A set of features independent of character stroke direction and order variations is proposed for online handwritten character recognition. A method is developed that maps features like co-ordinates of points, orientations of strokes at points, and dynamics of orientations of strokes at points spatially as a function of co-ordinate values of the points and computes histograms of these features from different regions in the spatial map. Different features like spatio-temporal, discrete Fourier transform, discrete cosine transform, discrete wavelet transform, spatial, and histograms of oriented gradients used in other studies for training classifiers for character recognition are considered. The classifier chosen for classification performance comparison, when trained with different features, is support vector machines (SVM). The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters. There are 12832 and 2821 samples in training and testing datasets, respectively. SVM classifiers trained with the proposed features has the highest classification accuracy of 92.9\% when compared to the performances of SVM classifiers trained with the other features and tested on the same testing dataset. Therefore, the proposed features have better character discriminative capability than the other features considered for comparison.

翻译：提出了一组独立于字符笔画方向和顺序变化的特征，用于在线手写字符识别。开发了一种方法，在空间上将点的坐标、笔画在点处的方向以及笔画在点处的方向动态等特征映射为坐标值的函数，并从空间映射的不同区域计算这些特征的直方图。研究考虑了其他文献中用于训练字符识别分类器的不同特征，包括时空特征、离散傅里叶变换、离散余弦变换、离散小波变换、空间特征以及方向梯度直方图。用于分类性能比较的分类器选用支持向量机（SVM），该分类器在训练时采用不同特征。用于训练和测试分类器的字符数据集包含96种不同印地语字符的在线手写样本，其中训练集和测试集分别有12832个和2821个样本。与采用其他特征训练并在相同测试集上测试的SVM分类器相比，采用所提特征训练的SVM分类器取得了最高的分类准确率（92.9%）。因此，所提特征相比其他用于比较的特征具有更强的字符区分能力。