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 T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , (2) converting T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> to a two-dimensional point set T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> which has solved the problem of mirror rotation, and (3) converting T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> to a dimensionally reduced point set T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> 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.
Highlights
3D-Handwritten character recognition is one of important method to realize non-contact human-computor interaction
In order to solve the problems of random appearance of mirror rotation and angle rotation, which are caused by dimensionality reduction of 3D handwritten characters, we propose a new dimensionality reduction algorithm based on oriented bounding boxes
In order to verify the visual processing effect of the proposed method, we compared it with the dimensionality reduction results of four other algorithms: Principal Component Analysis [5] (PCA), KPCA, Multiple Dimensional Scaling [14] (MDS), and Isometric Mapping [15] (Isomap), which are mainstream and effective algorithms
Summary
3D-Handwritten character recognition is one of important method to realize non-contact human-computor interaction. Leap motion can provide 3D point cloud to track 3D finger movements with 0.01 millimeter accuracy. Based on Leap motion, many algorithms of 3D-Handwritten character recognition have been designed [1]–[3], which proceed the 3D-Handwritten character recognition directly on the 3D track. Three are still mature algorithms of 2D-Handwritten character recognition. As a result, another way to recognize the 3D-Handwritten character is transforming 3D-Handwritten character into 2D character by dimensionality reduction method firstly, and 2D character containing the equivalent information of 3D-Handwritten character is recognized by the 2D character recognition method to finish the recognition of 3D-Handwritten character
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