Sketch recognition aims to segment and identify objects in a collection of hand-drawn strokes. In general, segmentation is a computationally demanding process since it requires searching through a large number of possible recognition hypotheses. It has been shown that, if the drawing order of the strokes is known, as in the case of online drawing, a class of efficient recognition algorithms becomes applicable. In this article, we introduce a method that achieves efficient segmentation and recognition in offline drawings by combining dynamic programming with a novel stroke ordering method. Through rigorous evaluation, we demonstrate that the combined system is efficient as promised, and either beats or matches the state of the art in well-established databases and benchmarks.

中文翻译：

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使用动态规划对离线素描场景进行分割和识别

草图识别旨在分割和识别手绘笔画集合中的对象。一般来说，分割是一个计算要求很高的过程，因为它需要搜索大量可能的识别假设。已经表明，如果笔画的绘制顺序已知，如在线绘图的情况下，一类有效的识别算法变得适用。在本文中，我们介绍了一种通过将动态规划与新颖的笔画排序方法相结合来实现离线绘图中高效分割和识别的方法。通过严格的评估，我们证明了该组合系统如承诺的那样高效，并且在完善的数据库和基准测试中优于或匹配最先进的技术。