PDF to Markdown
استفاده تجاری
ص ۳۸۰.
بدون نشان آبی
نیازی به ثبت نام نیست
مدل:
+ 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
گزینههای پیشرفته
نتیجه
. رمزها کم ميشه
Get More Tokens
بيشتر ميخواي؟
ثبت نام مجانی برای 5K توکن/روز + 10K بونوس
ثبت نام
در حال پردازش درخواست شما...
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.
چطور استفاده کنم PDF to Markdown
1
وارد کردن ورودی
متن را تایپ کنید ، یک پرونده را بارگذاری کنید ، یا آنچه را که میخواهید توصیف کنید. حساب نیازی نیست.
2
ایجاد را بزنید
هوش مصنوعی ما درخواست شما را در چند ثانیه با استفاده از بهترین مدلهای منبع باز پردازش میکند.
3
بارگیری و اشتراک
دانلود، کپی یا اشتراکگذاری نتایج خود. برای استفاده شخصی و تجاری رایگان است.
استفاده از این ابزار از طریق API
این ابزار را از کد خودتان خودکار کنید. نقطه پایانی REST سازگار با OpenAI، گواهینامه حامل گواهینامه، نیازی به SDK اضافی نیست. هزینه گواهینامه با رابط وب مطابقت دارد.
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: ..."}]}'ابزارهای هوش مصنوعی آزاد مرتبط
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.
اين ابزار رو چطور رتبه بندي ميکني؟