MNIST dataset is made from [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and Special Database 1 (SD-1). Since SD-3 is labeled by staffs in U.S. Census Bureau, while SD-1 is labeled by high school students in U.S., SD-3 is cleaner and easier to recognize than SD-1 is. Yann LeCun et al. extracted half of samples from each of SD-1 and SD-3 for MNIST training set (60,000 samples) and test set (10,000 samples), where training set was labeled by 250 different annotators, and it was guaranteed that annotators of training set and test set are not completely overlapped.
Yann LeCun, one of the founders of Deep Learning, had large contribution on hand-written character recognition in early dates, and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for hand-written characters. CNN is now a critical key for Deep Learning. From Yann LeCun’s first proposal of LeNet, to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification) Course) CNN achieved a series of astonishing results in Image Classification.
Many algorithms are tested on MNIST. In 1998, LeCun experimented single layer linear classifier, MLP (Multilayer Perceptron) and Multilayer CNN LeNet, which continuously reduced test error from 12% to 0.7% \[[1](#References)\]. Since then, researchers worked on many algorithms such as k-NN (K-Nearest Neighbors) \[[2](#References)\], Support Vector Machine (SVM) \[[3](#References)\], Neural Networks \[[4-7](#References)\] and Boosting \[[8](#References)\], and applied various preprocessing methods, such as distortion removal, noise removal and blurring, to increase recognition accuracy.
本教程中,我们从简单的模型Softmax回归开始,带大家入门手写字符识别,并逐步进行模型优化。
本教程中,我们从简单的模型Softmax回归开始,带大家入门手写字符识别,并逐步进行模型优化。
In this chapter, we start from simple Softmax regression model, and guide readers to introduction of hand-written character recognition, and gradual improvement of models.
1. LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. ["Gradient-based learning applied to document recognition."](http://ieeexplore.ieee.org/abstract/document/726791/) Proceedings of the IEEE 86, no. 11 (1998): 2278-2324.
1. LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. ["Gradient-based learning applied to document recognition."](http://ieeexplore.ieee.org/abstract/document/726791/) Proceedings of the IEEE 86, no. 11 (1998): 2278-2324.
2. Wejéus, Samuel. ["A Neural Network Approach to Arbitrary SymbolRecognition on Modern Smartphones."](http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A753279&dswid=-434)(2014).
2. Wejéus, Samuel. ["A Neural Network Approach to Arbitrary SymbolRecognition on Modern Smartphones."](http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A753279&dswid=-434)(2014).