Off-line restricted-set handwritten word recognition for student identification in a short answer question automated assessment system

Abstract

Handwriting recognition is one of the most intensive areas of study in the field of pattern recognition. Many applications are able to benefit from a robust off-line handwriting recognition technique. An automatic off-line assessment system and a writer identification system are two of those applications. Off-line automatic assessment systems can be an aid for teachers in the marking process; they can reduce the time consumed by the human marker. There has only been limited work undertaken in developing off-line automatic assessment systems using handwriting recognition, and none in developing student identification systems, even though such systems would clearly benefit the education sector. In order to develop a complete off-line automatic assessment system, student identification using full student names is proposed in this paper. The Gaussian Grid and Modified Direction Feature Extraction Techniques are investigated in order to develop the proposed system. The recognition rates achieved using both techniques are encouraging (up to 99.08% for the Modified Direction feature extraction technique, and up to 98.28% for the Gaussian Grid feature extraction technique.