Humanize-Text: Open-Source AI Text Humanizer That Bypasses GPTZero and Turnitin
Explore Humanize-Text, a free open-source toolkit that rewrites AI-generated content into undetectable, human-like writing using a multi-step translation chain and LLM rewriting.
What is Humanize-Text?
Humanize-Text is an open-source Python toolkit designed to convert AI-generated text into natural, human-like writing that bypasses major AI detectors like Turnitin, GPTZero, and others. Developed by Lynote AI, this project offers a production-grade pipeline that combines LLM rewriting with multi-engine translation to break AI statistical fingerprints while preserving original meaning and style.
The repository has evolved through two major versions:
- v1.0 โ Documented four humanization methodologies as reference implementations: translation chain, multi-turn LLM rewriting, detection-guided feedback loop, and mixed-engine translation.
- v1.5 (current) โ Introduced the Standard Pipeline, a production-grade integration of Method 1 (Translation Chain) and Method 2 (LLM Rewriting), fixed as a 5-step chain recommended for real-world use.
How the Standard Pipeline Works
The Standard Pipeline routes text through a 4-step chain:
- DeepSeek (temp 1.3) โ Input โ Chinese (Chinese Rewriting)
- DeepSeek (temp 1.3) โ Chinese โ Japanese (Japanese Rewriting)
- Google Translate โ Japanese โ Finnish (First Translation Hop)
- Niutrans โ Finnish โ English (Second Translation Hop)
This chain leverages two key strategies:
- LLM Rewrite (Steps 1โ2): DeepSeek at temperature 1.3 rewrites while translating, breaking AI statistical fingerprints with creative variation. Step 2 carries Step 1 as conversation history for coherent humanization.
- Multi-Engine Translation (Steps 3โ4): Two different NMT engines (Google โ Niutrans) introduce compounding structural changes. No single-engine fingerprint survives.
- Distant Languages: Chinese โ Japanese โ Finnish maximizes linguistic distance at each hop, ensuring thorough restructuring before reconstruction to English.
Quality Metrics
Tested on 50 text pairs with expert evaluation:
| Dimension | Score (out of 10) |
|---|---|
| Information Completeness | 10.0 |
| Language Fluency | 9.0 |
| Style Adaptability | 8.8 |
| Readability | 9.2 |
| Creativity & Impact | 8.5 |
| Overall | 9.1 |
Key Information Retention: 100% (50/50 pairs) โ all texts preserved original key information without distortion.
Showcase Results
The pipeline was tested on 5 real input texts, and all final outputs were classified as human by the AI detector:
| # | Topic | Detection | Confidence |
|---|---|---|---|
| 01 | Quantum Computing | human | 0.9997 |
| 02 | Quantum Readiness Strategy | human | 0.9982 |
| 03 | Sustainable Supply Chains | human | 0.7810 |
| 04 | Financial Literacy | human | 0.9924 |
| 05 | Peer Review in Science | human | 0.7218 |
Each example shows the full trace: original input โ Step 1 (ไธญๆๆนๅ) โ Step 2 (ๆฅ่ฏญๆนๅ) โ Step 3 (ไธ่ฝฎ็ฟป่ฏ) โ Step 4 (ไบ่ฝฎ็ฟป่ฏ, final). See examples/showcase/ for full traces.
Quick Start
Python Script
git clone https://github.com/lynote-ai/humanize-text.git
cd humanize-text
pip install -r requirements.txt
cp config/config.example.toml config/config.toml
# Fill in your API keys in config.toml
python -m src.standard.pipeline --input "Your AI-generated text here"
n8n Workflow
Import n8n/humanize_standard.json into your n8n instance, configure the DeepSeek API key in the HTTP Request nodes, and run.
Lynote.ai (No Setup)
Visit lynote.ai for a zero-setup experience that combines all three tiers (Standard, Advanced, Focus) and automatically selects the optimal approach for each text passage.
Comparison with Other Tiers
| Feature | Standard (this repo) | Lynote.ai |
|---|---|---|
| Tiers Available | Standard only | Standard + Advanced + Focus |
| Tier Selection | Manual | Automatic per-passage |
| Style Preservation | Best | Adaptive โ best possible per passage |
| Setup | Python + API keys | Zero setup |
| Best For | Style-sensitive content | Any content type |
Repository Structure
src/
โโโ standard/ # โ
v1.5.1 production Standard Pipeline (recommended)
โ โโโ pipeline.py # 4-step chain, CLI entry
โ โโโ llm_rewriter.py # DeepSeek humanization rewrite
โ โโโ translators.py # Google + Niutrans engines
โ
โโโ methodologies/ # v1.0 four-methodology reference implementations
โโโ humanizer.py # v1.0 dispatcher + FastAPI app
โโโ translation_chain.py # Method 1
โโโ llm_rewriter.py # Method 2
โโโ detection_pipeline.py # Method 3
โโโ mixed_engine.py # Method 4
โโโ postprocess.py
โโโ detectors/ # Method 3 detectors
โโโ utils/
examples/
โโโ example_usage.py # โ
v1.5.1 minimal entry
โโโ showcase/ # โ
5 real samples with intermediate-step outputs
โโโ legacy/ # v1.0 examples + 4-method comparison outputs
Why This Matters
As AI-generated content becomes ubiquitous, the ability to humanize text is crucial for maintaining authenticity and avoiding detection in academic, professional, and creative contexts. Humanize-Text provides a transparent, customizable, and effective solution that developers can integrate into their workflows or use as a reference for building their own humanization pipelines.
License
MIT License. See LICENSE for details.