This project implements an automated grading system for handwritten or printed exam answers using OCR and vision-language models. It uses PaddleOCR for optical character recognition to extract text from answer images, and CLIP for multimodal matching to verify answers against expected responses. The system processes answer sheets by segmenting regions, recognizing text, and scoring fill-in-the-blank and other question types.