论文标题
使用硬冲程功能挖掘的在线阿拉伯语手写角色识别的神经计算
Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining
论文作者
论文摘要
由于现有的复杂性，包括阿拉伯语草书剧本样式，写作速度，作家情绪等，在线阿拉伯语草书性角色识别仍然是一个巨大的挑战。由于这些不可避免的约束，在线阿拉伯角色识别的准确性仍然很低，并且保留了改进的空间。在这项研究中，提出了一种增强的方法，该方法提出了在线阿拉伯文字识别的垂直和水平方向长度的所需关键点。每个提取的中风特征将每个孤立的字符分为一个称为令牌的有意义的模式。从这些令牌中提取了最小特征集，以使用具有后传播学习算法的多层感知器和基于Sigmoid函数的激活功能进行分类。在这项工作中，实现了两个里程碑。首先，达到固定数量的令牌，其次，最大程度地减少了最重复的令牌的数量。对于实验，从OHASD基准数据集中选择手写的阿拉伯字符来测试和评估所提出的方法。所提出的方法的平均准确性为98.6％，在最先进的角色识别技术中。
Online Arabic cursive character recognition is still a big challenge due to the existing complexities including Arabic cursive script styles, writing speed, writer mood and so forth. Due to these unavoidable constraints, the accuracy of online Arabic character's recognition is still low and retain space for improvement. In this research, an enhanced method of detecting the desired critical points from vertical and horizontal direction-length of handwriting stroke features of online Arabic script recognition is proposed. Each extracted stroke feature divides every isolated character into some meaningful pattern known as tokens. A minimum feature set is extracted from these tokens for classification of characters using a multilayer perceptron with a back-propagation learning algorithm and modified sigmoid function-based activation function. In this work, two milestones are achieved; firstly, attain a fixed number of tokens, secondly, minimize the number of the most repetitive tokens. For experiments, handwritten Arabic characters are selected from the OHASD benchmark dataset to test and evaluate the proposed method. The proposed method achieves an average accuracy of 98.6% comparable in state of art character recognition techniques.
