Mathpix OCR LaTeX Extraction

About

The mathpix-ocr skill bridges the gap between static mathematical documents and active computational structures. It integrates the Mathpix API with a specialized balanced ternary pipeline (Seed 1069) to ensure resilient batch processing of complex PDFs. Beyond simple text extraction, it provides specialized mapping of LaTeX constructs—such as dependent types, identity types, and category theory diagrams—directly into ACSet (Algebraic Database) schemas, making it an essential tool for researchers and developers working in formal verification, type theory, and automated mathematical modeling.

Key Features

• 2 GitHub stars
• Direct mapping of LaTeX constructs to ACSet (Algebraic Julia) schemas
• Sonification support for audio-based progress feedback and error monitoring
• High-fidelity OCR for complex LaTeX and mathematical notation extraction
• Resilient batch PDF processing using unique 7-trit balanced ternary checkpoints
• TAP state integration for monitoring conversion confidence and lifecycle stability

Use Cases

• Building resilient data pipelines for large-scale mathematical document processing with error recovery
• Extracting topos diagrams and spectral sequences from research papers for computational analysis
• Automating the digitization of mathematical textbooks into formal type-theoretic structures