TrulyFreeOCR – a Java OCR pipeline in a single fat JAR, zero native deps required

I'm the author of TrulyFreeOCR, an open-source OCR pipeline that turns scanned PDFs into searchable, highly-compressed PDFs. Everything is Apache 2.0 / MIT / BSD - no GPL, no AGPL, no proprietary model weights.

Why I built it

I needed an OCR pipeline for a document processing system where:

• Every dependency had to be business-friendly (no GPL/AGPL)
• Deployment required zero admin rights (no sudo, no brew, no apt-get)
• MRC compression was needed to hit 5-10x file size reduction vs JPEG-only
• Everything had to run offline on CPU - no cloud APIs, no GPU

I surveyed 20+ existing tools (full comparison in the repo's docs) and none fit all requirements. OCRmyPDF is closest but needs Python + Ghostscript + Tesseract as system deps, and MPL-2.0 requires publishing modifications. The VLM models (DeepSeek-OCR, GLM-OCR, etc.) produce better text extraction but need GPUs and don't output PDFs at all.

What it does

• Input: any PDF (scanned, born-digital, or mixed)
• Output: searchable PDF with invisible text layer + MRC compression (JBIG2/CCITT foreground + JPEG background)
• Single fat JAR - one file to copy, one command to run
• Bootstrap script downloads everything (JDK, Gradle, Tesseract, Leptonica, jbig2enc) into project subdirs
• Fully offline, CPU-only
• PDF/A-2b output available
• 7 bundled language models, 100+ more downloadable
• Concurrent OCR (configurable thread pool)

Try it in 3 commands

$ git clone https://github.com/msmarkgu/TrulyFreeOCR.git
$ cd TrulyFreeOCR
$ ./bootstrap.sh
$ ./run.sh tests/simple-text.pdf -o output.pdf

Limitations (being upfront)

• Tesseract-based accuracy - good for clean scans, not SOTA for noisy/photographed docs
• No table/formula extraction yet
• No handwriting recognition
• CPU-only is slower than GPU backends for high volume

Would love feedback - especially from anyone who's tried to deploy OCR in an enterprise environment.

https://github.com/msmarkgu/TrulyFreeOCR