Custom Dictionaries

There is no “one-size-fits-all” Myanmar dictionary. Medical reports, news articles, and social media posts use different vocabularies. mySpellChecker ships without a bundled dictionary intentionally: you build one from your own text corpus using the data pipeline, ensuring the vocabulary matches exactly what your users write.

Why Custom Dictionaries?

Custom dictionaries let you tailor the vocabulary for:

Domain terminology - Medical, legal, technical terms
Organization names - Company names, product names
Regional variations - Dialect-specific words
New vocabulary - Recent additions to the language

Building a Dictionary

Using Curated Lexicons

A curated lexicon is a carefully verified list of words that you want to mark as trusted in the database. Words from curated lexicons are stored with is_curated=1, ensuring they are always recognized as valid vocabulary. Key Feature: Curated words are inserted directly into the database before corpus processing. This ensures all curated vocabulary is included regardless of whether they appear in the corpus. Create a curated lexicon CSV file with a word column header:

word
ဆေးရုံ
ဆရာဝန်
လူနာ
ကုမ္ပဏီ

Build with curated lexicon:

# Build database with curated words marked as trusted
myspellchecker build -i corpus.txt -o dictionary.db \
  --curated-input curated_lexicon.csv

The curated lexicon can be combined with other build options:

# Combine corpus + curated lexicon + transformer POS tagging
myspellchecker build -i corpus.txt -o dictionary.db \
  --curated-input data/curated_lexicon.csv \
  --pos-tagger transformer \
  --min-frequency 5

How curated words are processed:

Pipeline Flow:
1. load_curated_words()  →  INSERT (freq=0, is_curated=1, syllables segmented)
2. load_words() (corpus) →  UPDATE frequency, preserve is_curated=1

Scenario	frequency	is_curated
Curated only (not in corpus)	0	1
Curated + corpus overlap	corpus_freq	1
Corpus only	corpus_freq	0

Benefits:

All curated words are in the database regardless of corpus coverage
Frequency is accurate from corpus (when word appears)
Syllable segmentation is applied for syllable_count
is_curated=1 is preserved even when corpus updates frequency

Preparing curated lexicons: Use the scripts/merge_vocabulary.py utility to merge and deduplicate vocabulary files:

# Merge CSV vocabulary files
python scripts/merge_vocabulary.py /path/to/csv/folder -o data/curated_lexicon.csv

# Merge CSV and TXT files
python scripts/merge_vocabulary.py /path/to/csv -t /path/to/text/files -o data/curated_lexicon.csv

# Append new files to existing lexicon
python scripts/merge_vocabulary.py -t /path/to/new/files --append -o data/curated_lexicon.csv

Priority hierarchy during database build:

Curated words inserted first (--curated-input) → is_curated=1, freq=0
Corpus words loaded → frequency updated, is_curated preserved via MAX()

From Text Corpus

# Prepare a text file with domain content
myspellchecker build --input medical_corpus.txt --output medical.db

# With frequency threshold
myspellchecker build --input corpus.txt --output custom.db --min-frequency 2

From CSV

text,frequency,pos
ဆေးရုံ,5000,N
ဆရာဝန်,3000,N
လူနာ,2500,N

myspellchecker build --input medical_terms.csv --output medical.db

From JSON

{
  "entries": [
    {"text": "ဆေးရုံ", "frequency": 5000, "pos": "N"},
    {"text": "ဆရာဝန်", "frequency": 3000, "pos": "N"}
  ]
}

myspellchecker build --input terms.json --output custom.db

Using Custom Dictionaries

Single Custom Dictionary

from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider

# Use custom dictionary
provider = SQLiteProvider(database_path="medical.db")
checker = SpellChecker(provider=provider)

result = checker.check("ဆရာဝန်က လူနာကို ကြည့်သည်")

Using Multiple Data Sources

To combine vocabulary from multiple sources, use the data pipeline to merge them into a single database:

from myspellchecker import SpellChecker
from myspellchecker.data_pipeline import Pipeline, PipelineConfig

# Build a unified database from multiple corpora
pipeline = Pipeline()
pipeline.build_database(
    input_files=["general_corpus.txt", "medical_corpus.txt", "organization_names.txt"],
    database_path="combined.db",
)

# Use the combined database via SQLiteProvider
from myspellchecker.providers import SQLiteProvider
provider = SQLiteProvider(database_path="combined.db")
checker = SpellChecker(provider=provider)

Alternative: Sequential Lookup

For runtime lookup across multiple databases, use custom logic:

from myspellchecker.providers import SQLiteProvider

class MultiProvider:
    """Custom provider that checks multiple databases."""

    def __init__(self, db_paths: list):
        self.providers = [SQLiteProvider(database_path=p) for p in db_paths]

    def is_valid_word(self, word: str) -> bool:
        return any(p.is_valid_word(word) for p in self.providers)

Python API for Building

Basic Pipeline

from myspellchecker.data_pipeline import Pipeline, PipelineConfig

# Configure pipeline settings
config = PipelineConfig(
    min_frequency=2,
    batch_size=50000,
)

# Create pipeline with config
pipeline = Pipeline(config=config)

# Build database from corpus files
pipeline.build_database(
    input_files=["corpus.txt"],
    database_path="custom.db",
)

With POS Tagging

from myspellchecker.core.config import POSTaggerConfig

# Configure POS tagger
pos_config = POSTaggerConfig(
    tagger_type="viterbi",
)

pipeline = Pipeline()
pipeline.build_database(
    input_files=["corpus.txt"],
    database_path="custom.db",
    pos_tagger_config=pos_config,
)

Incremental Updates

Add new words without rebuilding from scratch:

pipeline = Pipeline()
pipeline.build_database(
    input_files=["new_words.txt"],
    database_path="existing.db",
    incremental=True,  # Merge with existing data
)

Customizing Dictionary Content

Dictionary content is managed through the data pipeline by modifying your input corpus files. The pipeline builds a fresh database each time, ensuring consistency.

Adding New Words

Add new vocabulary by including them in your corpus or creating a supplementary file:

# Create a supplementary corpus file with domain terms
domain_terms = """
ကုမ္ပဏီအသစ်
အမည်သစ်တစ်ခု
ဝန်ဆောင်မှုသစ်
"""

with open("domain_terms.txt", "w", encoding="utf-8") as f:
    f.write(domain_terms)

# Rebuild database with the new terms included
from myspellchecker.data_pipeline import Pipeline

pipeline = Pipeline()
pipeline.build_database(
    input_files=["main_corpus.txt", "domain_terms.txt"],
    database_path="custom.db",
)

Filtering Low-Frequency Words

Control which words are included using the min_frequency parameter:

from myspellchecker.data_pipeline import Pipeline, PipelineConfig

# Only include words appearing 5+ times
config = PipelineConfig(min_frequency=5)
pipeline = Pipeline(config=config)
pipeline.build_database(
    input_files=["corpus.txt"],
    database_path="filtered.db",
)

Combining Multiple Corpora

The recommended approach is to combine corpora at build time rather than merging databases:

from myspellchecker.data_pipeline import Pipeline

pipeline = Pipeline()

# Combine multiple source files into one database
pipeline.build_database(
    input_files=[
        "general_corpus.txt",
        "domain_specific.txt",
        "organization_names.txt",
    ],
    database_path="merged.db",
)

Validation and Testing

Test Coverage

from myspellchecker.providers import SQLiteProvider

def test_dictionary_coverage(test_words: list, db_path: str) -> dict:
    """Test how many words are covered by dictionary."""
    provider = SQLiteProvider(database_path=db_path)

    found = sum(1 for w in test_words if provider.is_valid_word(w))

    return {
        "total": len(test_words),
        "found": found,
        "coverage": found / len(test_words) * 100,
    }

result = test_dictionary_coverage(domain_terms, "custom.db")
print(f"Coverage: {result['coverage']:.1f}%")

Best Practices

Corpus Quality

Clean input - Remove HTML, special characters
Normalize encoding - Ensure UTF-8, convert Zawgyi
Remove duplicates - Deduplicate sentences
Balance content - Include variety of contexts

Dictionary Size

Use Case	Recommended Size
Quick testing	1,000-10,000 words
Domain-specific	10,000-50,000 words
General use	50,000-200,000 words
Comprehensive	200,000+ words

Frequency Thresholds

from myspellchecker.data_pipeline import PipelineConfig

# Choose threshold based on corpus quality
config_rare = PipelineConfig(min_frequency=1)     # Include rare words (noisy)
config_balanced = PipelineConfig(min_frequency=2) # Exclude hapax legomena
config_clean = PipelineConfig(min_frequency=5)    # Only common words

Troubleshooting

Missing Words

from myspellchecker.providers import SQLiteProvider

# Check if word exists
provider = SQLiteProvider(database_path="custom.db")
exists = provider.is_valid_word("ရှာနေသောစကား")
print(f"Word exists: {exists}")

# Check word frequency
freq = provider.get_word_frequency("ရှာနေသောစကား")
print(f"Frequency: {freq}")

Wrong Suggestions

Low-frequency words get lower suggestion priority. To boost frequency for specific words, include them more times in your corpus or create a supplementary file:

# Create supplementary file with repeated important terms
important_terms = "စကား\n" * 100  # Repeat 100 times to boost frequency
with open("boost_terms.txt", "w", encoding="utf-8") as f:
    f.write(important_terms)

# Rebuild database with boosted terms
pipeline.build_database(
    input_files=["corpus.txt", "boost_terms.txt"],
    database_path="custom.db",
)

Large Dictionary Performance

from myspellchecker.core.config import SpellCheckerConfig, AlgorithmCacheConfig

# Increase cache sizes for large dictionaries
config = SpellCheckerConfig(
    cache=AlgorithmCacheConfig(
        word_cache_size=50000,
        syllable_cache_size=20000,
    )
)

Getting Started

Dictionary Building

Text Processing

Spelling & Text Checking

Grammar & NER

Performance & APIs

Customization

Integration & Deployment

Help & FAQ

Why Custom Dictionaries?

Building a Dictionary

Using Curated Lexicons

From Text Corpus

From CSV

From JSON

Using Custom Dictionaries

Single Custom Dictionary

Using Multiple Data Sources

Alternative: Sequential Lookup

Python API for Building

Basic Pipeline

With POS Tagging

Incremental Updates

Customizing Dictionary Content

Adding New Words

Filtering Low-Frequency Words

Combining Multiple Corpora

Validation and Testing

Test Coverage

Best Practices

Corpus Quality

Dictionary Size

Frequency Thresholds

Troubleshooting

Missing Words

Wrong Suggestions

Large Dictionary Performance

See Also

​Why Custom Dictionaries?

​Building a Dictionary

​Using Curated Lexicons

​From Text Corpus

​From CSV

​From JSON

​Using Custom Dictionaries

​Single Custom Dictionary

​Using Multiple Data Sources

​Alternative: Sequential Lookup

​Python API for Building

​Basic Pipeline

​With POS Tagging

​Incremental Updates

​Customizing Dictionary Content

​Adding New Words

​Filtering Low-Frequency Words

​Combining Multiple Corpora

​Validation and Testing

​Test Coverage

​Best Practices

​Corpus Quality

​Dictionary Size

​Frequency Thresholds

​Troubleshooting

​Missing Words

​Wrong Suggestions

​Large Dictionary Performance

​See Also

Why Custom Dictionaries?

Building a Dictionary

Using Curated Lexicons

From Text Corpus

From CSV

From JSON

Using Custom Dictionaries

Single Custom Dictionary

Using Multiple Data Sources

Alternative: Sequential Lookup

Python API for Building

Basic Pipeline

With POS Tagging

Incremental Updates

Customizing Dictionary Content

Adding New Words

Filtering Low-Frequency Words

Combining Multiple Corpora

Validation and Testing

Test Coverage

Best Practices

Corpus Quality

Dictionary Size

Frequency Thresholds

Troubleshooting

Missing Words

Wrong Suggestions

Large Dictionary Performance

See Also