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The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems.[1][2] The database is also widely used for training and testing in the field of machine learning.[3][4] It was created by "re-mixing" the samples from NIST's original datasets. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments.[5] Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels.[5]

MNIST sample images.
Sample images from MNIST test dataset.

This is one test sentence. This is a second test sentence. This is a third test sentence, with a comma in the middle, and a quote coming right up: "This is supposed to be a quote."

The MNIST database contains 60,000 training images and 10,000 testing images.[6] Half of the training set and half of the test set were taken from NIST's training dataset, while the other half of the training set and the other half of the test set were taken from NIST's testing dataset.[7] There have been a number of scientific papers on attempts to achieve the lowest error rate; one paper, using a hierarchical system of convolutional neural networks, manages to get an error rate on the MNIST database of 0.23%.[8] The original creators of the database keep a list of some of the methods tested on it.[5] In their original paper, they use a support vector machine to get an error rate of 0.8%.[9] An extended dataset similar to MNIST called EMNIST has been published in 2017, which contains 240,000 training images, and 40,000 testing images of handwritten digits and characters.[10]

  1. ^ "Support vector machines speed pattern recognition - Vision Systems Design". Vision Systems Design. Retrieved 17 August 2013.
  2. ^ Gangaputra, Sachin. "Handwritten digit database". Retrieved 17 August 2013.
  3. ^ Qiao, Yu (2007). "THE MNIST DATABASE of handwritten digits". Retrieved 18 August 2013.
  4. ^ Platt, John C. (1999). "Using analytic QP and sparseness to speed training of support vector machines" (PDF). Advances in Neural Information Processing Systems: 557–563. Retrieved 18 August 2013.
  5. ^ a b c LeCun, Yann; Corinna Cortes; Christopher J.C. Burges. "MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges". Retrieved 17 August 2013.
  6. ^ Kussul, Ernst; Tatiana Baidyk (2004). "Improved method of handwritten digit recognition tested on MNIST database". Image and Vision Computing. 22 (12): 971–981. doi:10.1016/j.imavis.2004.03.008.
  7. ^ Zhang, Bin; Sargur N. Srihari (2004). "Fast k -Nearest Neighbor Classification Using Cluster-Based Trees" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 26 (4): 525–528. doi:10.1109/TPAMI.2004.1265868. PMID 15382657. Retrieved 18 August 2013.
  8. ^ Cires¸an, Dan; Ueli Meier; Jürgen Schmidhuber (2012). "Multi-column deep neural networks for image classification" (PDF). 2012 IEEE Conference on Computer Vision and Pattern Recognition: 3642–3649. arXiv:1202.2745. doi:10.1109/CVPR.2012.6248110. ISBN 978-1-4673-1228-8.
  9. ^ LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). "Gradient-Based Learning Applied to Document Recognition" (PDF). Proceedings of the IEEE. 86 (11): 2278–2324. doi:10.1109/5.726791. Retrieved 18 August 2013.
  10. ^ Cohen, Gregory; Afshar, Saeed; Tapson, Jonathan; van Schaik, André (2017-02-17). "EMNIST: an extension of MNIST to handwritten letters". arXiv:1702.05373 [cs.CV].