论文标题
使用独特的功能提取技术手写角色识别
Handwritten Character Recognition Using Unique Feature Extraction Technique
论文作者
论文摘要
图像处理下最艰巨，最迷人的域之一是手写字符识别。在本文中，我们提出了一种特征提取技术，该技术是基于几何，基于区域的混合，梯度特征提取方法的独特特征和三种不同的神经网络，即使用反向传播算法（MLP BP）的多层感知器网络（MLP BP），使用MultiLayer Perceptron网络和MultiLayer Perceptron网络。与最小距离分类器（MDC）一起实施的神经网络（CNN）。该程序得出的结论是，所提出的特征提取算法比其单个对应物更准确，并且卷积神经网络是考虑到这三个考虑的最有效的神经网络。
One of the most arduous and captivating domains under image processing is handwritten character recognition. In this paper we have proposed a feature extraction technique which is a combination of unique features of geometric, zone-based hybrid, gradient features extraction approaches and three different neural networks namely the Multilayer Perceptron network using Backpropagation algorithm (MLP BP), the Multilayer Perceptron network using Levenberg-Marquardt algorithm (MLP LM) and the Convolutional neural network (CNN) which have been implemented along with the Minimum Distance Classifier (MDC). The procedures lead to the conclusion that the proposed feature extraction algorithm is more accurate than its individual counterparts and also that Convolutional Neural Network is the most efficient neural network of the three in consideration.
