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PDF इत्यस्य Markdown इत्यत्र परिवर्तनार्थं मुक्तस्रोतसाधनानाम् विश्लेषणम्

2024-07-12

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मार्करः : PDF इत्यस्य Markdown इत्यत्र परिवर्तनार्थं मुक्तस्रोतसाधनानाम् विश्लेषणम्
Marker इति एकः मुक्तस्रोतप्रकल्पः Vik Paruchuri इत्यनेन GitHub इत्यत्र विकसितः अस्ति अस्य मूलकार्यं PDF सञ्चिकाः Markdown प्रारूपे परिवर्तयितुं । मार्कर परियोजनायाः विस्तृतं विश्लेषणं निम्नलिखितम् अस्ति ।

परियोजनायाः अवलोकनम् : १.

परियोजनालिङ्कः https://github.com/VikParuchuri/marker.git
निर्वाह : विकपरुचुरी
मुख्यविशेषताः : PDF इत्येतत् Markdown प्रारूपे शीघ्रं सटीकतया च परिवर्तयन्तु, बहुविधदस्तावेजप्रकारस्य समर्थनं कुर्वन्ति, विशेषतः पुस्तकानि वैज्ञानिकपत्राणि च।

तकनीकीविशेषताः : १.

गहनशिक्षणप्रतिरूपम् : मार्करः पाठं निष्कासयितुं, पृष्ठविन्यासस्य अन्वेषणाय, पाठखण्डान् स्वच्छं कर्तुं प्रारूपयितुं च, अन्ते च तान् Markdown दस्तावेजेषु संयोजयितुं गहनशिक्षणप्रतिमानानाम् एकां श्रृङ्खलां उपयुज्यते
OCR समर्थनम् : येषां परिदृश्यानां OCR इत्यस्य आवश्यकता भवति, तेषां कृते Marker पाठनिष्कासनस्य सटीकता सुनिश्चित्य Surya तथा Tesseract इत्यादीनां OCR उपकरणानां उपयोगं समर्थयति ।
बहु-मञ्चसमर्थनम् : मार्करः भिन्न-भिन्न-हार्डवेयर-वातावरणानां आवश्यकतानां पूर्तये GPU, CPU अथवा MPS इत्यत्र चालयितुं शक्नोति ।

विशेषताविवरणम् : १.

दस्तावेजप्रक्रियाकरणम् : शीर्षकाणि, पादलेखाः अन्ये च अशुद्धयः दूरीकर्तुं, सारणीः, कोडखण्डाः च स्वरूपयितुं, चित्राणि निष्कासयितुं, रक्षितुं च समर्थयति ।
भाषासमर्थनम् : मार्करः सर्वान् भाषान् समर्थयति, उपयोक्तारः भाषासूचीं निर्दिश्य OCR प्रभावं अनुकूलितुं शक्नुवन्ति ।
समीकरणरूपान्तरणम् : अधिकांशसमीकरणानि LaTeX प्रारूपे परिवर्तयितुं समर्थः, येन Markdown दस्तावेजेषु गणितीयसूत्राणि एम्बेड् कर्तुं सुलभं भवति ।

प्रदर्शनम्‌:

गतिः सटीकता च : मार्करः वेगे सटीकता च उत्कृष्टः भवति, विशेषतः अन्यसाधनानाम् यथा नूगाट् इत्यादीनां तुलने महत्त्वपूर्णं लाभं ददाति ।
संसाधनस्य उपयोगः : A6000 Ada इत्यत्र चालने प्रत्येकं कार्यं औसतेन प्रायः 4GB VRAM गृह्णाति, यत् बहुविधदस्तावेजानां समानान्तरप्रक्रियाकरणस्य समर्थनं करोति ।

उपयोक्तुः मार्गदर्शनम् : १.

संस्थापनम् : उपयोक्तृभ्यः pip मार्गेण marker-pdf संकुलं संस्थापनीयम्

pip install marker-pdf 


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  Downloading https://mirrors.aliyun.com/pypi/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 495.3 kB/s eta 0:00:00
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


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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。

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व्यक्तिगत प्रोफाइल

सः ३० वर्षाणाम् अधिकं कालात् प्रौद्योगिक्याः शोधकार्यं कर्तुं समर्पितः अस्ति, तथा च जावा, लिनक्स, जावास्क्रिप्ट्, php, css इत्यादिषु विविधभाषासु प्रवीणः अस्ति, मुक्तस्रोतक्षेत्रे सः बहु योगदानं कृतवान् अस्ति विकासक दस्तावेजीकरणस्थानकं भविष्ये सन्दर्भार्थं प्रौद्योगिकीविकासे केचन विषयाः साझां कर्तुं सर्वे तत् पश्यन्तु

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