GitHub - beatrizalmeidaf/papero-pdf-text-extractor: Fast, open-source PDF text extraction API. Files never stored.

Document structure extraction without the heavyweight stack. PDF → Markdown · JSON · Word · Excel — with reading order, tables, formulas, figures and the position of every block. CPU only. No ML models. Runs in your browser, in Python, or as an API. ▶ Try it in your browser · Quick start · Benchmarks 30 seconds in the browser app: load a PDF, inspect any block, check tables and formulas, export to Word. Your PDF never leaves your machine. (MP4) Why papero Getting the text out of a PDF is easy. Getting its structure back — which column comes first, which lines are a table, where the formula is — is what makes the output usable for RAG, search and LLMs. papero does that with plain geometry, so it stays fast on a laptop CPU. 📖 Reading order Two- and three-column papers read column by column. Headers, footers, page numbers and repeated logos are set aside. ▦ Real tables Ruled, borderless and LaTeX booktabs tables come back as rows and columns — multi-line cells included. Export to CSV or Excel. ∑ Formulas Superscripts, subscripts and math symbols become LaTeX (E = mc^{2}), plus a cropped image of the formula. 📍 Position of everything Every block has a bounding box — cite the exact spot in a RAG answer, draw over the page, or crop it. 🖼 Figures & charts Images and vector charts are cropped to PNG, with their caption, axis labels and legend kept together. 📝 Back to Word Alignment, indents, line spacing, bold runs and fonts are kept, so a .docx export looks like the original page. Also: accents drawn as separate glyphs in LaTeX PDFs (Computa¸ca˜o → Computação), invisible white text used by form generators is dropped, scanned pages go through OCR, and DOCX/PPTX/XLSX/EPUB/HTML are read through Apache Tika. Quick start pip install pdf-text-api from pdf_text_api import extract doc = extract("paper.pdf") print(doc.to_markdown()) Or skip the install: open the browser app, drop a PDF, export to the format you need. More Python — tables, formulas, positions, images, options from pdf_text_api import extract, extract_text doc = extract("paper.pdf", images=True) doc.tables[0].rows # [["Model", "Accuracy"], ["Base", "0.81"], ...] doc.formulas[0].latex # "E = mc^{2}" doc.figures[0].image.data # PNG bytes for block in doc.pages[0].blocks: # reading order, with positions print(block.type, block.bbox, block.text[:60]) doc.to_html() # keeps alignment and indents doc.to_dict() # the full JSON extract("slides.pptx").to_markdown() # any format Apache Tika reads extract_text("contract.pdf").text # fastest: clean text only Option Default pages all "1-3,5,10-" images False crop figures, tables and formulas to PNG tables / formulas True detection on/off ocr "auto" "auto" (scanned pages only), "force", "off" ocr_language "por+eng" Tesseract languages tika True False runs the layout engine alone (no Java) workers 1 processes for long documents CLI pdf-text-api extract paper.pdf -o paper.md --images # Markdown + images/ folder pdf-text-api extract paper.pdf -o paper.json # format from the extension pdf-text-api extract paper.pdf -f csv -o tables.csv # tables only pdf-text-api extract paper.pdf -p 1-5 -f html pdf-text-api extract paper.pdf --fast # clean text only pdf-text-api serve --port 8000 # API + browser app REST API & Docker docker compose up # API + Apache Tika + Tesseract + browser app on :8000 curl -F "[email protected]" "localhost:8000/v1/extract?format=markdown" curl -F "[email protected]" "localhost:8000/v1/extract?format=zip&images=true" -o paper.zip curl -F "[email protected]" "localhost:8000/v1/extract?per_page=true" # blocks + positions One endpoint, POST /v1/extract; interactive docs at /docs. Parameter Default mode structured structured (layout + Tika) or fast (text only) format json json, markdown, text, html, csv, zip pages all 1-3,5,10- per_page false include pages, blocks and positions in the JSON images false crop figures, tables and formulas ocr auto auto, force, off Configuration through environment variables — see .env.example. What comes out Every block knows what it is and where it was: { "type": "table", "bbox": [56.7, 294.8, 481.9, 374.2], "rows": [["Model", "Accuracy"], ["Base", "0.81"]], "caption": "Table 1: Comparison between models." } Output Python · CLI · API Browser app Markdown, plain text, JSON ✓ ✓ HTML (keeps alignment and indents) ✓ ✓ CSV of the tables, ZIP with images ✓ ✓ Word .docx that keeps the page's look — ✓ Excel .xlsx, one sheet per table — ✓ Full JSON schema and block types { "schema": "pdf-text-api/document@1", "engine": "tika+pdfium", "page_count": 12, "metadata": { "title": "...", "author": "...", "language": "en" }, "pages": [{ "number": 1, "width": 595.3, "height": 841.9, "blocks": [{ "id": "p1-b4", "type": "paragraph", "bbox": [74.0, 217.0, 522.0, 275.0], "text": "Atestamos que a estudante ...", "style": { "pt": 11.0, "font": "Arial", "bold": false }, "format": { "align": "justify", "first_line": 42.7, "line_spacing": 1.8 }, "runs": [{ "text": "FULANA DE TAL", "bold": true, "italic": false, "script": null }] }] }] } Block types: heading (with level), paragraph, list_item (with marker), table (with rows), figure, formula (with latex), caption, code, and — kept apart from the text — header, footer, page_number. Bounding boxes are [x0, y0, x1, y1] in points, origin at the top-left of the page. Benchmarks Dense arXiv papers (multi-column, formulas, tables, figures) on one laptop CPU, no GPU. papero · fast returns clean text; papero · structured also rebuilds reading order, tables, formulas and figures — 0 failures on 54 papers, 39 ms per page (median). Reproduce with benchmarks/. papero PyMuPDF pdfplumber pypdf Docling Marker License MIT AGPL MIT BSD MIT GPL Needs ML models / PyTorch no no no no yes yes Multi-column reading order ✓ partial — — ✓ ✓ Structured tables ✓ ✓ ✓ — ✓ ✓ Formulas LaTeX from glyphs + image — — — ✓ ✓ Bounding boxes ✓ ✓ ✓ — ✓ ✓ DOCX / PPTX / XLSX / EPUB ✓ partial — — ✓ partial Runs entirely in the browser ✓ — — — — — ML-based tools still win on very irregular layouts and complex math (stacked fractions, matrices) — papero gives you the formula as approximate LaTeX and as an image so nothing is lost. How it works Two engines run on the same file at the same time: A layout engine on PDFium reads every glyph with its position, font and size, plus every rule and image, and rebuilds columns, tables, formulas, lists and figures with a column-aware XY-cut. Apache Tika adds metadata, tagged-PDF headings, OCR (Tesseract) and every non-PDF format. The browser app runs the same algorithm ported to JavaScript on pdf.js, and CI checks block by block that both engines agree. Limitations Math: LaTeX is rebuilt from glyphs — stacked fractions, matrices and big radicals come out linear (the cropped image is always there). Borderless tables with very narrow gaps between columns can read as text. Scanned PDFs need OCR, which runs on the server path (Tesseract is in the Docker image). Word/Excel export is in the browser app for now. Development git clone https://github.com/beatrizalmeidaf/papero-pdf-text-extractor.git && cd pdf-text-extractor pip install -e ".[dev]" pytest -q # includes real-world regressions ruff check src tests && ruff format --check src tests npm install --prefix tests/js && python tests/js/expected.py tests/js/out && node tests/js/parity.mjs tests/js/out python -m http.server -d web # browser app at http://localhost:8000 src/pdf_text_api/ is the Python engine, API and CLI · web/ is the browser app (GitHub Pages) · tests/js/ checks the two engines agree · benchmarks/ downloads the dataset and draws the chart. Contributing Found a PDF papero gets wrong? That's the most useful issue you can open — attach the file (or a page of it) and say what you expected. Reading order, tables, formulas, encoding, OCR and browser/server differences are all fair game. If papero saves you time, a ⭐ helps other people find it. Keywords: PDF to Markdown · PDF to JSON · PDF to Word · PDF to Excel · PDF table extraction · PDF parser · document parsing · layout analysis · reading order · multi-column PDF · formula extraction · LaTeX · bounding boxes · OCR · Apache Tika · PDFium · pdf.js · RAG preprocessing · LLM document loader · Docling alternative · PyMuPDF alternative · converter PDF para Markdown, Word e Excel · extrair tabelas de PDF · extrair texto de PDF mantendo a formatação · OCR de PDF escaneado MIT © Beatriz Almeida · package and imports keep the name pdf-text-api / pdf_text_api for compatibility.