Lynote Humanize Text – Open-source AI text humanization toolkit

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English | 中文

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.

v1.5.1 — Standard Pipeline (Recommended)

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

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.

Steps 3–4 (Multi-Engine Translation): Two different NMT engines (Google → Niutrans) introduce compounding structural changes. No single-engine fingerprint survives.

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

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 DeepSeek API key in the HTTP Request nodes

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

Standard Pipeline Technical Details — v1.5 production pipeline

4 Methodologies Reference — v1.0 underlying methods

Configuration Guide

n8n Workflow Guide

Lynote.ai vs Open Source Comparison

FAQ

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

License

MIT License. See LICENSE for details.

Links

Lynote.ai — AI Humanization Platform

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About

Free open-source AI text humanizer to convert AI-generated content into undetectable, human-like writing. Bypass Turnitin, GPTZero, and all major AI detectors. No sign-up required. Try our unlimited free online tool

lynote.ai/ai-humanizer

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humanize-text

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