Expertise — 01
Neural Math OCR
Handwritten notation to LaTeX pipeline
The Problem
Converting handwritten university lecture notes into structured, professional LaTeX PDFs is manually exhausting, especially for complex mathematical notation.
Architecture
Vision Core
OpenCV
Binarization & deskewing
Inference
pix2tex (Transformer)
Formula prediction
Engine
XeLaTeX
PDF compilation
Localization
Polyglossia
Multi-language support
Implementation
Developed a Python-based preprocessing engine that handles noise reduction and contrast enhancement before feeding segments to a pre-trained Vision Transformer. The output is dynamically injected into Jinja2 LaTeX templates and compiled via XeLaTeX to ensure perfect mathematical typesetting.
Transcribe Time
8s
per page
Accuracy
96.4%
verified
Format
PDF/TEX
dual output
Key Learnings
"Preprocessing is 80% of OCR success. Simple deskewing algorithms improved model accuracy by over 30% compared to raw photo imports."