Webbläsartillägg för Firefox
  • Tillägg
  • Teman
    • för Firefox
    • Ordlistor & språkpaket
    • Andra webbläsarplatser
    • Tillägg för Android
Logga in
Förhandsvisning av Local Manga Translator

Local Manga Translator av Camekan

Local Manga Translator allows you to read raw Manga (Japanese), Manhwa (Korean), and Manhua (Chinese) by capturing text from your browser and translating it using a powerful AI server running locally on your computer.

Vissa funktioner kan kräva betalningVissa funktioner kan kräva betalning
0 (0 recensioner)0 (0 recensioner)
3 användare3 användare
Hämta Firefox och få tillägget
Hämta fil

Metadata för tillägg

Om detta tillägg
🦊 Firefox Add-on Description & Instructions

⚠️ IMPORTANT: REQUIRES COMPANION SCRIPT
This add-on is a "connector" tool. To perform the translation, you must run the free companion Python script (manga_server.py) on your computer.

✨ Key Features
  • Universal Hardware Support: Works on NVIDIA (CUDA), AMD/Intel (Vulkan), or CPU.
  • Specialized Japanese OCR: Uses Manga-OCR to read vertical, handwritten, and messy manga text perfectly.
  • Advanced Bubble Detection: Now uses Comic-Text-Detector (specialized for Manga/Manhwa) to accurately split connected bubbles and ignore background noise.
  • Smart Korean Mode: Uses PaddleOCR for high-accuracy recognition of Korean webtoons.
  • Natural AI Translation: Connects to local LLMs (like Qwen, Llama 3) for human-quality translation.
  • 100% Private & Free: No API keys, no monthly fees. Everything runs offline.



🛠️ Step-by-Step Installation Guide

Step 1: Install Visual Studio Build Tools (Windows Only)
  1. Download Visual Studio Build Tools 2022 from the Microsoft website.
  2. Run the installer.
  3. CRITICAL: Select the workload named "Desktop development with C++".
  4. Ensure the checklist on the right includes "Windows 10/11 SDK" and "MSVC... C++ x64/x86 build tools".
  5. Click Install and wait for it to finish.

Step 2: Install Python
  1. Download Python 3.10.11 from python.org.
  2. CRITICAL: During installation, check the box "Add Python to PATH".

Step 3: Download & Setup the Server Files

1. Get the Main Script
  • Download manga_server.py from thehttps://github.com/Camekan/Manga_server.py/blob/main/manga_server.py and place it in a new folder (e.g., C:\MangaTranslator).

2. Get the Comic Detector (Critical Step)
  • Download this ZIP file: https://github.com/dmMaze/comic-text-detector/archive/refs/heads/master.zip
  • Extract the ZIP. You will see a folder named comic-text-detector-master.
  • RENAME that folder to: comic_text_detector
  • ⚠️ Important: Use underscores _, not dashes -.
  • MOVE this folder next to manga_server.py.

3. Get the Detector Model (Optional - Auto-downloads on first run)
  • The script will try to download this automatically. If it fails, do this:
  • Download the model file: https://github.com/zyddnys/manga-image-translator/releases/download/beta-0.2.1/comictextdetector.pt
  • Create a new folder named models inside your comic_text_detector folder if it isn`t there..
  • Place the .pt file there.
  • Correct Path: C:\MangaTranslator\comic_text_detector\models\comictextdetector.pt

Your folder must look exactly like this:

MangaTranslator/
├── manga_server.py
└── comic_text_detector/ <-- The folder you renamed
├── inference.py <-- File inside
├── basemodel.py <-- File inside
└── models/ <-- Folder inside
└── comictextdetector.pt

Step 4: Install Dependencies

Open Command Prompt (cmd) inside the folder you extracted and run these commands:

1. Install Basic Tools:

pip install flask manga-ocr pytesseract pillow opencv-python numpy requests

2. Install Korean OCR (PaddleOCR):

pip install paddlepaddle paddleocr protobuf==3.20.3

3. Install AI Engine (Choose Your Hardware):
  • Option A: NVIDIA Users (Best Performance)

pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124
  • Option B: AMD / Intel Users (Vulkan Mode)
  • Download and install the Vulkan SDK.
  • Run this command:

set CMAKE_ARGS="-DGGML_VULKAN=on" && pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir
  • Option C: CPU Only (Compatible but Slower)

pip install llama-cpp-python

Step 5: Get an AI Model
  1. Download a .gguf model (Recommended: Qwen2.5-14B-Instruct-Q4_K_M.gguf) from HuggingFace.
  2. Open manga_server.py with a text editor (like Notepad) and set the MODEL_PATH to point to your downloaded file.

Step 6: Run & Read!
  1. Double-click manga_server.py to start the server.
  2. First run note: It will automatically download the detection model (~100MB). Wait for it to finish.
  3. Open a manga page in Firefox.
  4. Press Alt+Q (or your custom hotkey) and draw a box over the text!

🔗 Download the Script Here: https://github.com/Camekan/Manga_server.py/blob/main/manga_server.py
Bug Reports: Please report issues on the GitHub Issues page.
Betyg 0 av 0 recensenter
Logga in för att betygsätta detta tillägg
Det finns inga betyg än

Stjärnklassificering sparad

5
0
4
0
3
0
2
0
1
0
Inga recensioner ännu
Behörigheter och data

Nödvändiga behörigheter:

  • Åtkomst till dina data för alla webbplatser

Valfria behörigheter:

  • Åtkomst till dina data för 127.0.0.1:5000
  • Åtkomst till dina data för 127.0.0.1
Läs mer
Mer information
Länkar för tillägg
  • Webbplats för support
Version
2.8
Storlek
16,04 kB
Senast uppdaterad
för 2 månader sedan (21 jan 2026)
Relaterade kategorier
  • Språkstöd
  • Foton, musik & videor
Licens
MIT-licens
Sekretesspolicy
Läs sekretesspolicyn för detta tillägg
Versionshistorik
  • Se alla versioner
Lägg till i samling
Rapportera detta tillägg
Gå till Mozillas hemsida

Tillägg

  • Om
  • Firefox tilläggsblogg
  • Verkstad för tillägg
  • Utvecklarcenter
  • Utvecklarpolicyer
  • Community-blogg
  • Forum
  • Rapportera en bugg
  • Recensionsriktlinjer

Webbläsare

  • Desktop
  • Mobile
  • Enterprise

Produkter

  • Browsers
  • VPN
  • Relay
  • Monitor
  • Pocket
  • Bluesky (@firefox.com)
  • Instagram (Firefox)
  • YouTube (firefoxchannel)
  • Sekretess
  • Kakor
  • Juridisk information

Om inget annat anges, är innehållet på denna webbplats licensierat under licensen Creative Commons Attribution Share-Alike License v3.0 eller senare version.