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TextHumanize

The most advanced open-source text naturalization engine

Transform AI-generated text into natural, human-like content — with proprietary PHANTOM™, ASH™, and SentenceValidator™ technologies

Reduce AI detection scores by 60–90% · 25 languages · 38-stage adaptive pipeline · 100% offline · Zero dependencies

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235,000+ lines of code · 122 Python modules · 38-stage pipeline · 25 languages + universal · 2,073 tests

3 proprietary technologies: PHANTOM™ (gradient-guided adversarial engine) · ASH™ (adaptive signature humanization) · SentenceValidator™ (interstage quality gate)

Quick Start · Proprietary Technologies · Before & After · Features · Benchmarks · AI Detection · API Reference · Documentation · Live Demo · License

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Table of Contents

• Why TextHumanize?
• Proprietary Technologies
• Installation
• Quick Start
• Before & After Examples
• Feature Matrix
• Comparison with Competitors
• Processing Pipeline
• AI Detection Engine
• API Reference
• Profiles & Presets
• Language Support
• NLP Infrastructure
• SEO Mode
• Readability Metrics
• Paraphrasing Engine
• Tone Analysis & Adjustment
• Watermark Detection & Cleaning
• Content Spinning
• Coherence Analysis
• Morphological Engine
• Stylistic Fingerprinting
• Auto-Tuner
• Plugin System
• Using Individual Modules
• CLI Reference
• REST API Server
• Async API
• Performance & Benchmarks
• Architecture
• TypeScript / JavaScript Port
• PHP Library
• Testing & Quality
• Security & Limits
• For Business & Enterprise
• FAQ & Troubleshooting
• What's New in v0.28.0
• Contributing
• Limitations
• Support the Project
• License & Pricing

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TextHumanize is a pure-algorithmic text processing engine that transforms AI-generated text into natural, human-like content. Three proprietary technologies — PHANTOM™ (gradient-guided adversarial optimization), ASH™ (adaptive signature humanization), and SentenceValidator™ (interstage quality control) — drive a 38-stage pipeline that reduces AI detection scores by 60–90%. No neural networks, no API keys, no internet — just 235K+ lines of finely tuned rules, dictionaries, and statistical methods.

Honest note: TextHumanize is a style-normalization tool, not an AI-detection bypass tool. It reduces AI-like patterns (formulaic connectors, uniform sentence length, bureaucratic vocabulary) but does not guarantee that processed text will pass external AI detectors. Quality of humanization varies by language and text type. See Limitations below.

Built-in toolkit: AI Detection (3 detectors) · Paraphrasing · Tone Analysis · Watermark Cleaning · Content Spinning · Coherence Analysis · Readability Scoring · Stylistic Fingerprinting · Auto-Tuner · Perplexity Analysis · Plagiarism Detection · Grammar Check · Morphology Engine · Neural LM · Async API · SSE Streaming

Platforms: Python (full — 122 modules) · TypeScript/JavaScript (core) · PHP (full)

For business: SaaS integration · REST API with SSE streaming · Docker deployment · Bulk processing · Custom dictionaries · On-prem enterprise · White-label ready

Languages: 🇬🇧 EN · 🇷🇺 RU · 🇺🇦 UK · 🇩🇪 DE · 🇫🇷 FR · 🇪🇸 ES · 🇵🇱 PL · 🇧🇷 PT · 🇮🇹 IT · �🇱 NL · 🇸🇪 SV · 🇨🇿 CS · 🇷🇴 RO · 🇭🇺 HU · 🇩🇰 DA · 🇸🇦 AR · 🇨🇳 ZH · 🇯🇵 JA · 🇰🇷 KO · 🇹🇷 TR · 🇮🇳 HI · 🇻🇳 VI · 🇹🇭 TH · 🇮🇩 ID · 🇮🇱 HE · 🌍 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 | |:-:|:----------|:--------| | 🚀 | Blazing fast | 300–500 ms for a paragraph; full article in 1–2 seconds | | 🔒 | 100% private | All processing is local — your text never leaves your machine | | 🎯 | Precise control | Intensity 0–100, 9 profiles, 5 style presets, keyword preservation, max change ratio | | 🌍 | 25 languages | Deep support for EN/RU/UK/DE; dictionaries for 25 languages; statistical processor for any other | | 📦 | Zero dependencies | Pure Python stdlib — no pip packages, no model downloads, starts in <100 ms | | 🔁 | Reproducible | Seed-based PRNG — same input + same seed = identical output | | 🧠 | 3-layer AI detection | 18-metric heuristic + 35-feature logistic regression + MLP neural detector — no ML framework required | | 🔌 | Plugin system | Register custom hooks at any of 38 pipeline stages | | 📊 | Full analytics | Readability (6 indices), coherence, plagiarism, stylometric fingerprint, content health score | | 🎭 | Tone control | Analyze and adjust formality across 7 levels | | 📚 | 2,944 dictionary entries | EN 1,733 + RU 1,345 + UK 1,042 + DE 874 + FR 718 + ES 749 + more | | 🏢 | Enterprise-ready | Dual license, 2,073+ tests, CI/CD, REST API, Docker, on-prem deployment | | 🛡️ | Secure by design | Input limits, zero network calls, linear-time regex, no eval/exec | | 📝 | Full auditability | Every call returns change_ratio, quality_score, similarity, explain() report |

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� Proprietary Technologies

TextHumanize includes three original, proprietary technologies not found in any other open-source library:

PHANTOM™ — Gradient-Guided Adversarial Humanization Engine

phantom.py — 2,943 lines | The only open-source text humanizer that uses numerical gradient optimization against its own AI detector.

Input Text → ORACLE (gradient analysis) → SURGEON (32 surgical ops) → FORGE (iterative optimization) → Output

• ORACLE computes numerical gradients through the MLP detector via central differences (~70 forward passes, ~1.4ms), producing per-feature contribution analysis and ranked gap reports
• SURGEON executes 32 feature-targeted surgical text operations guided by Oracle gradients — rank-based magnitude scheduling focuses effort on highest-impact features first
• FORGE runs an iterative optimization loop with combined score tracking, stall detection, adaptive budget escalation, text expansion limits, and post-iteration cleanup
• Result: 100% bypass rate on built-in detector (15/15 texts across EN, RU, UK). Processing time: 0.7–1.4s

result = humanize("AI text...", lang="en", phantom=True)  # Enable PHANTOM™
result = humanize_until_human("AI text...", lang="en")     # Auto-iterates with PHANTOM™

ASH™ — Adaptive Signature Humanization

ash_engine.py + signature_transfer.py + perplexity_sculptor.py | Statistically transforms text to match real human writing signatures.

AI Text → Feature Extraction → Human Profile Matching → Signature Transfer → Perplexity Sculpting → Human-like Text

• Human Profiles — statistical fingerprints of real human writing per language (sentence length distribution, vocabulary richness, burstiness patterns, punctuation habits)
• Signature Transfer — morphs AI text's statistical signature toward the target human profile
• Perplexity Sculpting — adjusts word-level perplexity to match human perplexity distribution curves
• Metric Gaps — identifies and systematically closes the gap between AI and human writing on 35+ features

from texthumanize import ASHEngine, ASH_PRESETS
ash = ASHEngine(preset="balanced")
result = ash.humanize("AI text...", lang="en")

SentenceValidator™ — Interst