Exploring the Impact of Handwriting Recognition on the Automated Scoring of Handwritten Student Answers

Handwriting is still the preferred writing mode in the classroom, but it makes introducing intelligent tutoring systems much harder. A major building block within these systems are scoring algorithms that assess whether the content of free-text student answers is correct or not. We collect a dataset of handwritten student answers which we make publicly available to foster future research in the field. We use a state-of-the-art system for handwriting recognition containing a line and word segmentation module and a neural recognition model. We find that scoring performance on handwritten answers is significantly worse compared to typed answers due to recognition errors. Using a simple postprocessing step based on a unigram language model is already sufficient to achieve a performance very close to that on typed input.