Direction properties of online strokes are used to analyze them in terms of homogeneous regions or sub-strokes with points satisfying common geometric properties. Such sub-strokes are called sub-units. These properties are used to extract sub-units from Hindi ideal online characters. These properties along with some heuristics are used to extract sub-units from Hindi online handwritten characters.\\ A method is developed to extract point stroke, clockwise curve stroke, counter-clockwise curve stroke and loop stroke segments as sub-units from Hindi online handwritten characters. These extracted sub-units are close in structure to the sub-units of the corresponding Hindi online ideal characters.\\ Importance of local representation of online handwritten characters in terms of sub-units is assessed by training a classifier with sub-unit level local and character level global features extracted from characters for character recognition. The classifier has the recognition accuracy of 93.5\% on the testing set. This accuracy is the highest when compared with that of the classifiers trained only with global features extracted from characters in the same training set and evaluated on the same testing set.\\ Sub-unit extraction algorithm and the sub-unit based character classifier are tested on Hindi online handwritten character dataset. This dataset consists of samples from 96 different characters. There are 12832 and 2821 samples in the training and testing sets, respectively.

翻译：利用在线笔画的走向特性，将其分析为满足共同几何属性的点构成的同质区域或子笔画（称为子单元）。这些属性被用于从印地语理想在线字符中提取子单元，并结合启发式规则从印地语在线手写字符中提取子单元。\\ 提出了一种从印地语在线手写字符中提取点笔段、顺时针曲线笔段、逆时针曲线笔段及环笔段作为子单元的方法。所提取的子单元在结构上接近于对应印地语在线理想字符的子单元。\\ 通过训练一个分类器，该分类器利用从字符中提取的子单元级局部特征与字符级全局特征进行字符识别，评估了以子单元形式表征在线手写字符局部信息的重要性。该分类器在测试集上的识别准确率达到93.5%，相较于仅使用同一训练集字符全局特征训练并在相同测试集上评估的分类器，该准确率为最高值。\\ 子单元提取算法及基于子单元的字符分类器在印地语在线手写字符数据集上进行了测试。该数据集包含96种不同字符的样本，训练集与测试集分别有12832个和2821个样本。