SketchREAD:一个多域草图识别引擎

C. Alvarado, Randall Davis
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引用次数: 50

摘要

我们提出了SketchREAD,一个多域草图识别引擎,能够识别自由绘制的手绘草图。现有的计算机素描识别系统结构复杂,或者脆弱,或者通过限制设计者的绘图自由来实现鲁棒性。我们的系统可以通过提供该领域形状的结构描述来应用于各种领域;不需要训练数据或编程。鲁棒性的模糊性和不确定性固有的复杂,自由绘制的草图是通过使用上下文实现的。该系统使用上下文来指导搜索可能的解释,并使用一种新型的动态构建贝叶斯网络来评估这些解释。这个过程允许系统从低级识别错误(例如,一条线被错误分类为弧)中恢复,否则将导致域级识别错误。我们在两个领域(家谱和电路图)的真实草图上评估了SketchREAD,并发现在这两个领域中,使用上下文对低级形状进行重新分类大大减少了基线系统没有重新解释低级分类的识别错误。我们还讨论了系统在基于草图的用户界面中的潜在作用。
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SketchREAD: a multi-domain sketch recognition engine
We present SketchREAD, a multi-domain sketch recognition engine capable of recognizing freely hand-drawn diagrammatic sketches. Current computer sketch recognition systems are difficult to construct, and either are fragile or accomplish robustness by severely limiting the designer's drawing freedom. Our system can be applied to a variety of domains by providing structural descriptions of the shapes in that domain; no training data or programming is necessary. Robustness to the ambiguity and uncertainty inherent in complex, freely-drawn sketches is achieved through the use of context. The system uses context to guide the search for possible interpretations and uses a novel form of dynamically constructed Bayesian networks to evaluate these interpretations. This process allows the system to recover from low-level recognition errors (e.g., a line misclassified as an arc) that would otherwise result in domain level recognition errors. We evaluated SketchREAD on real sketches in two domains---family trees and circuit diagrams---and found that in both domains the use of context to reclassify low-level shapes significantly reduced recognition error over a baseline system that did not reinterpret low-level classifications. We also discuss the system's potential role in sketch-based user interfaces.
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