I improved my handwritten math OCR (now preserves derivations)

Name: I improved my handwritten math OCR (now preserves derivations)
Availability: InStock
Author: mrajatnath

by mrajatnath·Mar 4, 2026·1 point·4 comments

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AI Analysis

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Math OCR that preserves derivation structure, not just transcription—solves a real student pain.

Strengths

•Genuine problem: existing OCR destroys math structure; Axiom focuses on layout preservation, not character accuracy alone.
•Clean web interface with upload, preview, and export—feels like a real product, not a proof-of-concept.
•LaTeX + Markdown output is searchable and future-proof, not locked into proprietary format.

Weaknesses

•No details on accuracy metrics, model choice, or how layout preservation actually works under the hood.
•Math OCR is a narrow use case; Mathpix and Adobe Acrobat already handle this, though may not preserve structure equally well.

Post Description

I built this after almost losing a semester’s worth of handwritten math derivations.

I was taking a Signals and Systems course and filling notebooks with Laplace transforms and long derivations. Before finals I tried digitizing them so I could search my notes.

Everything failed.

Most OCR tools can recognize the characters, but they destroy the structure that makes math readable:

- aligned equations lose alignment - multi-step derivations collapse into paragraphs - numbered problems merge together - tables flatten into plain text

So I built *Axiom*.

Instead of focusing only on transcription accuracy, it focuses on *preserving mathematical structure*.

Upload a photo of handwritten STEM notes and it returns structured Markdown with real LaTeX — keeping aligned equations, derivation steps, and problem blocks intact.

Under the hood it’s basically:

image → vision model → structured Markdown + LaTeX → KaTeX render

Most of the work ended up being in *layout preservation*, not OCR.

https://www.useaxiomnotes.com/app

Happy to answer questions.