I underestimated how hard handwritten notes are for computers

When I started building Axiom, I thought the hardest part would be OCR.

Turns out that was the easy part.

The real difficulty appeared when I tested the system on actual student notes.

Handwritten math is messy in ways that most software simply isn’t designed to handle.

Students draw arrows between steps.

They add corrections above equations.

They write side notes in the margins.

Tables are drawn by hand.

Sometimes half an equation appears on the next line.

Humans understand this instantly.

Computers don't.

At first I assumed existing OCR tools would solve most of the problem. But what they usually produce is something that technically contains the text, yet loses the structure that makes math readable.

I realized something important while building this:

Digitizing math isn't just a recognition problem.

It's a layout and reasoning problem.

You have to understand which steps belong together, where a derivation starts and ends, and when a block of math should stay grouped.

Once I started thinking about the problem that way, the system improved a lot.

What surprised me most while working on this project is how much information is actually encoded in the layout of handwritten notes, not just the symbols themselves.

Axiom is my attempt to preserve that structure when converting notes into LaTeX or Markdown.

Still learning a lot while building it.

Curious if anyone here has worked on document AI or handwritten recognition problems — the space is deeper than I expected.