Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. 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 dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset 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 a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems.
翻译:手写识别是图象处理和模式识别领域积极和具有挑战性的研究领域之一。它有许多应用,包括:视觉障碍阅读辅助、自动阅读和银行检查处理、使任何手写文件可以搜索,以及将其转换成结构文本等。此外,英文、中文、阿拉伯文、波斯文和许多其他语言的笔迹识别系统记录了高精度。然而,没有这样的系统可供脱线库尔德笔迹识别。在本文中,试图设计和开发一个模型,以利用深学习技术识别库尔德字母的手写字符。库尔德语(索拉尼语)包含34个字符,主要使用基于阿拉伯语的文字和经修改的字母。在这项工作中,深进化神经网络模型在笔迹识别系统中显示堪称性。随后,为手写库尔德字符创建了一套全面的数据集,其中包含4万多幅图像。创建的数据集被用于培训深进化神经网络的分类和识别任务模型。在拟议系统中,实验结果显示一个可接受的、有可接受的深度识别水平。测试结果显示一个从深度精确度的精确度为97的实验性测试结果,还报告了一个类似的学习精确度。