Short answer question examination using an automatic off-line handwriting recognition system and a novel combined feature

Abstract

Off-line automatic assessment systems can be an aid for teachers in the marking process. There has been no recent work in the development of off-line automatic assessment systems using handwriting recognition, even though such systems will clearly benefit the education sector. The reason is many schools and universities in many parts of the world still use paper-based examination. This research proposes the use of a newly developed feature extraction technique called the Modified Water Reservoir, Loop and Gaussian Grid Feature, as well as other feature extraction techniques. These techniques were investigated employing artificial neural networks and support vector machines as classifiers to develop an automatic assessment system for marking short answer questions. The system has high assessment accuracy (up to 94.75% for hand printed, 96.09% for cursive handwritten, and 95.71 % for hand printed and cursive handwritten combined). The proposed system also includes assessment criteria to augment its accuracy.

Off-Line Handwritten Bilingual Name Recognition for Student Identification in an Automated Assessment System

“…A feature extraction technique called WRLGGF is proposed here. Also MDF and GGF extraction techniques were chosen due to their ability to successfully extract important features from images, which have enabled accurate recognition rates to be attained in a number of applications [3], [9].…”

“…There is not much work that has been reported on SIS [1]. In addition, there is only a small amount of research focusing on OFAAS [1], [2], [3], [4]. Commonly for manual assessment, when the marking of examination papers is concluded, the marker has to rewrite each student's mark onto a marking summary sheet.…”

Off-line handwritten Thai name recognition for student identification in an automated assessment system

“…Nevertheless, many applications benefit from off-line handwriting recognition techniques, for example, postal address interpretation, signature verification, and bank cheque verification. However, there is only a small amount of research focusing on off-line assessment systems [3], [4], [5], [6]. To the best of the authors' knowledge, there is no off-line Thai language automated assessment system proposed in the literature.…”