PDF to Markdown
व्यावसायिक उपयोग ठीक
380+ मॉडल
कोई जलमार्क नहीं
कोई हस्ताक्षर की आवश्यकता नहीं
मॉडल:
+ GPT-5, Claude, Gemini
Drop a PDF — AI converts it into clean GitHub-flavored Markdown with headings, paragraphs, lists, tables, and code blocks all preserved. Powered by IBM Granite-Docling-258M (Apache 2.0). Faster + smarter than plain text extraction.
Extracting layout-aware Markdown… ~5-10 sec/page
उन्नत विकल्प
परिणाम
Tomons कम चल रहा है.
Get More Tokens
❤️ Love this tool? Share it!
< aURUC=" शैली=" शैली:#16AA> हस्ताक्षर एक गोपनीय लिंक प्राप्त करने के लिए और एक दोस्त में 2-2 निशानियाँ प्राप्त करने के लिए.
अधिक चाहते हो?
5K/ दिन + 10Kus के लिए मुक्त पर हस्ताक्षर करें
मुक्त पर हस्ताक्षर करें
आपके निवेदन को प्रोसेस कर रहा है...
Convert any PDF into clean GitHub-flavored Markdown with headings, tables, lists, and code blocks preserved. Powered by IBM Granite-Docling. Free, unlimited, no signup.
कैसे इस्तेमाल करें PDF to Markdown
1
अपना इनपुट भरें
पाठ टाइप करें, फ़ाइल अपलोड करें या वर्णन करें कि आप क्या चाहते हैं. कोई खाता आवश्यक नहीं.
2
उत्पन्न करने के लिए क्लिक करें
हमारे एआई प्रक्रिया सेकंड में आपके अनुरोध को सबसे अच्छा खुले स्रोत मॉडल का उपयोग कर रही है।
3
डाउनलोड (A)
निजी और व्यावसायिक प्रयोग के लिए स्वतंत्र ।
इस औजार का प्रयोग एपीआई के द्वारा करें
इस औजार को अपने कोड से स्वचालित हल करें. बाहर निकलने के लिए INBERTATATATATEREATATATATATE, TACKK रोकिएशन, कोई अतिरिक्त SKCKRT की आवश्यकता नहीं है. वेब इंटरफेस से मिलान करने के लिए.
curl -X POST https://api.free.ai/v1/chat/ \
-H "Authorization: Bearer sk-free-..." \
-H "Content-Type: application/json" \
-d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Use the PDF to Markdown tool on: ..."}]}'सम्बन्धित फ्री एआई औज़ार
PDF to Markdown — FAQ
Drop in any PDF and the AI converts it into clean GitHub-flavored Markdown — headings stay headings, tables stay tables, lists stay lists, code blocks stay code blocks. Goes way beyond plain text extraction; the document's structural hierarchy is preserved so you can drop the output straight into a docs site, an LLM RAG pipeline, or a search index.
IBM Granite-Docling-258M (Apache 2.0). Tiny vision-to-sequence model fine-tuned for layout-aware document conversion — beats pdftotext + much faster + smarter than running a generic vision-language model on each page.
pdftotext is a flat dump — paragraphs and tables collapse into a wall of words. Adobe Export to Word preserves layout but produces .docx + costs ~$15/mo. Docling preserves the SEMANTIC structure (heading levels, lists as lists, tables as Markdown tables) and outputs a format LLMs and dev tools can both consume natively.
LlamaParse and unstructured both have free tiers but cap pages/month and require an API key. Docling-258M runs locally on our GPU + is fully self-hosted Apache 2.0, no per-page metering, no key signup. Quality is competitive with LlamaParse on standard documents.
Yes — tables come back as proper Markdown pipe-tables. Complex multi-column / nested tables are flattened more aggressively (a fundamental Markdown limitation, not the model's fault). For perfect table fidelity, we also support `format=html` via the API which preserves rowspan/colspan.
Granite-Docling does the OCR step itself — works on born-digital AND scanned PDFs alike. Scanned at lower DPI (<150) loses some text accuracy; rescan at 200+ DPI for best results.
Most LaTeX-rendered equations come through as inline `$...$` Markdown math. For research papers with heavy math, we also offer the academic-paper-extract tool (Nougat) which is specifically tuned for equations and citations.
About 5-10 seconds per page on our H200. A 30-page report is ~3-5 minutes. Tiny model means batches of small PDFs are essentially free in the daily pool.
200 tokens per page, with a 500-token floor. A 5-page contract = 1,000 tokens. A 30-page report = 6,000 tokens. The 5K daily free pool covers most typical use.
PDF — born-digital + scanned both supported. Max 50 MB upload. Other document formats (DOCX, EPUB, HTML, etc.) are on the roadmap; for now upload-and-convert with the pdf-conversion tool first.
Processed immediately, the Markdown output is kept (24h anonymous / 7d paid share-link expiry), the source PDF is deleted right after extraction. Never used for training. /privacy/ for the full policy.
Yes — POST a multipart `file` to /v1/document/pdf-to-markdown/. Returns {markdown_url, pages, preview, tokens, share_url}. Bearer auth (sk-free-…) gives 10K free tokens/month. /api/ has the curl example.
आप इस औज़ार को कैसे दरेंगे?