pygarble

Detect gibberish, garbled text, and nonsense with high precision.

A zero-dependency Python library for identifying random character sequences, keyboard mashing, encoding errors, and other forms of text corruption. Uses statistical analysis, phonotactic rules, and pattern matching to distinguish meaningful text from gibberish.

Installation

pip install pygarble

Quick Start

from pygarble import GarbleDetector, EnsembleDetector, Strategy

# Recommended: Use the default ensemble (99.2% precision, 85.6% recall)
detector = EnsembleDetector()
detector.predict("Hello world")      # False - valid text
detector.predict("asdfghjkl")        # True - keyboard mashing
detector.predict("qxzjkwp")          # True - impossible letter combinations

# Get probability scores (0.0 = valid, 1.0 = gibberish)
detector.predict_proba("Hello world")  # ~0.1
detector.predict_proba("xkqzjwp")      # ~0.9

# Batch processing
texts = ["Hello world", "asdfghjkl", "Normal sentence here"]
results = detector.predict(texts)      # [False, True, False]

Performance

Tested on 1,644 samples (dictionary words, sentences, random strings, keyboard mashing) via regression/benchmark.py:

Detector	Precision	Recall	F1 Score
EnsembleDetector()	99.2%	85.6%	91.9%
MARKOV_CHAIN	99.2%	84.3%	91.2%
NGRAM_FREQUENCY	97.6%	75.0%	84.8%
LOG_LIKELIHOOD_RATIO	100%	63.4%	77.6%
WORD_ANOMALY	100%	52.8%	69.1%

The default ensemble is the union (voting="any") of MARKOV_CHAIN with two strategies that produced zero false positives on the benchmark (LOG_LIKELIHOOD_RATIO, WORD_ANOMALY), so each member only adds recall. It strictly dominates any single strategy.

Two useful variants:

# Strict precision: majority vote drove false positives to 0 on the
# benchmark (100% precision, 74.9% recall) at the cost of recall
detector = EnsembleDetector(
    strategies=[
        Strategy.MARKOV_CHAIN, Strategy.NGRAM_FREQUENCY, Strategy.WORD_LOOKUP,
        Strategy.LOG_LIKELIHOOD_RATIO, Strategy.WORD_ANOMALY,
    ],
    voting="majority",
)

# Specialist coverage: adds detection of hashes, mojibake, repeated junk,
# and homoglyph attacks that the character-model strategies don't target
detector = EnsembleDetector(
    strategies=[
        Strategy.LOG_LIKELIHOOD_RATIO, Strategy.WORD_ANOMALY,
        Strategy.KEYBOARD_ADJACENCY, Strategy.MOJIBAKE,
        Strategy.HEX_STRING, Strategy.REPETITION, Strategy.UNICODE_SCRIPT,
    ],
    voting="any",
)

Detection Strategies

Recommended Strategies

Strategy	Description	Precision
MARKOV_CHAIN	Character transition probabilities trained on English	99.2%
NGRAM_FREQUENCY	Common English trigram analysis	97.6%
LOG_LIKELIHOOD_RATIO	English-vs-random two-model bigram comparison	100%
WORD_ANOMALY	Per-word scoring; catches one garbage token in a valid sentence	100%
WORD_LOOKUP	Dictionary of 49K English words	high recall

All Available Strategies

Statistical Models (v0.7.0+)

• LOG_LIKELIHOOD_RATIO - Log-likelihood ratio of English vs uniform character models (length-normalized)
• WORD_ANOMALY - Fraction of individually-anomalous words; robust to garbage embedded in valid text
• KEYBOARD_ADJACENCY - Physical key-adjacency walks (catches mash the trigram lists miss)

High Precision (v0.5.0)

• BIGRAM_PROBABILITY - Impossible letter pairs
• LETTER_POSITION - Invalid letter positions
• CONSONANT_SEQUENCE - Too many consecutive consonants
• VOWEL_PATTERN - Invalid vowel sequences
• LETTER_FREQUENCY - Abnormal letter distribution
• RARE_TRIGRAM - Impossible trigrams

Core Strategies

• MARKOV_CHAIN - Character-level Markov chain (best overall)
• NGRAM_FREQUENCY - Trigram frequency analysis
• WORD_LOOKUP - English dictionary lookup
• PRONOUNCEABILITY - English phonotactic rules
• KEYBOARD_PATTERN - Keyboard row sequences
• ENTROPY_BASED - Shannon entropy analysis
• VOWEL_RATIO - Vowel to consonant ratio

Specialized Detectors

• MOJIBAKE - Encoding corruption (UTF-8 as Latin-1)
• UNICODE_SCRIPT - Homoglyph/script mixing attacks
• HEX_STRING - Hash strings and UUIDs
• SYMBOL_RATIO - Excessive symbols/numbers
• REPETITION - Repeated patterns (ababab, repeated words)

Pattern Heuristics

• PATTERN_MATCHING - Configurable regex patterns (keyboard rows, repeated/alternating chars)

Removed in v0.8.0: CHARACTER_FREQUENCY, WORD_LENGTH, STATISTICAL_ANALYSIS, COMPRESSION_RATIO, and ENGLISH_WORD_VALIDATION (the only strategy requiring a third-party dependency). All were dominated by the strategies above; WORD_LOOKUP replaces ENGLISH_WORD_VALIDATION dependency-free.

Using Individual Strategies

from pygarble import GarbleDetector, Strategy

# Markov chain - best overall performance
detector = GarbleDetector(Strategy.MARKOV_CHAIN)
detector.predict("the quick brown fox")  # False
detector.predict("xkqzjwpmv")            # True

# High precision - zero false positives
detector = GarbleDetector(Strategy.BIGRAM_PROBABILITY)
detector.predict("hello world")          # False
detector.predict("qxjjxz")               # True (impossible: qx, jj, xz)

# Encoding corruption detection
detector = GarbleDetector(Strategy.MOJIBAKE)
detector.predict("Café")                 # False - valid UTF-8
detector.predict("Café")                # True - mojibake

# Homoglyph attack detection
detector = GarbleDetector(Strategy.UNICODE_SCRIPT)
detector.predict("paypal")               # False - all Latin
detector.predict("pаypal")               # True - Cyrillic 'а'

Ensemble Detector

Combine multiple strategies for better accuracy:

from pygarble import EnsembleDetector, Strategy

# Default ensemble (recommended)
# Uses: MARKOV_CHAIN, LOG_LIKELIHOOD_RATIO, WORD_ANOMALY
# Voting: "any" - the two companions had zero benchmark false positives,
# so they only add recall on top of MARKOV_CHAIN
detector = EnsembleDetector()

# Custom strategies
detector = EnsembleDetector(
    strategies=[
        Strategy.MARKOV_CHAIN,
        Strategy.BIGRAM_PROBABILITY,
        Strategy.KEYBOARD_PATTERN,
    ]
)

# Different voting modes (default: "any" for the built-in strategy set,
# "majority" when you pass a custom strategies list)
detector = EnsembleDetector(voting="any")       # High recall - flag if ANY strategy detects
detector = EnsembleDetector(voting="all")       # High precision - flag only if ALL agree
detector = EnsembleDetector(voting="majority")  # Balanced
detector = EnsembleDetector(voting="average")   # Average probabilities

# Weighted voting
detector = EnsembleDetector(
    strategies=[Strategy.MARKOV_CHAIN, Strategy.WORD_LOOKUP],
    voting="weighted",
    weights=[0.7, 0.3]
)

API Reference

GarbleDetector

GarbleDetector(
    strategy: Strategy,
    threshold: float = 0.5,    # Probability threshold for predict()
    **kwargs                   # Strategy-specific parameters
)

# Methods
detector.predict(text)         # Returns bool or List[bool]
detector.predict_proba(text)   # Returns float or List[float] (0.0-1.0)

EnsembleDetector

EnsembleDetector(
    strategies: List[Strategy] = None,  # Default: MARKOV_CHAIN + LLR + WORD_ANOMALY
    threshold: float = 0.5,
    voting: str = None,                 # "majority", "any", "all", "average", "weighted"
                                        # default: "any" (built-in set) / "majority" (custom set)
    weights: List[float] = None,        # Required if voting="weighted"
)

# Methods (same as GarbleDetector)
detector.predict(text)
detector.predict_proba(text)

Common Use Cases

Filter User Input

detector = EnsembleDetector()

def validate_input(text):
    if detector.predict(text):
        return "Please enter valid text"
    return None

Clean Data Pipeline

detector = GarbleDetector(Strategy.MARKOV_CHAIN)

clean_data = [text for text in raw_data if not detector.predict(text)]

Detect Encoding Issues

detector = GarbleDetector(Strategy.MOJIBAKE)

for text in documents:
    if detector.predict(text):
        print(f"Encoding issue detected: {text[:50]}...")

Detect Phishing/Homoglyphs

detector = GarbleDetector(Strategy.UNICODE_SCRIPT)

if detector.predict(domain_name):
    print("Warning: Possible homoglyph attack")

Requirements

• Python 3.8+
• Zero dependencies

Development

git clone https://github.com/brightertiger/pygarble.git
cd pygarble
pip install -e ".[dev]"
pytest tests/ -v

License

MIT License

Changelog

0.8.0

• Breaking: removed legacy strategies CHARACTER_FREQUENCY, WORD_LENGTH, STATISTICAL_ANALYSIS, COMPRESSION_RATIO, ENGLISH_WORD_VALIDATION (and the spellchecker extra)

• New default ensemble: MARKOV_CHAIN

  LOG_LIKELIHOOD_RATIO

  WORD_ANOMALY with voting="any" (99.2% precision, 85.6% recall - strictly dominates any single strategy)

• voting now defaults to “any” for the built-in strategy set, “majority” for custom sets

0.7.0

• Fixed ~45 verified bugs across all strategies (false positives on accented text, y-vowel words, proper nouns, Japanese, URLs, formatted text; false negatives on ALL-CAPS gibberish, cp1252 mojibake, repeated words)
• 3 new strategies: LOG_LIKELIHOOD_RATIO, WORD_ANOMALY, KEYBOARD_ADJACENCY
• Ensemble abstention: word-level strategies no longer dilute votes on short text
• Consistent TypeError contract; cleaned 670 junk entries from the word list

0.5.0

• 6 new high-precision strategies (BIGRAM_PROBABILITY, LETTER_POSITION, CONSONANT_SEQUENCE, VOWEL_PATTERN, LETTER_FREQUENCY, RARE_TRIGRAM)
• Redesigned default ensemble for 99.5% precision
• External validation benchmark (1,644 test cases)

0.4.0

• Added COMPRESSION_RATIO, MOJIBAKE, PRONOUNCEABILITY, UNICODE_SCRIPT strategies

0.3.0

• Zero-dependency core with embedded training data
• Added MARKOV_CHAIN, NGRAM_FREQUENCY, WORD_LOOKUP strategies

This site is open source. Improve this page.