Academic Paper Extractor
Uso comercial aceptado
Máis de 380 modelos
Sen marca de auga
Non é preciso rexistrarse
Modelo:
+ GPT-5, Claude, Gemini
Drop an arXiv preprint, journal paper, or thesis chapter — AI converts it into clean LaTeX-flavored text. Math equations stay as equations, multi-column layouts get unwound, citations preserved. Powered by Meta Nougat-base.
Reading equations + unwinding columns… ~10 sec/page
Opcións avanzadas
Resultado
A faltar fichas.
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A procesar o seu pedido...
Pull text + equations out of arXiv papers, journals, and theses. Math equations are converted to LaTeX, multi-column layouts are unwound, citations are preserved. Powered by Meta Nougat. Free, no signup.
Como usar Academic Paper Extractor
1
Introduza a súa entrada
Escriba texto, envíe un ficheiro ou describa o que queira. Non se precisa conta.
2
Prema para xerar
A nosa IA procesa a túa solicitude en segundos empregando os mellores modelos de código aberto.
3
Obter e compartir
Obteña, copie ou comparta o seu resultado. Gratuito para uso persoal e comercial.
Empregar esta ferramenta mediante API
Automatice esta ferramenta a partir do seu propio código. Punto final REST compatíbel con OpenAI, autenticación por token de portador, non se require SDK adicional. Os custos dos tokens son iguais aos da interface web.
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 Academic Paper Extractor tool on: ..."}]}'Ferramentas libres de IA relacionadas
Academic Paper Extractor — FAQ
Drop in any academic / research paper PDF — arXiv preprint, conference paper, journal article, thesis chapter — and the AI converts it into clean LaTeX-formatted text. Math equations come through as proper LaTeX, multi-column layouts are unwound into reading order, and citations + reference lists are preserved. Built specifically for the kind of dense scientific documents pdftotext mangles.
Meta's Nougat-base — a vision-encoder-decoder model trained on millions of arXiv pages. It treats each PDF page as an image and outputs structured Markdown + LaTeX, which is why equations come through correctly even when they're rendered as raster glyphs in the source PDF.
The Docling tool (PDF to Markdown) uses IBM Granite-Docling — fast, layout-aware, optimized for general business documents like contracts, reports, manuals. Nougat is slower but FAR better on academic papers because it was specifically trained on math + multi-column scientific layouts. Use Docling for business docs, Nougat for research.
Yes — that's the killer feature. Inline math comes back as `$...$`, displayed equations as `$$...$$`. It can read both LaTeX-rendered equations from arXiv submissions AND raster equations scanned from older papers. Quality is publication-grade for the vast majority of papers.
Yes — Nougat unwinds two-column / three-column layouts into proper reading order automatically. No more text jumping mid-sentence between columns. Footnotes are extracted into footnote blocks at the end of each section.
Citation markers `[12]` / `(Smith 2020)` stay inline. Reference lists at the end come through preserved with formatting intact, so you can pipe the output into Zotero / Mendeley / a custom citation parser.
About 8-15 seconds per page on our H200. A typical 10-page conference paper runs in ~2 minutes. Long survey papers (50+ pages) take 8-12 minutes — submit and walk away.
300 tokens per page (floor 600). A 10-page conference paper = 3,000 tokens. A 30-page thesis chapter = 9,000 tokens. The daily free pool covers most casual research-reading.
Pipe it into ChatGPT/Claude for paper summarization, build a personal RAG over a corpus of papers, semantic-search your own library, copy equations directly into LaTeX projects, or just read the paper as plain text on your phone.
Yes — Nougat does its own OCR step. Born-digital arXiv submissions are best (clean equation rendering); scanned older papers work too but math fidelity drops a bit. For best math results on scans, rescan at 300+ DPI before upload.
Processed immediately, the LaTeX text 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/academic-pdf/. Returns {text_url, pages, preview, tokens, share_url}. Bearer auth (sk-free-…) gives 10K free tokens/month. /api/ has the curl example.
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