GOT-OCR 2.0

(General OCR Theory) is a highly innovative, end-to-end open-source vision model developed by research teams to establish a unified architecture for all optical character recognition tasks. Traditional workflows require developers to stitch together multiple independent networks for text line detection, cropping, and text transcription. GOT-OCR 2.0 eliminates this complexity by utilizing a single 5-billion-parameter vision-language model to directly translate raw document images into structured markdown, LaTeX math formulas, musical notation, and multi-column tables. It handles full-page documents with mixed handwriting, geometric shapes, and programming code flawlessly. This streamlined approach eliminates multi-stage pipeline latency, providing a highly precise engine for localized legal, financial, and academic document digitization systems.