Abstract
We present a new structural method of sketched symbol recognition, which aims to recognize a hand-drawn symbol before it is fully completed. It is invariant to scale, stroke number, and order. We also present two novel descriptors to represent the spatial distribution between two primitives. One is invariant to rotation and the other is not. Then a symbol is represented as a set of descriptors. The distance between the input symbol and the template one is calculated based on the assignment problem. Moreover, a fast nearest neighbor (NN) search algorithm is proposed for recognition. The method achieves a satisfactory recognition rate in real time.
Highlights
Sketch recognition is widely used in pen-based interaction, especially with the increasing popularity of devices with touch screens
Sketch recognition refers to recognition of predefined symbols or free-form drawings; in the latter case, the recognition task is generally preceded by segmentation in order to locate individual symbols [5]
Many challenges remain in terms of intraclass compactness and interclass separation due to the variability of sketching, because it is likely that different people have different drawing styles, such as the stroke order, stroke number, and nonuniform scaling, as well as complex local shifts
Summary
Sketch recognition is widely used in pen-based interaction, especially with the increasing popularity of devices with touch screens. It is a natural and efficient means of capturing information by automatically interpreting hand-drawn sketches and it can be the import part of the early design process, where it helps people explore rough ideas and solutions in an informal environment. Many challenges remain in terms of intraclass compactness and interclass separation due to the variability of sketching, because it is likely that different people have different drawing styles, such as the stroke order, stroke number, and nonuniform scaling, as well as complex local shifts
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