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.
翻译:在线阿拉伯曲解字符识别仍然是一个巨大的挑战,因为现有的复杂因素,包括阿拉伯曲解脚本风格、写速、作家情绪等。由于这些不可避免的限制,在线阿拉伯字符识别的准确性仍然很低,并保留了改进的空间。在这项研究中,提出了从在线阿拉伯文字识别的笔迹笔迹特征的纵向和横向方向-方向-长度检测所需临界点的强化方法。每个抽取的中风特征将每个孤立字符分割为某种有意义的模式,称为符号。从这些符号中提取了一个最起码的特征集,用于使用多层透视器对字符进行分类,该符号中含有一个后推进学习算法和经过修改的像形函数激活功能。在这项工作中,实现了两个里程碑;首先,达到固定的象征数,其次是尽量减少最重复的象征数。在实验中,从OHASD基准数据集中选择手写阿拉伯字符,以测试和评价拟议的方法。拟议的方法在艺术特征识别技术方面平均达到98.6%的精确度。