Lightweight PDF parser with layout, tables, formulas and bounding boxes

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. 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 papero-extract 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. 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 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 docker compose up # API + Apache Tika + Tesseract + browser app on :8000 curl -F "file=@paper.pdf" "localhost:8000/v1/extract?format=markdown" curl -F "file=@paper.pdf" "localhost:8000/v1/extract?format=zip&images=true" -o paper.zip curl -F "file=@paper.pdf" "localhost:8000/v1/extract?per_page=true" # blocks + positions One endpoint, POST /v1/extract; interactive docs at /docs. 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." } { "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/. ML-based tools still win on very irregular layouts and complex math (matrices, aligned systems) — 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. Math: rebuilt from glyphs and strokes. Fractions, roots, exponents and indices are recognised; matrices, aligned systems and nested constructs come out linear (the cropped image is always there). In PDFs whose producer renumbered the glyphs of a math font, the Python engine can miss a symbol the browser engine reads by its glyph name. Word export keeps each page on its own page; where Word breaks lines differently, a dense page can run a few lines over onto an extra one. 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. 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 · published as papero-extract on PyPI; imports and CLI keep the name pdf-text-api / pdf_text_api for compatibility.