Singer Y, Tishby N
Institute of Computer Science, Hebrew University, Jerusalem, Israel.
Biol Cybern. 1994;71(3):227-37. doi: 10.1007/BF00202762.
A model-based approach to on-line cursive handwriting analysis and recognition is presented and evaluated. In this model, on-line handwriting is considered as a modulation of a simple cycloidal pen motion, described by two coupled oscillations with a constant linear drift along the line of the writing. By slow modulations of the amplitudes and phase lags of the two oscillators, a general pen trajectory can be efficiently encoded. These parameters are then quantized into a small number of values without altering the writing intelligibility. A general procedure for the estimation and quantization of these cycloidal motion parameters for arbitrary handwriting is presented. The result is a discrete motor control representation of the continuous pen motion, via the quantized levels of the model parameters. This motor control representation enables successful word spotting and matching of cursive scripts. Our experiments clearly indicate the potential of this dynamic representation for complete cursive handwriting recognition.
本文提出并评估了一种基于模型的在线草书手写分析与识别方法。在该模型中，在线手写被视为简单摆线笔运动的一种调制，由两个耦合振荡描述，并沿书写线具有恒定的线性漂移。通过对两个振荡器的幅度和相位滞后进行缓慢调制，可以有效地编码一般的笔轨迹。然后将这些参数量化为少量值，而不会改变书写的清晰度。提出了一种用于估计和量化任意手写的这些摆线运动参数的通用过程。结果是通过模型参数的量化级别得到连续笔运动的离散电机控制表示。这种电机控制表示能够成功地进行单词识别和草书脚本匹配。我们的实验清楚地表明了这种动态表示在完整草书手写识别中的潜力。
