一种基于卷积神经网络的手写数字识别方法

Malathy. S, C. Vanitha, Nirdhum Narayan, Rajesh Kumar, Gokul. R
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引用次数: 8

摘要

手写数字识别在深度学习的应用中有着重要的影响。卷积神经网络已经成为深度学习中的主要方法之一,也是近年来各种成功的重要因素之一,深度学习主要应用于物体识别领域。在本文的工作中,语音输出功能与文本输出功能相结合。将卷积神经网络模型应用于图像分类。用于训练和测试的数据集是MNIST数据集。实时手写数字识别有各种各样的应用。它被应用于检测车辆号码,读取银行支票,在邮局安排信件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Enhanced Handwritten Digit Recognition Using Convolutional Neural Network
Handwritten digit recognition have great impact in the applications of deep learning. Convolutional Neural Network in the deep learning has become one of the major methods and one of the important factors in the various success in recent times and deep learning is used majorly in the area of object recognition. In the paper work, the speech output feature is integrated along with the text output. Convolutional Neural Network model is applied in the image classification. The dataset used to train and test is the MNIST dataset. There are various applications of handwritten digit recognition in the real time. It is applied in detection of vehicle number, reading of bank cheques, the arrangement of letters in the post office.
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