Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel). 2023 Jul 28;23(15):6774. doi: 10.3390/s23156774.
Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults' handwriting, children's handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children's handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children's handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.
近年来，手写阿拉伯字符识别受到了越来越多的研究关注。然而，到目前为止，大多数现有的手写识别系统仅关注成人手写。相比之下，针对儿童手写的研究并不多，也尚未将其视为主要研究问题。与成人手写相比，儿童手写更具挑战性，因为其质量通常较低、变化较大且扭曲较大。此外，这些针对成人数据设计和当前使用的系统大多数都没有针对儿童数据识别目的或应用进行训练或测试。本文提出了一种用于识别儿童手写阿拉伯字母的新卷积神经网络（CNN）模型。本文进行了多项实验，以研究和分析在不同的儿童、成人和两者数据集上训练模型时的影响，以衡量和比较识别儿童手写字符以及区分其笔迹与成人笔迹的性能。此外，基于经验研究和观察提出了一些补充特征，并将其与 CNN 提取的特征相结合，以增强儿童和成人作者群体分类。最后，使用不同的分类器（包括 Softmax、支持向量机（SVM）、k-最近邻（KNN）和随机森林（RF））以及 Hijja 中的儿童数据和 AHCD 中的成人数据的不同数据集组合，评估和比较了提取的深度和补充特征的性能。我们的研究结果表明，训练策略至关重要，并且包含成人数据可以显著提高儿童手写字符识别的准确率，最高可达约 93%。此外，将提出的补充特征与深度特征融合可以提高儿童笔迹识别的性能，最高可达约 94%。
