Automated assessment: it's assessment Jim but not as we know it
J. Allan, T. Allen, Nasser Sherkat, P. Halstead · 2002
An extensive literature survey on automated assessment and handwriting recognition has shown that no work has been done in addressing the area of assessment of handwritten exam scripts. We therefore introduce the novel concept of applying image extraction and cursive script recognition (CSR) techniques to the area of automated assessment. We demonstrate the potential for using a holistic CSR engine as the input process for a system capable of automatically scoring handwritten responses to multi-choice questions. This innovative system utilises the constrained nature of simple multiple choice questions to enhance the recognition rate of the handwritten response. Fifty writers were chosen to answer eight multiple choice questions and results show that the system yields an average 83% CSR word accuracy, which enables the system to score over 54% of all response with 99% confidence.