This repository is inspired by ppisljar/pdf-translator and adds the following features:

• [GUI] Add support for download-save-translation process on the server (better for mobile devices)
• Support of Ollama and QWEN for translation (by api)
• Support multi threading for translation
• Make a better support of inplace translation of different languages (e.g. support Chinese translation)
• Support batch translation of PDF files without api calls (provides an example later)
• Use single process for ocr / layout model to save vram
• Use LLM for reference checking (fix the bug of supplemental translation)

• translate PDF files while preserving layout

• translation engines:

  • ollama (just added, works fine for translation)
  • openAI (best)
  • QWEN
  • google translate

• layout recognition engines:

  • UniLM DiT

• OCR engines:

  • PaddleOCR

• Render engine:

  • ReportLab

1. Clone this repository

   git clone https://github.com/poppanda/LLM_PDF_Translator.git
   cd LLM_PDF_Translator

1. prerequesites:

• Basically ffmpeg, propper-utils and the font you want to use
• Check if you wants to use uv or venv for individual installation

2. Install the dependencies

• (Recommand)If you are using uv, run the following command

uv sync
uv pip install --no-build-isolation git+https://github.com/facebookresearch/detectron2.git

• Otherwise, run the following command

pip install --no-build-isolation -r requirements.txt

1. Build the docker image via Makefile

make docker-build

make get_models

1. Edit config.yaml

• The type could be ollama, openai, qwen, or google.
• The api_key is the corresponding API key.
• The model is the specific model name you want to use.

2. Check the font you want to use in the 'render' part of config.yaml For a basic demo, you can download the font by:

make install-cn-font

If you are using uv:

uv run server.py

Otherwise

python3 server.py

1. Run the docker container via Makefile

make docker-run

Access to GUI via browser.

http://localhost:8765

• NVIDIA GPU (currently only support NVIDIA GPU)
• Docker

This repository does not allow commercial use.

This repository is licensed under CC BY-NC 4.0. See LICENSE for more information.

• The scene is that if you run a LLM model locally (e.g. ollama), the ocr/layout model will be loaded for nothing while translation, which is a waste of vram (for about 5GB).
• This problem is fixed by
  • Seperate the ocr / layout process from the translation process.
  • Use a single process for ocr/layout model.
  • Kill the process before translation.

• The original code checks the reference by recognizing the 'reference' keyword in the title.
• The problem is that:
  • There may be some supplemental material after the reference, and by the original code, the supplemental material will be translated as well.
  • The 'reference' keyword may not be recognized in some cases.
• The problem is fixed by:
  • Use LLM to check the reference and skip the translation.

• Support M1 Mac or CPU
• switch to VGT for layout detection
• add font detection (family/style/color/size/alignment)
• add support for translating lists
• add support for translating tables
• add support for translating text within images

# batch.py
import warnings
warnings.filterwarnings("ignore")
import server
import os
from pathlib import Path
import tempfile
from loguru import logger

pdf_dir = "" # path to the directory with pdf files

if __name__ == "__main__":
    translator = server.TranslateApi()
    files = list(os.scandir(pdf_dir))
    for file in files:
        if file.is_dir():
            files.extend(list(os.scandir(file.path)))
        elif file.is_file() and file.name.endswith(".pdf"):
            if file.name.endswith("_translated.pdf") or os.path.exists(file.path.replace(".pdf", "_translated.pdf")):
                logger.info(f"Skip {file.path}")
                continue
            logger.info(f"Translating {file.path}")
            response = translator._translate_pdf(
               file.path, 
               translator.temp_dir_name, 
               "English", 
               "Chinese", 
               translate_all=True, 
               p_from=0, 
               p_to=0, 
               side_by_side=True, 
               output_file_path=file.path.replace(".pdf", "_translated.pdf"))

• For PDF layout analysis, using DiT.

• For PDF to text conversion, using PaddlePaddle model.