脱机手写汉字识别的最优采样特征新方法

在脱机手写汉字识别中，笔画形变是造成识别率下降的主要原因，减少笔画形变的影响是提高脱机手写汉字识别率的关键。针对上述问题，提出了最优采样特征。该特征以目前被广泛应用的方向线素特征为基础，在一定的约束条件下，通过移动采样点的位置，可以适应笔画的形变。从而减少特征的类内方差，提高特征的可分性，改进了识别性能。通过在THCHR样本集上进行实验，并对最优采样特征和方向线素特征的实验结果进行比较，验证了最优采样特征的识别率优于方向线索特征。

New Method of Optimal Sampling Features for Offline Handwritten Chinese Character Recognition

In offline handwritten Chinese character recognition, the high variability of the handwriting strokes is the main cause for lowering the recognition performance, thus decreasing the variability of the handwriting strokes is one effective and important way to improve the recognition accuracy. To solve this problem, we propose a new method of optimal sampling features, which are developed from the prevalently used directional features by following procedures. Firstly, four directional factor images are generated from an input binary character image. Next, these four images are transferred through a low pass filter, and then these four low passed images are sampled. The image values at these sampling positions produce a feature vector that is defined as sampling features. In the case of the sampling positions are uniform and fixed, the sampling features are subject to stroke variations, and these stroke variations will increase the within class pattern variability. In order to compensate for stroke variations, the sampling positions should be adaptable to these stroke variations. That is, the sampling positions should be displaced against reference patterns to decrease the within class variability, on the other hand the smoothness of the displacement should be preserved to keep the character's primary structure unchanged. The sampling features satisfying above conditions are defined as optimal sampling features. These two conditions could be expressed as a constrained minimization problem, thus optimal sampling features could be solved in an iteration procedure. For the sake of saving the time cost, a coarse to fine strategy is utilized. Finally, optimal sampling features are obtained, the discrimination of features is increased; and the recognition performance is improved. In order to demonstrate the effectiveness of optimal sampling features, we apply it to the THCHR database and compare it with directional features. The result shows that sampling features achieve higher recognition accuracy than directional features.