AbstractWartegg Test is a drawing completion task designed to reflect the personal characteristics of the testers. A complete Wartegg Test has eight 4 cm \(\times \) 4 cm boxes with a printed hint in each of them. The tester will be required to use pencil to draw eight pictures in the boxes after they saw these printed hints. In recent years the trend of utilizing high-speed hardware and deep learning based model for object detection makes it possible to recognize hand-drawn objects from images. However, recognizing them is not an easy task, like other hand-drawn images, the Wartegg images are abstract and diverse. Also, Wartegg Test images are multi-object images, the number of objects in one image, their distribution and size are all unpredictable. These factors make the recognition task on Wartegg Test images more difficult. In this paper, we present a complete framework including PCC (Pearson’s Correlation Coefficient) to extract lines and curves, SLIC for the selection of feature key points, DBSCAN for object cluster, and finally YoloV3-SPP model for detecting shapes and objects. Our system produced an accuracy of 87.9\(\%\) for one object detection and 75\(\%\) for multi-object detection which surpass the previous results by a wide margin.KeywordsWartegg testImage processingObject detection 

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Recognition of Graphological Wartegg Hand-Drawings

Wartegg Drawing Completion Test (WDCT) is one of the most commonly used personality analysis tools of graphology, which is the mapping from the inside world to personal qualities. It helps institutes or individuals to have a better knowledge of intrinsic personality characters. However, the WDCT evaluation of the applicants was manually performed by human experts, thus the accessibility and outcome of WDCT are heavily restricted by the availability and experience of the expert. To overcome such issues, this paper proposes the computer-aided WDCT (CA-WDCT) system, a fully-automatic WDCT system based on Digital Image Processing (DIP) and Machine Learning techniques. The CA-WDCT system extracts multimodal features and analyzes them under the Big-Five traits automatically. This CA-WDCT system can mitigate the heavy manual labour of psychologists and provide clients with flexible access.

Graziella Pettinati

Papers

Computer-Aided Wartegg Drawing Completion Test