Problem
Manual grading is slow and inconsistent when submissions include both typed and handwritten responses.
Project
Autograder: Precision in Every Grade
Semi-automated grading system with BERT-based NLP and OCR to evaluate typed and handwritten responses.
Approach
I combined semantic similarity, OCR extraction, and a grading workflow UI to support semi-automated evaluation.
Result
The project demonstrated how NLP and OCR can reduce repetitive grading effort while still keeping human review in the loop.
Design
The main idea was not to remove human evaluation completely, but to shorten the repetitive parts of grading.
Stack choices
- BERT-based semantic comparison for meaning-aware answer matching.
- OCR for scanned or handwritten text extraction.
- React + Flask for an end-to-end grading flow.
Practical constraint
The system worked best as a reviewer assistant, not as a fully autonomous grader.