Academic Paper Extractor
상업적 사용 OK
380+ 모델
워터마크 없음
가입이 필요하지 않습니다
모델:
+ 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
고급 옵션
결과
토큰이 부족해요
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하루 5K 토큰 + 10K 보너스 무료 가입
무료로 가입하세요
귀하의 요청을 처리 중...
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.
사용 방법 Academic Paper Extractor
1
입력을 입력하십시오
텍스트를 입력하거나 파일을 업로드하거나 원하는 내용을 설명하세요. 계정이 필요하지 않습니다.
2
생성하기를 클릭하십시오
당사의 AI는 최고의 오픈 소스 모델을 사용하여 몇 초 만에 요청을 처리합니다.
3
다운로드 및 공유
다운로드, 복사 또는 결과를 공유. 개인 및 상업용 무료.
API를 통해 이 도구를 사용
이 도구를 자신의 코드로 자동화하세요. OpenAI 호환 REST 엔드포인트, 베어러 토큰 인증, 추가 SDK 필요 없음. 토큰 비용은 웹 인터페이스와 일치합니다.
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: ..."}]}'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.
이 도구를 어떻게 평가하시겠습니까?