參考鏈接如下：：

參考鏈接：https://github.com/datalab-to/marker?tab=readme-ov-file#llm-services

底層的OCR模型：https://github.com/datalab-to/surya

作用：開源免費🆓，多 GPU 推理、生成效果不錯，可結合 llm 進行優化，后續可以多試試，對比對比其他工具，目前工作中夠用就行，暫時還沒必要自己訓練。

1、測試和使用非常的簡單：只需要 2 行：

1.1 安裝：

pip install marker-pdf

1.2 使用: 全部默認配置的話是這樣的

marker_single ./2506.11763v1.pdf 

建議：

1、查看文檔配置相關參數，我這里因為是 32 頁文檔，沒有處理完成，處理了25 頁（1.4w字（word 統計）?用時152s ，每秒接近 92 個字，使用的是 4090 GPU 機器。）；

2、對比看看表格情況怎樣？

原始 PDF: 感覺差不多夠用了，不夠后面可以再加 LLM 增強

3、還可以多 GPU 執行，大家可以多多探索下

官方 -> Usage

First, some configuration:

• Your torch device will be automatically detected, but you can override this. For example,?TORCH_DEVICE=cuda.
• Some PDFs, even digital ones, have bad text in them. Set?-force_ocr?to force OCR on all lines, or the?strip_existing_ocr?to keep all digital text, and strip out any existing OCR text.
• If you care about inline math, set?force_ocr?to convert inline math to LaTeX.

Interactive App

I've included a streamlit app that lets you interactively try marker with some basic options. Run it with:

pip install streamlit streamlit-ace
marker_gui

Convert a single file

marker_single /path/to/file.pdf

You can pass in PDFs or images.

Options:

• -page_range TEXT: Specify which pages to process. Accepts comma-separated page numbers and ranges. Example:?-page_range "0,5-10,20"?will process pages 0, 5 through 10, and page 20.
• -output_format [markdown|json|html|chunks]: Specify the format for the output results.
• -output_dir PATH: Directory where output files will be saved. Defaults to the value specified in settings.OUTPUT_DIR.
• -paginate_output: Paginates the output, using?\\n\\n{PAGE_NUMBER}?followed by??* 48, then?\\n\\n
• -use_llm: Uses an LLM to improve accuracy. You will need to configure the LLM backend - see?below.
• -force_ocr: Force OCR processing on the entire document, even for pages that might contain extractable text. This will also format inline math properly.
• -block_correction_prompt: if LLM mode is active, an optional prompt that will be used to correct the output of marker. This is useful for custom formatting or logic that you want to apply to the output.
• -strip_existing_ocr: Remove all existing OCR text in the document and re-OCR with surya.
• -redo_inline_math: If you want the absolute highest quality inline math conversion, use this along with?-use_llm.
• -disable_image_extraction: Don't extract images from the PDF. If you also specify?-use_llm, then images will be replaced with a description.
• -debug: Enable debug mode for additional logging and diagnostic information.
• -processors TEXT: Override the default processors by providing their full module paths, separated by commas. Example:?-processors "module1.processor1,module2.processor2"
• -config_json PATH: Path to a JSON configuration file containing additional settings.
• config --help: List all available builders, processors, and converters, and their associated configuration. These values can be used to build a JSON configuration file for additional tweaking of marker defaults.
• -converter_cls: One of?marker.converters.pdf.PdfConverter?(default) or?marker.converters.table.TableConverter. The?PdfConverter?will convert the whole PDF, the?TableConverter?will only extract and convert tables.
• -llm_service: Which llm service to use if?-use_llm?is passed. This defaults to?marker.services.gemini.GoogleGeminiService.
• -help: see all of the flags that can be passed into marker. (it supports many more options then are listed above)

The list of supported languages for surya OCR is?here. If you don't need OCR, marker can work with any language.

Convert multiple files

marker /path/to/input/folder

• marker?supports all the same options from?marker_single?above.
• -workers?is the number of conversion workers to run simultaneously. This is automatically set by default, but you can increase it to increase throughput, at the cost of more CPU/GPU usage. Marker will use 5GB of VRAM per worker at the peak, and 3.5GB average.

Convert multiple files on multiple GPUs

NUM_DEVICES=4 NUM_WORKERS=15 marker_chunk_convert ../pdf_in ../md_out

• NUM_DEVICES?is the number of GPUs to use. Should be?2?or greater.
• NUM_WORKERS?is the number of parallel processes to run on each GPU.

Use from python

See the?PdfConverter?class at?marker/converters/pdf.py?function for additional arguments that can be passed.

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.output import text_from_renderedconverter = PdfConverter(artifact_dict=create_model_dict(),
)
rendered = converter("FILEPATH")
text, _, images = text_from_rendered(rendered)

rendered?will be a pydantic basemodel with different properties depending on the output type requested. With markdown output (default), you'll have the properties?markdown,?metadata, and?images. For json output, you'll have?children,?block_type, and?metadata.