Dimensionality Reduction Method for 3D-Handwritten Characters Based on Oriented Bounding Boxes

Abstract

Dimensionality reduction of 3D-handwritten characters can be problematic because of random mirror rotations and angle rotations which appear after using the traditional algorithms. In order to overcome the above drawbacks of the traditional methods, this study proposes a new algorithm for dimensionality reduction of 3D-handwritten characters based on oriented bounding boxes. First, we get a 3D discrete point set T and generate a 3D trajectory. Then, we apply an oriented bounding box model and determine the projection surface. Next, we perform three coordinate transformations, including (1) pre-transforming the 3D discrete point set T into the projection point set T1, (2) converting T1 to a two-dimensional point set T2 which has solved the problem of mirror rotation, and (3) converting T2 to a dimensionally reduced point set T3 which has solved the problem of angle rotation. Finally, we obtain a dimensionally reduced image without mirror and angle rotations. The experimental results confirm that the proposed method can not only obtain a better visual dimensionally reduced image, but also has a higher recognition rate than the conventional ones.

Keywords

• 3D-handwritten character recognition
• dimension reduction
• oriented bounding box