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Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B. and Ng, A.Y. (2011) Reading Digits in Natural Images with Unsupervised Feature Learning. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, Granada, 12-17 December 2011, 5.

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• TITLE: Classifying Hand Written Digits with Deep Learning

  AUTHORS: Ruzhang Yang

  KEYWORDS: Digit Classification, Deep Network, Gradient Descent

  JOURNAL NAME: Intelligent Information Management, Vol.10 No.2, March 28, 2018

  ABSTRACT: 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.