Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

阿拉伯语内容识别的自动化技术与拉丁语和中文内容识别的合作伙伴相比处于起步阶段。图书馆、数据中心、历史中心和工作场所都有大量手写的阿拉伯语档案。这些文件的数字化有利于 (1) 以电子方式保存和传输国家历史，(2) 节省物理存储空间，(3) 正确处理文件，以及 (4) 加强通过互联网检索信息和其他媒体。阿拉伯语手写字符识别 (AHCR) 系统面临着若干挑战，包括人类手写体的无限变化以及大型公共数据库的泄漏。在当前的研究中，解决了分割和识别阶段。提出了文本分割挑战和针对每个挑战的一组解决方案。卷积神经网络 (CNN)，深度学习方法，用于识别阶段。CNN 的使用导致不同机器学习分类算法的显着改进。它有助于图像的自动特征提取。经过一系列反复试验后，提出了 14 种不同的原生 CNN 架构。他们在包含 54,115 个手写阿拉伯字符的 HMBD 数据库上接受训练和测试。在原生 CNN 架构上进行了实验，报告的最佳测试准确率为 91.96 CNN 的使用导致不同机器学习分类算法的显着改进。它有助于图像的自动特征提取。经过一系列反复试验后，提出了 14 种不同的原生 CNN 架构。他们在包含 54,115 个手写阿拉伯字符的 HMBD 数据库上接受训练和测试。在原生 CNN 架构上进行了实验，报告的最佳测试准确率为 91.96 CNN 的使用导致不同机器学习分类算法的显着改进。它有助于图像的自动特征提取。经过一系列反复试验后，提出了 14 种不同的原生 CNN 架构。他们在包含 54,115 个手写阿拉伯字符的 HMBD 数据库上接受训练和测试。在原生 CNN 架构上进行了实验，报告的最佳测试准确率为 91.96% . 建议使用名为“HMB-AHCR-DLGA”的迁移学习（TF）和遗传算法（GA）方法来优化识别阶段的训练参数和超参数。预训练的 CNN 模型（VGG16、VGG19 和 MobileNetV2）用于后面的方法。进行了五次优化实验，并报告了最佳组合。报告的最高测试准确率为 92.88 %。