Department of Robot System Engineering, Tongmyong University, Busan 48520, Korea.
Sensors (Basel). 2020 Oct 17;20(20):5884. doi: 10.3390/s20205884.
Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto's hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, "Poha", for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.
手写字符识别在各种自动化领域变得越来越重要，例如银行签名认证、信件地址邮编识别和法证证据。尽管对象识别技术有所提高，但由于存在许多神秘的手写字符、极草书写的普什图字符以及缺乏研究关注，普什图手写字符识别 (PHCR) 在很大程度上仍然没有得到解决。我们首次在不受限制的环境中提出了一种用于识别普什图手写字符的卷积神经网络 (CNN) 模型。首先，构建了一个新的普什图手写字符数据集“Poha”，包含 44 个字符。对于预处理，应用了深度融合图像处理技术和降噪以优化文本。在卷积层数量及其参数方面进行了优化的 CNN 模型在准确性方面优于常见的深度模型。此外，还评估和比较了一组应用于 Poha 的基准流行 CNN 模型与所提出的模型。实验结果表明，所提出的模型在 PHCR 方面的测试准确率达到 99.64%，优于其他模型。结果表明，我们的模型可能是手写字符识别和自动化 PHCR 应用的有力候选者。
