Quick Start Guide

Get up and running with mySpellChecker in 5 minutes. This guide covers the essential concepts and most common use cases.

Prerequisite: You must build a dictionary database before spell checking will work. mySpellChecker does not include a bundled dictionary.

Setup

Before checking spelling, you need a dictionary database:

# Build a sample database for testing
myspellchecker build --sample

This creates ./mySpellChecker-default.db in the current directory. For production, build from your own corpus:

myspellchecker build --input your_corpus.txt --output dictionary.db

Your First Spell Check

Quick Check (One-liner)

from myspellchecker import check_text

# One-liner spell check
result = check_text("မြန်မာနိုင်ငံသည်အာရှတွင်ရှိသည်")
print(f"Has errors: {result.has_errors}")

Standard Usage

from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider

# Create a spell checker instance (requires a built database)
checker = SpellChecker(provider=SQLiteProvider(database_path="mySpellChecker-default.db"))

# Check some text
text = "မြန်မာနိုင်ငံသည်အာရှတွင်ရှိသည်"
result = checker.check(text)

# Examine the result
print(f"Original: {result.text}")
print(f"Has errors: {result.has_errors}")
print(f"Error count: {len(result.errors)}")

Understanding Results

The check() method returns a Response object:

result = checker.check("ကျေးဇူးတင်ပါသည်")

# Access the original text
print(result.text)  # "ကျေးဇူးတင်ပါသည်"

# Check if errors exist
if result.has_errors:
    # Iterate through errors
    for error in result.errors:
        print(f"Position: {error.position}")
        print(f"Error text: {error.text}")
        print(f"Error type: {error.error_type}")
        print(f"Suggestions: {error.suggestions}")
        print(f"Confidence: {error.confidence}")

Error Types

mySpellChecker identifies several types of errors:

Error Type	Value	Description	Example
`ErrorType.SYLLABLE`	`invalid_syllable`	Invalid syllable structure	Misspelled character combinations
`ErrorType.WORD`	`invalid_word`	Valid syllables but unknown word	Compound word errors
`ErrorType.GRAMMAR`	`grammar_error`	Syntactic/grammar issues	Wrong particle usage
`ErrorType.CONTEXT_PROBABILITY`	`context_probability`	Low probability word sequence	”Rice go” instead of “Rice eat”

from myspellchecker.core.constants import ErrorType

for error in result.errors:
    if error.error_type == ErrorType.SYLLABLE:
        print("Syllable-level error (typo)")
    elif error.error_type == ErrorType.WORD:
        print("Word-level error (unknown word)")
    elif error.error_type == ErrorType.GRAMMAR:
        print("Grammar error (syntactic issue)")
    elif error.error_type == ErrorType.CONTEXT_PROBABILITY:
        print("Context error (unlikely word sequence)")

Validation Levels

Control the depth of checking with validation levels at check time:

from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider
from myspellchecker.core.constants import ValidationLevel

provider = SQLiteProvider(database_path="mySpellChecker-default.db")
checker = SpellChecker(provider=provider)

# Fast: syllable-only validation (default, catches most errors)
result = checker.check(text, level=ValidationLevel.SYLLABLE)

# Standard: syllable + word validation
result = checker.check(text, level=ValidationLevel.WORD)

Note: Validation level is specified per-check via the level parameter, not in configuration.

Getting Suggestions

Access correction suggestions for errors:

result = checker.check("နိူင်ငံ")  # Example with error

for error in result.errors:
    print(f"Error: {error.text}")

    # Get top suggestion
    if error.suggestions:
        print(f"Best suggestion: {error.suggestions[0]}")

    # Get all suggestions with scores
    for suggestion in error.suggestions[:5]:
        print(f"  - {suggestion}")

Batch Processing

Process multiple texts efficiently:

texts = [
    "မြန်မာနိုင်ငံ",
    "ကျေးဇူးတင်ပါသည်",
    "နေကောင်းလား"
]

# Check multiple texts
results = checker.check_batch(texts)

for text, result in zip(texts, results):
    print(f"{text}: {len(result.errors)} errors")

Async Processing

For web applications and async workflows:

import asyncio
from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider

provider = SQLiteProvider(database_path="mySpellChecker-default.db")
checker = SpellChecker(provider=provider)

async def check_texts():
    # Single text
    result = await checker.check_async("မြန်မာနိုင်ငံ")
    print(result.has_errors)

    # Batch async
    texts = ["text1", "text2", "text3"]
    results = await checker.check_batch_async(texts)
    return results

# Run async
results = asyncio.run(check_texts())

Using with FastAPI

from fastapi import FastAPI
from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider

app = FastAPI()
provider = SQLiteProvider(database_path="mySpellChecker-default.db")
checker = SpellChecker(provider=provider)

@app.post("/check")
async def check_spelling(text: str):
    result = await checker.check_async(text)
    return {
        "has_errors": result.has_errors,
        "error_count": len(result.errors),
        "errors": [
            {
                "position": e.position,
                "text": e.text,
                "suggestions": e.suggestions[:3]
            }
            for e in result.errors
        ]
    }

Context Manager Usage

Ensure proper resource cleanup:

from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider

# Automatic cleanup
provider = SQLiteProvider(database_path="mySpellChecker-default.db")
with SpellChecker(provider=provider) as checker:
    result = checker.check("မြန်မာနိုင်ငံ")
    print(result.has_errors)
# Resources released here

Custom Database

Use your own dictionary database:

from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider

# Specify database path
provider = SQLiteProvider(database_path="/path/to/my/dictionary.db")
checker = SpellChecker(provider=provider)

Configuration Presets

Use built-in presets for common scenarios:

from myspellchecker import SpellChecker
from myspellchecker.core.config import get_profile

# Fast mode: maximum speed, minimal validation
config = get_profile("fast")

# Production mode: good balance of speed and accuracy
config = get_profile("production")

# Accurate mode: maximum accuracy, all validations
config = get_profile("accurate")

checker = SpellChecker(config=config)

Available profiles: "development", "production", "testing", "fast", "accurate"

CLI Usage

Check text from command line:

# Check a file
myspellchecker check input.txt

# Check with output
myspellchecker check input.txt -o results.json

# Interactive mode
echo "မြန်မာနိုင်ငံ" | myspellchecker check -

# Use specific format
myspellchecker check input.txt --format json
myspellchecker check input.txt --format csv

Colloquial Handling

Control how colloquial (informal) spellings are handled:

from myspellchecker import SpellChecker
from myspellchecker.core.config import SpellCheckerConfig
from myspellchecker.core.config.validation_configs import ValidationConfig

# Lenient mode (default): accept colloquial forms with info note
config = SpellCheckerConfig(
    validation=ValidationConfig(colloquial_strictness="lenient")
)

# Strict mode: flag all colloquial forms as errors
config = SpellCheckerConfig(
    validation=ValidationConfig(colloquial_strictness="strict")
)

checker = SpellChecker(config=config)

Localized Error Messages

Display error messages in Myanmar:

from myspellchecker.core.i18n import set_language

# Switch to Myanmar
set_language("my")

# Now error messages will be in Myanmar
result = checker.check("invalid text")
# Error message: စာလုံးပေါင်း မမှန်ကန်ပါ

Streaming Large Files

Process large files with bounded memory:

from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider
from myspellchecker.core.streaming import StreamingChecker

provider = SQLiteProvider(database_path="mySpellChecker-default.db")
checker = SpellChecker(provider=provider)
streaming = StreamingChecker(checker)

with open("large_file.txt") as f:
    for result in streaming.check_stream(f):
        if result.response.has_errors:
            print(f"Line {result.line_number}: {len(result.response.errors)} errors")

Text Normalization

Always normalize input for consistent results:

from myspellchecker.text.normalize import normalize

# Normalize before checking
text = normalize("မြန်မာ")
result = checker.check(text)

Zawgyi Detection

Handle legacy Zawgyi encoding:

from myspellchecker.text.normalize import is_likely_zawgyi, convert_zawgyi_to_unicode

text = "..."  # User input
is_zawgyi, confidence = is_likely_zawgyi(text)

if is_zawgyi and confidence > 0.8:
    text = convert_zawgyi_to_unicode(text)

result = checker.check(text)

Error Handling

Handle errors gracefully with specific exception types:

from myspellchecker import SpellChecker
from myspellchecker.providers import SQLiteProvider
from myspellchecker.core.exceptions import (
    DataLoadingError,
    ConfigurationError,
    ValidationError,
)

try:
    provider = SQLiteProvider(database_path="mySpellChecker-default.db")
    checker = SpellChecker(provider=provider)
    result = checker.check(text)
except DataLoadingError as e:
    print(f"Database error: {e}")
except ConfigurationError as e:
    print(f"Configuration error: {e}")
except ValidationError as e:
    print(f"Validation error: {e}")

Summary Table

Use Case	Method	Speed
Quick check	`check_text(text)`	Convenient
Single text	`checker.check(text)`	Fast
Multiple texts	`checker.check_batch(texts)`	Faster (batched)
Async single	`await checker.check_async(text)`	Non-blocking
Async batch	`await checker.check_batch_async(texts)`	Non-blocking, batched
Large files	`streaming.check_stream(file)`	Memory-bounded
Fast validation	`checker.check(text, level=ValidationLevel.SYLLABLE)`	Fastest
Full validation	`checker.check(text, level=ValidationLevel.WORD)`	More thorough

Next Steps

Configuration Guide - Customize behavior
API Reference - Complete API documentation
Performance Tuning - Optimize for your use case
Custom Dictionaries - Build domain-specific dictionaries

Getting Started

Dictionary Building

Spell Checking

Grammar

Language Processing

AI-Powered Checking

Text Utilities

Performance & Scale

Customization

Integration & Deployment

Help & FAQ

Setup

Your First Spell Check

Quick Check (One-liner)

Standard Usage

Understanding Results

Error Types

Validation Levels

Getting Suggestions

Batch Processing

Async Processing

Using with FastAPI

Context Manager Usage

Custom Database

Configuration Presets

CLI Usage

Colloquial Handling

Localized Error Messages

Streaming Large Files

Text Normalization

Zawgyi Detection

Error Handling

Summary Table

Next Steps

Getting Started

Dictionary Building

Spell Checking

Grammar

Language Processing

AI-Powered Checking

Text Utilities

Performance & Scale

Customization

Integration & Deployment

Help & FAQ

​Setup

​Your First Spell Check

​Quick Check (One-liner)

​Standard Usage

​Understanding Results

​Error Types

​Validation Levels

​Getting Suggestions

​Batch Processing

​Async Processing

​Using with FastAPI

​Context Manager Usage

​Custom Database

​Configuration Presets

​CLI Usage

​Colloquial Handling

​Localized Error Messages

​Streaming Large Files

​Text Normalization

​Zawgyi Detection

​Error Handling

​Summary Table

​Next Steps

Setup

Your First Spell Check

Quick Check (One-liner)

Standard Usage

Understanding Results

Error Types

Validation Levels

Getting Suggestions

Batch Processing

Async Processing

Using with FastAPI

Context Manager Usage

Custom Database

Configuration Presets

CLI Usage

Colloquial Handling

Localized Error Messages

Streaming Large Files

Text Normalization

Zawgyi Detection

Error Handling

Summary Table

Next Steps