A novel approach to a dynamic template generation algorithm for multiple-choice forms

Abstract

Multiple-choice (MC) forms have become the dominant, fast, reliable and the easiest tools of assessment in learning environments such as schools. Present automatic grading systems for such forms, though fast and accurate are either rated as expensive due to materials and equipments they require for their operation or can only operate on a particular set of forms or papers. Part of the main reasons this is so is due to the inability of such systems to generate a template from an unknown form or another form produced by a similar system. As a novel step to creating a more dynamic and efficient automatic MC test grading system, we present in this paper a template generation algorithm capable of generating a template from any type of bubble MC test form. The empty sheet is first scanned into digital form and then normalized to a given size. To consistently remove non-relevant objects such as lines from the image, a unique combination of Hough transform (HT) and Region of interest (ROI) is proposed. Finally, the true answer regions are segmented using region processing by calculating their sizes and positions, this result in the final template. The preliminary results showed that the algorithm is dynamic with overall high precision accuracy even at low resolutions.