangDetect/Resources/Trigrams/ and rebuild.

License

This project is licensed under the GNU General Public License v3.0.