texthumanize 0.24.0

Algorithmic text humanization with AI detection, tone analysis, paraphrasing, and spinning — 20-stage pipeline, 14 languages, zero dependencies

TextHumanize

The most advanced open-source text naturalization engine

Normalize style, improve readability, and ensure brand-safe content — offline, private, and blazing fast

     

42,375 lines of code · 75 Python modules · 17-stage pipeline · 14 languages + universal · 1,802 tests

Quick Start · Features · Documentation · Live Demo · License

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TextHumanize is a pure-algorithmic text processing engine that normalizes style, improves readability, and removes mechanical patterns from text. No neural networks, no API keys, no internet — just 42K+ lines of finely tuned rules, dictionaries, and statistical methods.

Built-in toolkit: AI Detection · Paraphrasing · Tone Analysis · Watermark Cleaning · Content Spinning · Coherence Analysis · Readability Scoring · Stylistic Fingerprinting · Auto-Tuner · Perplexity Analysis · Plagiarism Detection · Async API · SSE Streaming

Platforms: Python (full) · TypeScript/JavaScript (core) · PHP (full)

Languages: 🇷🇺 RU · 🇺🇦 UK · 🇬🇧 EN · 🇩🇪 DE · 🇫🇷 FR · 🇪🇸 ES · 🇵🇱 PL · 🇧🇷 PT · 🇮🇹 IT · 🇸🇦 AR · 🇨🇳 ZH · 🇯🇵 JA · 🇰🇷 KO · 🇹🇷 TR · 🌍 any language via universal processor

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Why TextHumanize?

Problem: Machine-generated text has uniform sentence lengths, bureaucratic vocabulary, formulaic connectors, and low stylistic diversity — reducing readability, engagement, and brand authenticity.

Solution: TextHumanize algorithmically normalizes text style while preserving meaning. Configurable intensity, deterministic output, full change reports. No cloud APIs, no rate limits, no data leaks.

Advantage	Details
30,000+ chars/sec	Process a full article in milliseconds, not seconds
100% private	All processing is local — your text never leaves your machine
Precise control	Intensity 0–100, 9 profiles, keyword preservation, max change ratio
14 languages	Full dictionaries for 14 languages; statistical processor for any other
Zero dependencies	Pure Python stdlib — no pip packages, no model downloads, starts in <100ms
Reproducible	Seed-based PRNG — same input + same seed = identical output
AI detection	13-metric ensemble + 35-feature statistical detector — no ML required
Enterprise-ready	Dual license, 1,802 tests, CI/CD, benchmarks, on-prem deployment

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Comparison with Competitors

Criterion	TextHumanize	Online Humanizers	GPT/LLM Rewriting
Works offline	✅	❌	❌
Privacy	✅ Local only	❌ Third-party servers	❌ Cloud API
Speed	30K+ chars/sec	2–10 sec (network)	~500 chars/sec
Cost per 1M chars	$0	$10–50/month	$15–60 (GPT-4)
API key required	No	Yes	Yes
Deterministic	✅ Seed-based	❌	❌
Languages	14 + universal	1–3	10+ but expensive
Built-in AI detector	✅ 13 metrics	❌ or basic	❌
Max change control	✅ max_change_ratio	❌	❌ Unpredictable
Open source	✅	❌	❌
Self-hosted	✅	❌	❌

vs. Other Open-Source Libraries

Feature	TextHumanize	Typical Alternatives
Pipeline stages	17	2–4
Languages	14 + universal	1–2
AI detection	✅ 13 metrics + statistical ML	❌
Python tests	1,802	10–50
Codebase size	42,375 lines	500–2K
Platforms	Python + JS + PHP	Single
Plugin system	✅	❌
Tone analysis	✅ 7 levels	❌
REST API	✅ 12 endpoints	❌
Readability metrics	✅ 6 indices	0–1
Morphological engine	✅ 4 languages	❌

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Installation

pip install texthumanize

From source:

git clone https://github.com/ksanyok/TextHumanize.git
cd TextHumanize && pip install -e .

PHP / TypeScript

# PHP
cd php/ && composer install

# TypeScript
cd js/ && npm install

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Quick Start

from texthumanize import humanize, analyze, detect_ai, explain

# Humanize text
result = humanize("This text utilizes a comprehensive methodology for implementation.", lang="en")
print(result.text)       # → "This text uses a complete method for setup."
print(result.change_ratio)  # → 0.15
print(result.quality_score) # → 0.85

# With profile and intensity
result = humanize(text, lang="en", profile="web", intensity=70)

# AI Detection — 13-metric ensemble
ai = detect_ai("Text to check for AI generation.", lang="en")
print(f"AI: {ai['score']:.0%} | {ai['verdict']} | Confidence: {ai['confidence']:.0%}")

# Analyze text metrics
report = analyze("Text to analyze.", lang="en")
print(f"Artificiality: {report.artificiality_score:.1f}/100")

# Full change report
print(explain(result))

All Features at a Glance

from texthumanize import (
    humanize, humanize_batch, humanize_chunked, humanize_ai,
    detect_ai, detect_ai_batch, detect_ai_sentences, detect_ai_mixed,
    paraphrase, analyze_tone, adjust_tone,
    detect_watermarks, clean_watermarks,
    spin, spin_variants, analyze_coherence, full_readability,
    AutoTuner, BenchmarkSuite, STYLE_PRESETS,
)

# Paraphrasing
print(paraphrase("The system works efficiently.", lang="en"))

# Tone — 7-level formality scale
tone = analyze_tone("Please submit the documentation.", lang="en")
casual = adjust_tone("It is imperative to proceed.", target="casual", lang="en")

# Watermarks
clean = clean_watermarks("Te\u200bxt wi\u200bth hid\u200bden chars")

# Spinning
variants = spin_variants("Original text.", count=5, lang="en")

# Batch + chunked processing
results = humanize_batch(["Text 1", "Text 2"], lang="en", max_workers=4)
result = humanize_chunked(large_doc, chunk_size=3000, lang="ru")

# Async API — native asyncio support
from texthumanize import async_humanize, async_detect_ai
result = await async_humanize("Text to process", lang="en")
ai = await async_detect_ai("Text to check", lang="en")

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Before & After

Before (AI-generated):

Furthermore, it is important to note that the implementation of cloud computing facilitates the optimization of business processes. Additionally, the utilization of microservices constitutes a significant advancement.

After (TextHumanize, profile="web", intensity=70):

But cloud computing helps optimize how businesses work. Also, microservices are a big step forward.

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Feature Matrix

Category	Feature	Python	JS	PHP
Core	humanize() — 17-stage pipeline	✅	✅	✅
	humanize_batch() — parallel processing	✅	—	✅
	humanize_chunked() — large text support	✅	—	✅
	humanize_ai() — three-tier AI + rules	✅	—	—
	analyze() — artificiality scoring	✅	✅	✅
	explain() — change report	✅	—	✅
AI Detection	detect_ai() — 13-metric + statistical ML	✅	✅	✅
	detect_ai_batch() — batch detection	✅	—	—
	detect_ai_sentences() — per-sentence	✅	—	—
	detect_ai_mixed() — mixed content	✅	—	—
NLP	paraphrase() — syntactic transforms	✅	—	✅
	POSTagger — rule-based POS (EN/RU/UK/DE)	✅	—	—
	CJKSegmenter — zh/ja/ko word segmentation	✅	—	—
	SyntaxRewriter — 8 sentence transforms	✅	—	—
	WordLanguageModel — perplexity (14 langs)	✅	—	—
	CollocEngine — PMI collocation scoring	✅	—	—
Tone	analyze_tone() — formality analysis	✅	—	✅
	adjust_tone() — 7-level adjustment	✅	—	✅
Watermarks	detect_watermarks() — 5 types	✅	—	✅
	clean_watermarks() — removal	✅	—	✅
Spinning	spin() / spin_variants()	✅	—	✅
Analysis	analyze_coherence() — paragraph flow	✅	—	✅
	full_readability() — 6 indices	✅	—	✅
	Stylistic fingerprinting	✅	—	—
Quality	BenchmarkSuite — 6-dimension scoring	✅	—	—
	FingerprintRandomizer — anti-detection	✅	—	—
Advanced	Style presets (5 personas)	✅	—	—
	Auto-Tuner (feedback loop)	✅	—	—
	Plugin system	✅	—	✅
	REST API (12 endpoints)	✅	—	—
	CLI (15+ commands)	✅	—	—
Languages	Full dictionary support	14	2	14
	Universal processor	✅	✅	✅

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Profiles

Profile	Use Case	Sentence Length	Colloquialisms	Default Intensity
chat	Messaging, social media	8–18 words	High	80
web	Blog posts, articles	10–22 words	Medium	60
seo	SEO content (keyword-safe)	12–25 words	None	40
docs	Technical documentation	12–28 words	None	50
formal	Academic, legal	15–30 words	None	30
academic	Research papers	15–30 words	None	25
marketing	Sales, promo copy	8–20 words	Medium	70
social	Social media posts	6–15 words	High	85
email	Business emails	10–22 words	Medium	50

Style presets: student · copywriter · scientist · journalist · blogger

result = humanize(text, profile="seo", intensity=40,
                  constraints={"keep_keywords": ["API", "cloud"]})

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Processing Pipeline

Input → Watermark Cleaning → Segmentation → CJK Segmentation → Typography
      → Debureaucratization → Structure → Repetitions → Liveliness
      → Paraphrasing → Syntax Rewriting → Tone Harmonization → Universal
      → Naturalization → Word LM Quality Gate → Readability → Grammar
      → Coherence Repair → Fingerprint Diversification → Validation → Output

17 stages with adaptive intensity (auto-reduces processing for already-natural text) and graduated retry (retries at lower intensity if change ratio exceeds limit).

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AI Detection

13-metric ensemble + 35-feature statistical detector. No ML models, no APIs.

Metric	What It Measures
AI Patterns	Formulaic phrases ("it is important to note", "furthermore")
Burstiness	Sentence length uniformity (humans vary, AI doesn't)
Opening Diversity	Repetitive sentence starts
Entropy	Word predictability (Shannon entropy)
Vocabulary	Lexical richness (type-to-token ratio)
Perplexity	Character-level predictability
+ 7 more	Stylometry, coherence, grammar perfection, punctuation, rhythm, readability, Zipf

Ensemble: Weighted sum (50%) + Strong signal detector (30%) + Majority voting (20%)

Verdicts: human_written (< 35%) · mixed (35–65%) · ai_generated (≥ 65%)

result = detect_ai("Text to check.", lang="en")
print(f"{result['score']:.0%} — {result['verdict']}")

# Per-sentence detection
for s in detect_ai_sentences(text, lang="en"):
    print(f"{'🤖' if s['label'] == 'ai' else '👤'} {s['text'][:80]}")

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CLI

texthumanize input.txt -l en -p web -i 70 -o output.txt
texthumanize input.txt --detect-ai
texthumanize input.txt --analyze
texthumanize input.txt --paraphrase -o out.txt
texthumanize input.txt --tone casual
texthumanize dummy --api --port 8080
echo "Text" | texthumanize - -l en

REST API

python -m texthumanize.api --port 8080

Method	Endpoint	Description
POST	/humanize	Humanize text
POST	/detect-ai	AI detection (single or batch)
POST	/analyze	Text metrics
POST	/paraphrase	Paraphrase
POST	/tone/analyze	Tone analysis
POST	/tone/adjust	Tone adjustment
POST	/watermarks/detect	Detect watermarks
POST	/watermarks/clean	Clean watermarks
POST	/spin	Text spinning
POST	/coherence	Coherence analysis
POST	/readability	Readability metrics
GET	/health	Health check

curl -X POST http://localhost:8080/humanize \
  -H "Content-Type: application/json" \
  -d '{"text": "Your text here.", "lang": "en", "profile": "web"}'

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Language Support

Language	Code	Bureaucratic	Synonyms	Collocations
Russian	ru	70+	50+	408
Ukrainian	uk	50+	48	38
English	en	40+	35+	1,578
German	de	64	45	125
French	fr	20	20	128
Spanish	es	18	18	126
Polish	pl	18	18	34
Portuguese	pt	16	17	36
Italian	it	16	17	38
Arabic	ar	81	80	—
Chinese	zh	80	80	—
Japanese	ja	60+	60+	—
Korean	ko	60+	60+	—
Turkish	tr	60+	60+	—

Universal processor works for any language using statistical methods (burstiness, perplexity, punctuation normalization).

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Performance

All benchmarks on Apple Silicon (M-series), Python 3.12, single thread.

Function	Text Size	Avg Latency	Per 1K Words	Peak Memory
humanize()	30 words	0.1 ms	~5 ms	4 KB
humanize()	80 words	1.5 ms	~19 ms	4 KB
humanize()	400 words	0.1 ms	< 1 ms	6 KB
detect_ai()	30 words	4.3 ms	—	22 KB
detect_ai()	80 words	36.8 ms	—	71 KB
detect_ai()	400 words	162 ms	—	196 KB
analyze()	80 words	478 ms	—	362 KB
paraphrase()	80 words	0.2 ms	—	8 KB

Property	Value
LRU cache hit	11× faster than cold call
External network calls	0 (offline-first)
Deterministic (same seed)	✅ Always
Pipeline timeout	30 s (configurable)
Rate limiting (API)	10 req/s per IP, burst 20

Run benchmarks yourself: python benchmarks/run_benchmark.py

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Plugin System

from texthumanize import Pipeline, humanize

def add_disclaimer(text: str, lang: str) -> str:
    return text + "\n\n---\nProcessed by TextHumanize."

Pipeline.register_hook(add_disclaimer, after="naturalization")
result = humanize("Your text here.")
Pipeline.clear_plugins()

Available stages: watermark → segmentation → typography → debureaucratization → structure → repetitions → liveliness → universal → naturalization → validation → restore

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Architecture

texthumanize/                    # 75 Python modules, 42,375 lines
├── core.py                      # Facade: humanize(), analyze(), detect_ai()
├── pipeline.py                  # 17-stage pipeline + adaptive intensity
├── api.py                       # REST API server (12 endpoints)
├── cli.py                       # CLI (15+ commands)
├── exceptions.py                # Exception hierarchy
│
├── analyzer.py                  # Artificiality scoring + 6 readability metrics
├── detectors.py                 # AI detector: 13 metrics + ensemble
├── statistical_detector.py      # 35-feature ML classifier
├── pos_tagger.py                # POS tagger (EN/RU/UK/DE)
├── collocation_engine.py        # PMI collocation scoring (2,511 collocations)
├── word_lm.py                   # Word-level LM (14 langs)
│
├── normalizer.py                # Typography (stage 2)
├── decancel.py                  # Debureaucratization (stage 3)
├── structure.py                 # Sentence diversification (stage 4)
├── naturalizer.py               # Burstiness + perplexity (stage 10)
├── paraphraser_ext.py           # Semantic paraphrasing (stage 7)
├── syntax_rewriter.py           # Structural transforms (stage 7b)
├── grammar_fix.py               # Grammar correction (stage 12)
├── coherence_repair.py          # Coherence repair (stage 13)
├── validator.py                 # Quality validation (stage 14)
│
├── tone.py                      # Tone analysis & adjustment
├── watermark.py                 # Watermark detection & cleaning
├── spinner.py                   # Text spinning
├── coherence.py                 # Coherence analysis
├── morphology.py                # Morphological engine (RU/UK/EN/DE)
├── ...                          # 30+ more modules
│
└── lang/                        # 14 language packs + registry
    ├── en.py, ru.py, de.py ...  # Data only, no logic
    └── ar.py, zh.py, ja.py ...  # Including CJK + RTL

Design principles: Modular · Declarative rules · Idempotent · Safe defaults · Extensible · Zero dependencies · Lazy imports

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Testing & Quality

Platform	Tests	Status
Python	1,802	✅ All passing
PHP	223	✅ All passing
TypeScript	28	✅ All passing
Total	2,053	✅

pytest -q                          # 1802 passed
ruff check texthumanize/           # Lint
mypy texthumanize/                 # Type check
cd php && php vendor/bin/phpunit   # 223 tests

CI/CD runs on every push: Python 3.9–3.13 + PHP 8.1–8.3 matrix, ruff, mypy, pytest with coverage ≥70%.

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Security

Aspect	Implementation
Input limits	1 MB text, 5 MB API body
Network calls	Zero. No telemetry, no analytics
Dependencies	Zero. Pure stdlib
Regex safety	All linear-time, no user input compiled to regex
Reproducibility	Seed-based PRNG, deterministic output
Sandboxing	Resource limits documented for production

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Docker

docker build -t texthumanize .
docker run -p 8080:8080 texthumanize

# API mode
docker run -p 8080:8080 texthumanize --api --port 8080

# Process a file
docker run -v $(pwd):/data texthumanize /data/input.txt -o /data/output.txt

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For Business & Enterprise

Requirement	How TextHumanize Delivers
Predictability	Seed-based PRNG — same input + seed = identical output
Privacy	100% local. Zero network calls. No data leaves your server
Auditability	Every call returns change_ratio, quality_score, similarity, explain() report
Integration	Python SDK · JS SDK · PHP SDK · CLI · REST API · Docker
Reliability	2,053 tests across 3 platforms, CI/CD with ruff + mypy
No vendor lock-in	Zero dependencies. No cloud APIs, no API keys, no rate limits
Language coverage	14 full language packs + universal processor for any language

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Contributing

See CONTRIBUTING.md for development setup, testing, and PR guidelines.

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License & Pricing

TextHumanize uses a dual license model:

Use Case	License	Cost
Personal / Academic / Open-source	Free License	Free
Commercial — 1 dev, 1 project	Indie	$199/year
Commercial — up to 5 devs	Startup	$499/year
Commercial — up to 20 devs	Business	$1,499/year
Enterprise / On-prem / SLA	Enterprise	Contact us

All commercial licenses include full source code, updates for 1 year, and email support.

Full licensing details → · See LICENSE for legal text · Contact: [e-mail]

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Documentation · Live Demo · GitHub · Issues · Discussions · Commercial License