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虐待在手写字符识别多类SVM分类与混合特征提取

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在本文中,我们倾向于描述混合离线写字符识别的特征提取。投影技术可能是一个混合结构,应用数学和相关选项。在开幕式上,投影技术标识的种类和位置中的一些基本笔画的性格。中风是寻找包括水平、垂直的,积极的和消极的倾斜线偏我们倾向于观察到任何字符的结构往往是近似的协助下简单的中风。中风是已知的关联完全不同的性格与所选的基本形状。这些标准化的相关值完全不同领域的角色提供相关选项。创建特征提取额外的强大,我们倾向于添加在第二步确定结构/统计选项相关的选项。额外的结构/统计选项支持预测,概要文件,不变的时刻,端点和连接点。这增加,强大的组合的选择导致157 -变量为每个字符特征向量,足以明确表示,我们发现并确定每个角色。之前,写字符识别的缺点没有回邮意味着我们预计混合特征提取技术处理它。 The extracted feature vector is employed throughout the coaching section for building a support vector machine (SVM) classifier. The trained SVM classifier is after used throughout the testing section for classifying unknown characters. Experiments were performed on written digit characters and uppercase alphabets taken from completely different writers, with none constraint on style. The obtained results were compared with some connected existing approaches. Attributable to the projected technique, the results obtained show higher potency concerning classifier accuracy, memory size and coaching time as compared to those different existing approaches.

Dr.Kathir。Viswalingam, G.Ayyappan

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