PDF轉Markdown的開源工具解析

Marker：PDF轉Markdown的開源工具解析
Marker是一個由VikParuchuri在GitHub上開發的開源項目，其核心功能是將PDF文件轉換為Markdown格式。以下是對Marker項目的詳細解析：

項目概述：

項目鏈接：https://github.com/VikParuchuri/marker.git
維護者：VikParuchuri
主要功能：將PDF快速且準確地轉換為Markdown格式，支持多種文檔類型，特別是書籍和科學論文。

技術特點：

深度學習模型：Marker采用了一系列深度學習模型來提取文本、檢測頁面布局、清理和格式化文本塊，并最終組合成Markdown文檔。
OCR支持：對于需要OCR的場景，Marker支持使用Surya和Tesseract等OCR工具，確保文本提取的準確性。
多平臺支持：Marker可以在GPU、CPU或MPS上運行，滿足不同硬件環境的需求。

功能細節：

文檔處理：支持去除頁眉、頁腳和其他雜質，格式化表格和代碼塊，提取并保存圖像。
語言支持：Marker支持所有語言，用戶可以通過指定語言列表來優化OCR效果。
方程轉換：能夠將大多數方程轉換為LaTeX格式，便于在Markdown文檔中嵌入數學公式。

性能表現：

速度與準確性：Marker在速度和準確性方面表現出色，特別是與nougat等其他工具相比，具有顯著優勢。
資源占用：在A6000 Ada上運行時，每個任務平均占用約4GB的VRAM，支持并行處理多個文檔。

使用指南：

安裝：用戶需要通過pip安裝marker-pdf包

pip install marker-pdf

(GraphRAG) PS D:\python-workspace\GraphRAG> pip install marker-pdf 
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Collecting marker-pdfDownloading https://mirrors.aliyun.com/pypi/packages/05/c1/782f56407ea60bd35c127c829b8e43da99a0da41f6c9ee002cab97e430c5/marker_pdf-0.2.15-py3-none-any.whl (63 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 63.2/63.2 kB 563.9 kB/s eta 0:00:00
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Installing collected packages: wcwidth, tbb, mpmath, intel-openmp, tabulate, sympy, safetensors, rapidfuzz, pypdfium2
, opencv-python, mkl, MarkupSafe, ftfy, filelock, jinja2, huggingface-hub, torch, tokenizers, pydantic-settings, transformers, pdftext, texify, surya-ocr, marker-pdf
Successfully installed MarkupSafe-2.1.5 filelock-3.15.4 ftfy-6.2.0 huggingface-hub-0.23.4 intel-openmp-2021.4.0 jinja2-3.1.4 marker-pdf-0.2.15 mkl-2021.4.0 mpmath-1.3.0 opencv-python-4.10.0.84 pdftext-0.3.10 pydantic-settings-2.3.4 pypdfium2-4.30.0 rapidfuzz-3.9.4 safetensors-0.4.3 surya-ocr-0.4.14 sympy-1.12.1 tabulate-0.9.0 tbb-2021.13.0 texify-0.1.10 tokenizers-0.19.1 torch-2.3.1 transformers-4.42.3 wcwidth-0.2.13使用示例：```bash
(GraphRAG) PS D:\python-workspace\GraphRAG> marker_single GPT.pdf ./folder --batch_multiplier 2 --max_pages 52 --langs English
config.json: 100%|█████████████████████████████████████████████████████████████████████| 1.18k/1.18k [00:00<?, ?B/s] 
model.safetensors: 100%|█████████████████████████████████████████████████████████| 120M/120M [00:07<00:00, 16.7MB/s] 
Loaded detection model vikp/surya_det2 on device cpu with dtype torch.float32
preprocessor_config.json: 100%|████████████████████████████████████████████████████████████| 430/430 [00:00<?, ?B/s] 
config.json: 100%|█████████████████████████████████████████████████████████████████████| 1.57k/1.57k [00:00<?, ?B/s] 
model.safetensors: 100%|█████████████████████████████████████████████████████████| 120M/120M [00:06<00:00, 18.0MB/s] 
Loaded detection model vikp/surya_layout2 on device cpu with dtype torch.float32
preprocessor_config.json: 100%|████████████████████████████████████████████████████████████| 430/430 [00:00<?, ?B/s] 
config.json: 100%|█████████████████████████████████████████████████████████████████████| 5.04k/5.04k [00:00<?, ?B/s] 
model.safetensors: 100%|█████████████████████████████████████████████████████████| 550M/550M [00:34<00:00, 16.2MB/s] 
generation_config.json: 100%|██████████████████████████████████████████████████████████████| 160/160 [00:00<?, ?B/s] 
Loaded reading order model vikp/surya_order on device cpu with dtype torch.float32
preprocessor_config.json: 100%|████████████████████████████████████████████████████████████| 684/684 [00:00<?, ?B/s] 
config.json: 100%|█████████████████████████████████████████████████████████████| 6.91k/6.91k [00:00<00:00, 6.82MB/s] 
model.safetensors: 100%|███████████████████████████████████████████████████████| 1.05G/1.05G [01:04<00:00, 16.2MB/s] 
generation_config.json: 100%|██████████████████████████████████████████████████████████████| 181/181 [00:00<?, ?B/s]
Loaded recognition model vikp/surya_rec on device cpu with dtype torch.float32
preprocessor_config.json: 100%|█████████████████████████████████████████████████████| 608/608 [00:00<00:00, 605kB/s]
config.json: 100%|█████████████████████████████████████████████████████████████████████| 4.92k/4.92k [00:00<?, ?B/s]
model.safetensors: 100%|█████████████████████████████████████████████████████████| 625M/625M [00:38<00:00, 16.4MB/s]
generation_config.json: 100%|██████████████████████████████████████████████████████████████| 191/191 [00:00<?, ?B/s]
Loaded texify model to cpu with torch.float32 dtype
preprocessor_config.json: 100%|████████████████████████████████████████████████████████████| 617/617 [00:00<?, ?B/s]
tokenizer_config.json: 100%|███████████████████████████████████████████████████████████| 4.49k/4.49k [00:00<?, ?B/s]
tokenizer.json: 100%|██████████████████████████████████████████████████████████| 2.14M/2.14M [00:00<00:00, 2.85MB/s]
added_tokens.json: 100%|███████████████████████████████████████████████████████████████| 18.3k/18.3k [00:00<?, ?B/s]
special_tokens_map.json: 100%|█████████████████████████████████████████████████████| 552/552 [00:00<00:00, 6.29MB/s] 
Detecting bboxes: 100%|███████████████████████████████████████████████████████████████| 7/7 [05:49<00:00, 49.99s/it] 
Recognizing Text: 100%|███████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.37s/it] 
Detecting bboxes: 100%|███████████████████████████████████████████████████████████████| 5/5 [05:32<00:00, 66.45s/it] 
Finding reading order: 100%|██████████████████████████████████████████████████████████| 5/5 [03:15<00:00, 39.04s/it] 
Saved markdown to the ./folder\GPT folder


配置：用戶可以通過環境變量或配置文件調整Marker的行為，如設置OCR引擎、指定GPU設備、配置內存使用等。
命令行工具：Marker提供了命令行工具，允許用戶以批處理方式轉換單個或多個PDF文件。商業使用與許可：商業限制：雖然研究和個人使用是免費的，但商業使用受到一定限制。模型權重采用cc-by-nc-sa-4.0許可證，但作者為符合條件的小型組織提供了許可證豁免。
雙許可選項：對于需要去除GPL許可證要求或超出收入限制的商業用戶，提供了雙許可選項。社區與支持：Discord社區：用戶可以在Discord上討論Marker的未來開發和其他相關問題。
文檔與示例：GitHub倉庫提供了詳細的文檔和示例，幫助用戶快速上手。總結：
Marker是一個功能強大、易于使用的PDF轉Markdown工具，通過深度學習模型和OCR技術的結合，實現了高效且準確的文檔轉換。它不僅支持多種文檔類型和語言，還提供了豐富的配置選項和命令行工具，滿足了不同用戶的需求。同時，Marker的社區支持和文檔也非常完善，為用戶提供了良好的使用體驗。