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