中文摘要：

在笔式用户界面中，对手绘图形和手写文字的识别通常采用不同的识别算法，因此通过笔画分类将混杂的笔画集自动分离是手绘草图识别中的一个重要研究课题。该文提出一种融合时空上下文的手绘笔画联合分类方法，采用支持向量随机场对时空关联的笔画集进行联合建模，不仅利用笔画自身的特征进行判别分类，还以时空邻域和笔画对特征同时融合了笔画间的时间上下文和空间上下文信息，通过模型环状置信传播（LBP）推断，最终求得最大后验边缘概率准则下的联合分类结果。实验结果表明，该文方法的分类准确率优于基于SVM的单笔画分类方法和基于马尔科夫随机场（MRF）的空间上下文联合分类方法，分类速度能基本满足交互实时性的要求，验证了利用随机场模型融合时空上下文进行笔画分类的可行性和有效性。

英文摘要：

Most pen-based user interfaces are incapable of recognizing both graphical symbols and text with a single recognizer. Thus, it is essential to distinguish between graphical strokes and textual ones before feeding them into the appropriate recognizer. An approach for classifying sketched strokes is presented using Support Vector Random Field （SVRF）. Inputting strokes as well as the interactions among them are jointly modeled by the random field. Not only the unary features of strokes themselves are utilized for discriminative classification, but also their temporal and spatial context are exploited through neighborhood system and features of binary stroke pairs. After applying Loopy Belief Propagation （LBP） inferring, the joint labeling solution according to maximum posterior marginal criterion is estimated. Experimental results show that the classification accuracy of the approach outperforms the Support Vector Machine （SVM） classifier as well as the Markov Random Field （MRF）-based joint classification approach which utilizes spatial context. The speed of classification meets basically the requirement of real-time interaction. Thus the feasibility and effectiveness of the proposed approach are verified.