论文标题
使用卷积神经网络识别数字
Digit Recognition Using Convolution Neural Network
论文作者
论文摘要
在模式识别中，数字识别一直是一项非常具有挑战性的任务。本文旨在提取正确的功能，以便可以实现识别数字的更好准确性。数字识别的应用，例如密码，银行检查过程等，以识别有效的用户标识。此前，一些研究人员在模式识别中使用了各种不同的机器学习算法，即KNN，SVM，RFC。这项工作的主要目的是通过使用卷积神经网络（CNN）来识别数字，而无需对数据集进行过多的预处理，以获得最高精度的99.15％。
In pattern recognition, digit recognition has always been a very challenging task. This paper aims to extracting a correct feature so that it can achieve better accuracy for recognition of digits. The applications of digit recognition such as in password, bank check process, etc. to recognize the valid user identification. Earlier, several researchers have used various different machine learning algorithms in pattern recognition i.e. KNN, SVM, RFC. The main objective of this work is to obtain highest accuracy 99.15% by using convolution neural network (CNN) to recognize the digit without doing too much pre-processing of dataset.
