Scaling Handwritten Student Assessments With a Document Image Workflow System

Vijay Rowtula, Varun Bhargavan, Mohan Kumar, C.V. Jawahar; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2307-2314

With the increase in the number of students enrolled in the university system, regular assessment of student performance has become challenging. This is specially true in case of summative assessments, where one expects the student to write down an answer on paper, rather than selecting a correct answer from multiple choices. In this paper, we present a document image workflow system that helps in scaling the handwritten student assessments in a typical university setting. We argue that this improves the efficiency since the book keeping time as well as physical paper movement is minimized. An electronic workflow can make the anonymization easy, alleviating the fear of biases in many cases. Also, parallel and distributed assessment by multiple instructors is straightforward in an electronic workflow system. At the heart of our solution, we have (i) a distributed image capture module with a mobile phone (ii) image processing algorithms that improve the quality and readability (iii) image annotation module that process the evaluations/feedbacks as a separate layer. Our system also acts as a platform for modern image analysis which can be adapted to the domain of student assessments. This include (i) Handwriting recognition and word spotting (ii) Measure of document similarity (iii) Aesthetic analysis of handwriting (iv)Identity of the writer etc. With the handwriting assessment workflow system, all these recent advances in computer vision can become practical and applicable in evaluating student assessments.