Machine Assessment of Shape Copying Tests using Zernike Moment Descriptors

A. Naftel, A. Throuvalas, and G. Evans (UK)


sketch recognition, moment descriptors, classification, dyslexia testing


Visual Motor Integration tests, which involve a subject copying geometric shapes, are often used as one of a battery of tests to assess the needs of a child who may have a specific learning difficulty (SpLD). As part of the Dyslexia Early Screening Test (DEST), the resulting free hand sketches are assessed by an expert who analyses them visually and decides on the degree of similarity between the sketches and a shape copying scoring template. The assessment is time-consuming and rather subjective. In this paper, we investigate the use of Zernike moment descriptors as a feature extraction technique for training a k-nearest neighbour classifier to recognise and automatically assign scores to a set of hand-sketched shapes. A prototype shape copying assessment system DESCAR has been implemented using the Matlab programming environment. Scoring classification accuracy has been evaluated on a test corpus of 840 sketches comprising 120 different drawings of each of 7 different shapes used in the DEST study [16]. Experimental results show that machine score accuracy rates in the range 63.6-77.9% can be obtained in the correct assignment of scores when compared to a human expert assessor. Accuracy rates depend on geometric shape, order of Zernike moments and choice of classification test used.

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