Classifying Hand Written Digits with Deep Learning  

作　　者：Ruzhang Yang 

机构地区：[1]Shanghai Foreign Language School, Shanghai, China

出　　处：《Intelligent Information Management》2018年第2期69-78,共10页智能信息管理（英文）

摘　　要：Recognizing digits from natural images is an important computer vision task that has many real-world applications in check reading, street number recognition, transcription of text in images, etc. Traditional machine learning approaches to this problem rely on hand crafted feature. However, such features are difficult to design and do not generalize to novel situations. Recently, deep learning has achieved extraordinary performance in many machine learning tasks by automatically learning good features. In this paper, we investigate using deep learning for hand written digit recognition. We show that with a simple network, we achieve 99.3% accuracy on the MNIST dataset. In addition, we use the deep network to detect images with multiple digits. We show that deep networks are not only able to classify digits, but they are also able to localize them.Recognizing digits from natural images is an important computer vision task that has many real-world applications in check reading, street number recognition, transcription of text in images, etc. Traditional machine learning approaches to this problem rely on hand crafted feature. However, such features are difficult to design and do not generalize to novel situations. Recently, deep learning has achieved extraordinary performance in many machine learning tasks by automatically learning good features. In this paper, we investigate using deep learning for hand written digit recognition. We show that with a simple network, we achieve 99.3% accuracy on the MNIST dataset. In addition, we use the deep network to detect images with multiple digits. We show that deep networks are not only able to classify digits, but they are also able to localize them.

关 键 词：DIGIT Classification Deep Network GRADIENT DESCENT 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]