Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems.

手写识别被认为是模式识别和图像处理探索中一个充满活力和启发性的课题。它有许多应用，包括盲人阅读辅助、计算机化阅读和纸质文档处理，使任何手写文档都可搜索并将其转换为结构化文本形式。在识别英语、中文阿拉伯语、波斯语和许多其他语言的手写识别系统时，该技术已经实现了很高的准确率。然而，没有这样的系统来识别库尔德手写体。在本文中，尝试设计和开发一种模型，该模型可以使用深度学习技术识别库尔德字母的手写字符。库尔德语 (Sorani) 包含 34 个字符，主要使用经过修改的字母表的阿拉伯语/波斯语文字。在这项工作中，采用了深度卷积神经网络模型，该模型在手写识别系统中表现出了出色的性能。然后，为包含 4 万多张图像的手写库尔德语字符创建了一个综合数据库。创建的数据库已用于训练用于分类和识别任务的深度卷积神经网络模型。在所提出的系统中，实验结果显示出可接受的识别水平。测试结果报告了 83% 的准确率，训练准确率报告了 96% 的准确率。从实验结果来看，