An efficient, classification-based approach for grouping pen strokes into objects

• Developed a novel 2-step algorithm for grouping pen strokes.
• Classifying single pen strokes first makes grouping more efficient and effective.
• We have developed an accurate multi-class, single-stroke classifier.
• Our approach to grouping is unique in its formulation as a classification task.
• The 2-step process enables a simple classifier to achieve high grouping accuracy.

Objects in freely drawn sketches often have no spatial or temporal separation, making object identification difficult. We present a two-step stroke-grouping algorithm that first classifies individual strokes according to the type of object to which they belong, and then groups strokes with like classifications into clusters representing individual objects. The first step facilitates clustering by naturally separating the strokes, and both steps fluidly integrate spatial and temporal information. Our single-stroke classifier has comparable accuracy to an existing state-of-the-art single-stroke classifier on text vs. non-text classification, and is significantly more efficient. Furthermore, our classifier is also suitable for applications with more than two classes of strokes. Our approach to grouping is unique in its formulation as an efficient classification task rather than, for example, an expensive search task. In experiments on several types of sketches, our grouping method performed accurately, correctly grouping up to 92% of the ink, with up to 79% of the shapes being perfectly clustered.

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