Building Axiom: structural OCR for handwritten STEM notes
I’ve been building Axiom over the past few months after repeatedly struggling with digitizing my own handwritten STEM notes.
Most OCR tools extract characters well enough. The problem is structural drift:
aligned equations lose alignment
multi-step derivations collapse into paragraphs
numbered problems merge together
tables flatten into plain text
Axiom focuses on preserving structure instead of just transcription accuracy.
The core pipeline:
OCR from image/PDF
Structural prompting tuned for alignment, derivation grouping, and table detection
Post-processing layer to normalize LaTeX/Markdown and stabilize layout
Export as compile-ready LaTeX, Markdown, or searchable PDF
The surprising part: most of the engineering effort went into post-processing and pagination logic rather than OCR itself.
Still improving diagram detection and more complex chemistry layouts.
Would love to connect with others building in the OCR / AI tooling space.
This is a really interesting direction. Most OCR pipelines focus heavily on character accuracy but ignore layout preservation. Curious how you handle multi-line equations or nested derivations when the handwriting spacing varies.