Bounding Boxes Improve Small Language Model Performance in Grading Handwritten Exams
A new study demonstrates that cropping student responses with bounding boxes significantly enhances both the accuracy and computational efficiency of small language models (SLMs) on vision-based grading tasks. Evaluated on scanned handwritten answers from the 2025 Australian Physics Olympiad, SLMs ranging from 4B to 72B parameters showed improved grading performance and reduced computational cost when this preprocessing step was applied. The method addresses challenges posed by visual distractions and large image sizes in automated exam grading.
Why it matters: This approach could make automated grading with SLMs more practical and scalable for educational assessments involving handwritten responses.
Full story at: arXiv Computer Vision ↗