cadar 0.1.6

Canonicalization and Darija Representation - Bidirectional transliteration for Darija

CaDaR: Canonicalization and Darija Representation

High-performance bidirectional transliteration for Darija (Moroccan Arabic)

CaDaR is a robust, FST-style (Finite State Transducer) transliteration library that provides seamless conversion between Arabic script and Latin (Romanized/Bizi) script for Darija, with text standardization capabilities.

Features

• 🔄 Bidirectional transliteration: Arabic ↔ Latin
• 🎯 Darija-aware processing: Moroccan Arabic linguistic patterns
• 🚀 High performance: Rust core with Python bindings
• ✨ Text standardization: Normalize both Arabic and Latin text
• 🧪 Well tested: 41 passing unit tests

Installation

pip install cadar

Quick Start

Arabic to Latin (Bizi)

import cadar

result = cadar.ara2bizi("كيفاش داير؟", darija="Ma")
print(result)  # Output: "kifash dayer?"

Latin to Arabic

import cadar

result = cadar.bizi2ara("salam 3likom", darija="Ma")
print(result)  # Output: "سلام عليكم"

Text Standardization

import cadar

# Standardize Arabic (remove diacritics, normalize)
result = cadar.ara2ara("أنَا مِنْ المَغْرِب", darija="Ma")
print(result)  # Output: "انا من المغرب"

# Standardize Latin (fix repeated chars)
result = cadar.bizi2bizi("salaaaam", darija="Ma")
print(result)  # Output: "salam"

Using the CaDaR Class

import cadar

# Create a reusable processor
processor = cadar.CaDaR(darija="Ma")

# Convert between scripts
arabic = processor.bizi2ara("wakha ghir shwiya")
latin = processor.ara2bizi("واخا غير شوية")

print(f"Dialect: {processor.get_dialect()}")

API Reference

Main Functions

• ara2bizi(text, darija="Ma") - Convert Arabic to Latin script
• bizi2ara(text, darija="Ma") - Convert Latin to Arabic script
• ara2ara(text, darija="Ma") - Standardize Arabic text
• bizi2bizi(text, darija="Ma") - Standardize Latin text

CaDaR Class

processor = cadar.CaDaR(darija="Ma")
processor.ara2bizi(text)    # Arabic to Latin
processor.bizi2ara(text)    # Latin to Arabic
processor.ara2ara(text)     # Standardize Arabic
processor.bizi2bizi(text)   # Standardize Latin
processor.get_dialect()     # Get current dialect

Convenience Functions

# Auto-detect and transliterate
cadar.transliterate(text, target="latin", darija="Ma")

# Auto-detect and standardize
cadar.standardize(text, script="auto", darija="Ma")

Use Cases

• Chat Applications: Support users writing in both scripts
• Search Engines: Match queries regardless of script
• Data Processing: Standardize mixed-script datasets
• NLP Pipelines: Normalize Darija text for machine learning
• Language Learning: See connections between scripts

How It Works

CaDaR uses a 6-stage FST-style pipeline:

1. Script Detection: Identify Arabic, Latin, or mixed scripts
2. Normalization: Clean and standardize input
3. Tokenization: Darija-aware word segmentation
4. ICR Generation: Convert to Intermediate Canonical Representation
5. Script Generation: Produce target script output
6. Validation: Apply final fixes and quality checks

The Intermediate Canonical Representation (ICR) is the core innovation - a script-independent phonological representation that enables accurate bidirectional conversion.

Supported Dialects

• Ma (Moroccan Darija) - Default

Support for Algerian, Tunisian, Libyan, and Egyptian dialects planned for future releases.

Examples

Chat Normalization

import cadar

def normalize_message(text):
    """Normalize user input regardless of script"""
    has_arabic = any('\u0600' <= c <= '\u06FF' for c in text)

    if has_arabic:
        return cadar.ara2ara(text, darija="Ma")
    else:
        return cadar.bizi2bizi(text, darija="Ma")

# Usage
user_input = "salaaaam 3likooom"
normalized = normalize_message(user_input)
print(normalized)  # "salam 3likom"

Search Variants

import cadar

def generate_search_variants(query):
    """Generate search terms in both scripts"""
    processor = cadar.CaDaR(darija="Ma")

    has_arabic = any('\u0600' <= c <= '\u06FF' for c in query)

    if has_arabic:
        return [
            query,
            processor.ara2bizi(query),
            processor.ara2ara(query)
        ]
    else:
        return [
            query,
            processor.bizi2ara(query),
            processor.bizi2bizi(query)
        ]

# Usage
variants = generate_search_variants("سلام")
print(variants)  # ['سلام', 'slam', 'سلام']

Batch Processing

import cadar

processor = cadar.CaDaR(darija="Ma")

texts = ["سلام", "بسلامة", "شكرا", "بزاف"]
results = [processor.ara2bizi(text) for text in texts]

print(results)  # ['slam', 'bslama', 'shokran', 'bzaf']

Performance

• Fast: ~1-2ms per sentence
• Efficient: Rust core with minimal overhead
• Scalable: Linear scaling for batch processing

Documentation

• Full Documentation: https://oit-technologies.github.io/CaDaR/
• GitHub Repository: https://github.com/Oit-Technologies/CaDaR
• API Reference: https://oit-technologies.github.io/CaDaR/api/python/

License

MIT License - see LICENSE file for details.

Citation

If you use CaDaR in your research, please cite:

@software{cadar2026,
  title={CaDaR: Canonicalization and Darija Representation},
  author={Ouail LAAMIRI},
  year={2026},
  url={https://github.com/Oit-Technologies/CaDaR}
}

Support

• Issues: GitHub Issues
• Documentation: https://oit-technologies.github.io/CaDaR/

---

Made with ❤️ for the Darija community by Oit Technologies