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Academic Paper Extractor

Whakama ā-pūnaha OK 380+ tauira Kāore he tohu wai Kāore he kōwhiringa e hiahiatia ana
Kāhua:
+ 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.

Drop a research paper PDF here or click to upload

PDF up to 50 MB. ~300 tokens per page (math-aware).

— —
Reading equations + unwinding columns… ~10 sec/page
Ko ngā kōwhiringa hōhonu
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Want better results? Kāhua Premium (GPT-5, Claude, Gemini) deliver higher quality. View Plans

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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.

He pēhea te whakamahi Academic Paper Extractor

1
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Type i te kupu, tuku i tētahi faila, whakaahua rānei i te mea e hiahiatia ana e koe. Kāore he tatau e hiahiatia ana.

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Ka taea te whakataki i te papatono Kitenga te kī API 
curl -X POST https://api.free.ai/v1/chat/ \
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  -d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Use the Academic Paper Extractor tool on: ..."}]}'

E pā ana ki ngā utauta AI wātea

PDF to Markdown
PDF Extractor
PDF to Text
Research Paper to Text
arXiv PDF Extractor

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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