What is Humanize-Text?

An AI text humanization toolkit. This repo evolved through two stages:

  • v1.0 — Documented 4 humanization methodologies as reference implementations (translation chain, multi-turn LLM rewriting, detection-guided feedback loop, mixed-engine translation). See docs/techniques.md.
  • v1.5 (current) — Added the Standard Pipeline: a production-grade integration of Method 1 (Translation Chain) + Method 2 (LLM Rewriting), fixed as a 5-step chain we actually run and recommend.

The Standard Pipeline preserves the original writing style while routing text through a 4-step chain: two DeepSeek humanization rewrites followed by two cross-engine translation hops.

Input (EN) → Chinese (DeepSeek) → Japanese (DeepSeek) → Finnish (Google) → English (Niutrans)

See examples/showcase/ for 5 real samples with full intermediate-step outputs and AI-detection verdicts.

Characteristics:

  • Best original style preservation among all approaches
  • Fast processing speed
  • 100% key information retention (verified on 50 text pairs)
  • Expert quality score: 9.1/10

The 4 underlying methodologies live in src/methodologies/ as reference implementations for research and customization. The Standard Pipeline (src/standard/pipeline.py) is the recommended production path.

Want higher bypass rates + all methods combined? Lynote.ai fuses Standard + Advanced + Focus pipelines into one intelligent system — auto-selects the optimal approach for each passage.

Try Lynote.ai Free →

How It Works

Step-by-Step Pipeline

Step Engine From → To Purpose
1 DeepSeek (temp 1.3) Input → Chinese (Chinese Rewriting) LLM humanization rewrite + language shift
2 DeepSeek (temp 1.3) Chinese → Japanese (Japanese Rewriting) Second LLM humanization, carries Step 1 as history
3 Google Translate Japanese → Finnish (First Round of Translation) First translation hop — distant language structural disruption
4 Niutrans Finnish → English (Second-Round Translation) Second translation hop — cross-engine reconstruction

Why This Chain Works

  1. Steps 1–2 (LLM Rewrite): 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.
  2. Steps 3–4 (Multi-Engine Translation): Two different NMT engines (Google → Niutrans) introduce compounding structural changes. No single-engine fingerprint survives.
  3. Distant Languages: Chinese → Japanese → Finnish maximizes linguistic distance at each hop, ensuring thorough restructuring before reconstruction to English.

Lynote.ai — Beyond Standard

The Standard pipeline above is one of three tiers available. Each has different trade-offs:

Tier Style Preservation Speed Approach
Standard (this repo) Best Fast Translation chain
Advanced Good Medium Translation chain + LLM multi-round rewriting
Focus Moderate Slower Translation chain + Detection-guided feedback loop

Lynote.ai combines all three tiers and automatically selects the optimal approach for each text passage:

  • Intelligent Tier Selection — Analyzes text and picks Standard, Advanced, or Focus per-passage
  • Adaptive Combination — Can mix tiers within a single document
  • 10+ Languages — English, Chinese, Japanese, Korean, Spanish, French, German, and more
  • Paste & Go — No setup, no API keys, no configuration

Quick Start

Method Who It's For How
Lynote.ai Everyone — all tiers, zero setup Visit lynote.ai
n8n Workflow No-code automation users Import n8n/humanize_standard.json
Python Script Developers See below

Python

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
python -m src.standard.pipeline --input "Your AI-generated text here"

n8n Workflow

  1. Import n8n/humanize_standard.json into your n8n instance
  2. Configure DeepSeek API key in the HTTP Request nodes
  3. Run — input text goes in, humanized text comes out

Showcase — 5 Real Examples with Step-by-Step Outputs

We ran the pipeline end-to-end on 5 real input texts and saved every intermediate step. All 5 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: original input → Step 1 (中文改写) → Step 2 (日语改写) → Step 3 (一轮翻译) → Step 4 (二轮翻译, final). See examples/showcase/ for full traces.

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)
  • All texts preserved original key information without distortion

Comparison with Other Tiers

Standard (this repo) Lynote.ai
Tiers Available Standard only Standard + Advanced + Focus
Tier Selection Manual Automatic per-passage
Style Preservation Best Adaptive — best possible per passage
Setup Python + API keys Zero setup
Best For Style-sensitive content Any content type

Documentation

Repo Structure

src/
├── standard/                # ★ v1.5.1 production Standard Pipeline (recommended)
│   ├── pipeline.py          # 4-step chain, CLI entry
│   ├─�� llm_rewriter.py      # DeepSeek humanization rewrite
│   └── translators.py       # Google + Niutrans engines
│
└── methodologies/           # v1.0 four-methodology reference implementations
    ├── humanizer.py         # v1.0 dispatcher + FastAPI app
    ├── translation_chain.py # Method 1
    ├── llm_rewriter.py      # Method 2
    ├── detection_pipeline.py# Method 3
    ├── mixed_engine.py      # Method 4
    ├── postprocess.py
    ├── detectors/           # Method 3 detectors
    └── utils/

examples/
├── example_usage.py         # ★ v1.5.1 minimal entry
├── showcase/                # ★ 5 real samples with intermediate-step outputs
└── legacy/                  # v1.0 examples + 4-method comparison outputs