2024-07-12
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Marker: Analyse von Open-Source-Tools zur Konvertierung von PDF in Markdown
Marker ist ein Open-Source-Projekt, das von Vik Paruchuri auf GitHub entwickelt wurde. Seine Kernfunktion besteht darin, PDF-Dateien in das Markdown-Format zu konvertieren. Im Folgenden finden Sie eine detaillierte Analyse des Marker-Projekts:
Projektübersicht:
Projektlink: https://github.com/VikParuchuri/marker.git
Verwaltet von: VikParuchuri
Hauptmerkmale: Konvertieren Sie PDF-Dateien schnell und präzise in das Markdown-Format und unterstützen Sie mehrere Dokumenttypen, insbesondere Bücher und wissenschaftliche Arbeiten.
Technische Eigenschaften:
Deep-Learning-Modell: Marker verwendet eine Reihe von Deep-Learning-Modellen, um Text zu extrahieren, das Seitenlayout zu erkennen, Textblöcke zu bereinigen und zu formatieren und sie schließlich zu Markdown-Dokumenten zu kombinieren.
OCR-Unterstützung: Für Szenarien, die OCR erfordern, unterstützt Marker die Verwendung von OCR-Tools wie Surya und Tesseract, um die Genauigkeit der Textextraktion sicherzustellen.
Unterstützung mehrerer Plattformen: Marker kann auf GPU, CPU oder MPS ausgeführt werden, um den Anforderungen verschiedener Hardwareumgebungen gerecht zu werden.
Funktionsdetails:
Dokumentenverarbeitung: Unterstützt das Entfernen von Kopf- und Fußzeilen und anderen Verunreinigungen, das Formatieren von Tabellen und Codeblöcken sowie das Extrahieren und Speichern von Bildern.
Sprachunterstützung: Marker unterstützt alle Sprachen und Benutzer können den OCR-Effekt optimieren, indem sie eine Sprachliste angeben.
Gleichungskonvertierung: Die meisten Gleichungen können in das LaTeX-Format konvertiert werden, sodass mathematische Formeln problemlos in Markdown-Dokumente eingebettet werden können.
Leistung:
Geschwindigkeit und Genauigkeit: Marker zeichnet sich durch Geschwindigkeit und Genauigkeit aus, was ihm insbesondere im Vergleich zu anderen Tools wie Nougat einen erheblichen Vorteil verschafft.
Ressourcennutzung: Bei der Ausführung auf A6000 Ada belegt jede Aufgabe durchschnittlich etwa 4 GB VRAM und unterstützt so die parallele Verarbeitung mehrerer Dokumente.
Benutzerführung:
Installation: Benutzer müssen das marker-pdf-Paket über pip installieren
pip install marker-pdf
(GraphRAG) PS D:python-workspaceGraphRAG> pip install marker-pdf
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Collecting marker-pdf
Downloading https://mirrors.aliyun.com/pypi/packages/05/c1/782f56407ea60bd35c127c829b8e43da99a0da41f6c9ee002cab97e430c5/marker_pdf-0.2.15-py3-none-any.whl (63 kB)
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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-workspaceGraphRAG> 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 ./folderGPT folder
配置：用户可以通过环境变量或配置文件调整Marker的行为，如设置OCR引擎、指定GPU设备、配置内存使用等。
命令行工具：Marker提供了命令行工具，允许用户以批处理方式转换单个或多个PDF文件。
商业使用与许可：
商业限制：虽然研究和个人使用是免费的，但商业使用受到一定限制。模型权重采用cc-by-nc-sa-4.0许可证，但作者为符合条件的小型组织提供了许可证豁免。
双许可选项：对于需要去除GPL许可证要求或超出收入限制的商业用户，提供了双许可选项。
社区与支持：
Discord社区：用户可以在Discord上讨论Marker的未来开发和其他相关问题。
文档与示例：GitHub仓库提供了详细的文档和示例，帮助用户快速上手。
总结：
Marker是一个功能强大、易于使用的PDF转Markdown工具，通过深度学习模型和OCR技术的结合，实现了高效且准确的文档转换。它不仅支持多种文档类型和语言，还提供了丰富的配置选项和命令行工具，满足了不同用户的需求。同时，Marker的社区支持和文档也非常完善，为用户提供了良好的使用体验。
Er widmet sich seit mehr als 30 Jahren der Technologieforschung und beherrscht verschiedene Sprachen wie Java, Linux, Javascript, PHP, CSS usw. Er hat viele Beiträge im Open-Source-Bereich geleistet Entwicklerdokumentationsstation, um einige Themen in der Technologieentwicklung als zukünftige Referenz zu teilen
