dreamyoungs

dreamyoungs / trex

Public

🦖 Lightweight Rust engine for extracting tables from PDFs — zero external dependencies, single binary.

17
1
100% credibility
Found Mar 01, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

TREX extracts tables from PDF documents into structured JSON or CSV output using lightweight detection methods, accessible via command line, programming languages, or a web service.

How It Works

1
📄 You have PDFs with tables

You're looking at reports or invoices packed with data tables you need to use elsewhere.

2
🔍 Discover TREX

You find TREX, a handy tool that pulls tables out of PDFs cleanly and quickly.

3
⬇️ Get TREX ready

Grab the tool and set it up on your computer in moments—no fuss.

4
Pick your way to use it
⚡
Quick command

Point it at your PDF and instantly get tables as lists.

💻
In your program

Add it to your scripts so apps handle PDFs automatically.

🌐
Web helper

Launch a service to upload and process files from anywhere.

5
✨ Magic extraction

Feed in your PDF, pick smart detection, and watch perfect rows and headers appear.

✅ Tables ready to go

Your data is now neat lists you can copy to spreadsheets, analyze, or build with—saving hours!

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Star Growth

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AI-Generated Review

What is trex?

Trex is a lightweight Rust binary that extracts tables from PDFs into JSON or CSV, handling both gridline-based (lattice) and borderless (stream) layouts with an optional DL router for smarter mode selection. It compiles to a single executable with zero external deps, running at ~30MB RAM—perfect for serverless. Use it via CLI (`trex extract invoice.pdf`), Node.js npm packages (CLI wrapper or native NAPI bindings), Python bindings, or a Dockerized REST API on port 8080.

Why is it gaining traction?

Unlike Python stalwarts like Camelot or Tabula that balloon to 500MB+ with OpenCV/Ghostscript, trex stays lean for Lambda or Cloud Run without OOM kills. Multi-runtime support (Rust core powers Node/Python/Docker) and a feedback loop—log events, retrain the router model via Python scripts—let accuracy improve over time. Devs dig the no-subprocess native Node option and event telemetry for prod monitoring.

Who should use this?

Backend engineers parsing invoices, reports, or financial PDFs in Node/Python services. Serverless teams needing a lightweight Rust HTTP server for API endpoints. Data pipelines where Python github lightweight charts alternatives fail on memory.

Verdict

Grab it if you need a credible lightweight github trex for PDF tables—docs are solid, multilingual, with benches and ML pipeline ready. At 12 stars and 1.0% credibility, it's early; benchmark against your docs before prod, but the single-binary hook makes it worth a spin.

(198 words)

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