Problem Statement: Paper-based exams are robust against cheating but impose a massive grading burden on educators. Many solutions (like Moodle) require separate Answer Sheets, which are confusing for students and disconnect the answer from the question context. There is a lack of open-source tools that allow flexible, "single-sheet" exam layouts (where answers are ticked directly next to the question) while still supporting fully automated grading via scanning.
Project Goal: The goal is to develop a "Scan-to-Grade" pipeline. The student will build a web application that allows lecturers to (1) Design an exam with auto-generated alignment markers, (2) Bulk-upload scanned PDF submissions, and (3) Automatically grade them using Computer Vision algorithms to detect ticked checkboxes.
Core Tasks:
- Exam Generation Engine: Create a module that generates PDF exams with Fiducial Markers (e.g., ArUco markers or QR codes) at the corners. These markers are essential for the software to "understand" the page geometry later.
- Computer Vision Pipeline (The Core): Implement a Python backend (OpenCV) that:
- Detects the corner markers to De-skew and align the scanned image.
- Applies perspective transformation to flatten the page.
- Extracts the "checkbox regions" based on the digital template coordinates.
- Uses thresholding/pixel density analysis to determine if a box is "Ticked" or "Empty."
- Review Interface: Build a frontend (React) that shows the scanned exam with the detected answers overlaid, allowing the lecturer to manually correct any detection errors.
Research Focus:
The research focus lies on accuracy evaluation. The student must conduct a stress test using a dataset of ~30 scans with various "messy" marking styles (e.g., faint ticks, crossed-out corrections) to calculate the system's Accuracy, False Positive, and False Negative rated compared to manual grading.
Technologies: React / TypeScript; Python / FastAPI; OpenCV; PDF Generation
Tags: Visual Content Analysis; Multimedia Content Management; Optical Mark Recognition (OMR); EdTech; Automated Assessment; Image Processing; Kalchgruber
