LaTeX-OCR is a specialized optical character recognition system designed to identify and transcribe complex mathematical symbols and their spatial relationships from images. It functions as a machine learning engine that converts visual representations of equations into structured LaTeX code for use in technical documentation and academic typesetting.
The project utilizes a hierarchical vision-based encoding and autoregressive sequence decoding architecture to process input images and generate mathematical notation token by token. Beyond its core recognition capabilities, the system provides an interactive interface for capturing formulas directly from screenshots and exposes a network service that allows external applications to integrate automated transcription into their own workflows.
The software includes a framework for training and fine-tuning models, enabling users to prepare specialized datasets and adjust parameters to improve recognition accuracy for unique symbols or specific handwriting styles. The project is distributed as a Python-based library and includes tools for both command-line interaction and programmatic integration.
