Analysis and comparison of machine learning classifiers and deep neural networks techniques for recognition of Farsi handwritten digits

Nanehkaran Y. A.; Zhang Defu; Salimi S.; Chen Junde; Tian Yuan; Al-Nabhan Najla

摘要

Handwriting recognition remains a challenge in the machine vision field, especially in optical character recognition (OCR). The OCR has various applications such as the detection of handwritten Farsi digits and the diagnosis of biomedical science. In expanding and improving quality of the subject, this research focus on the recognition of Farsi Handwriting Digits and illustration applications in biomedical science. The detection of handwritten Farsi digits is being widely used in most contexts involving the collection of generic digital numerical information, such as reading checks or digits of postcodes. Selecting an appropriate classifier has become an issue highlighted in the recognition of handwritten digits. The paper aims at identifying handwritten Farsi digits written with different handwritten styles. Digits are classified using several traditional methods, including K-nearest neighbor, artificial neural network (ANN), and support vector machine (SVM) classifiers. New features of digits, namely, geometric and correlation-based features, have demonstrated to achieve better recognition performance. A noble class of methods, known as deep neural networks (DNNs), is also used to identify handwritten digits through machine vision. Here, two types of introduce its expansion form, a convolutional neural network (CNN) and an auto-encoder, are implemented. Moreover, by using a new combination of CNN layers one can obtain improved results in classifying Farsi digits. The performances of the DNN-based and traditional classifiers are compared to investigate the improvements in accuracy and calculation time. The SVM shows the best results among the traditional classifiers, whereas the CNN achieves the best results among the investigated techniques. The ANN offers better execution time than the SVM, but its accuracy is lower. The best accuracy among the traditional classifiers based on all investigated features is 99.3% accuracy obtained by the SVM, and the CNN achieves the best overall accuracy of 99.45%.

机译：手写识别仍然是机器视野领域的挑战，尤其是在光学字符识别（OCR）中。 OCR具有各种应用，例如检测手写的波斯语数字和生物医学科学的诊断。在扩大和提高对象的质量方面，这项研究侧重于识别生物医学科学中的波斯语手写数字和插图应用。在涉及通用数字数字信息集合的大多数情况下，广泛使用手写波斯语数字的检测，例如读取邮政编码的检查或数字。选择适当的分类器已成为在识别手写数字中突出显示的问题。本文旨在识别用不同的手写风格写的手写的波斯语数字。数字使用几种传统方法进行分类，包括k最近邻居，人工神经网络（ANN），并支持向量机（SVM）分类器。数字的新功能，即基于几何和相关的特征，已经证明了实现更好的识别性能。一种贵族的方法，称为深度神经网络（DNN），也用于通过机器视觉识别手写的数字。这里，实现了两种类型的引入其扩展形式，卷积神经网络（CNN）和自动编码器。此外，通过使用CNN层的新组合，可以获得对Carsi数字的改进结果。比较了DNN的和传统分类器的性能，以研究准确性和计算时间的改进。 SVM显示了传统分类器之间的最佳效果，而CNN在调查技术中实现了最佳结果。 ANN提供比SVM更好的执行时间，但其准确性较低。基于所有调查特征的传统分类器之间的最佳精度是SVM获得的99.3％，CNN获得最佳总精度为99.45％。

Analysis and comparison of machine learning classifiers and deep neural networks techniques for recognition of Farsi handwritten digits

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