Academic Paper Extractor
Kommersiële gebruik OK
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Model:
+ 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
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Resultaat
Tokens loop laag.
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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.
Hoe om te gebruik Academic Paper Extractor
1
Tik jou invoer in
Tik teks, oplaai 'n lêer, of beskryf wat jy wil hê. Nee rekening benodig.
2
Kliek genereer
Ons kunsmatige intelligensie verwerk jou versoek in sekondes deur die beste ope-bou modelle te gebruik.
3
Aflaai klaar gemaak
Laai af, kopieer of deel jou resultaat. Vry vir persoonlike en kommersiële gebruik.
Gebruik hierdie program deur middel van API
Outomate hierdie program van jou eie kode. OpenAI- compatibleREST- end point, Beer-token auth, nee ekstra SDK benodig. Token kos ooreenstem die web koppelvlak.
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: ..."}]}'Verwante Free-KI-gereedskap
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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