# A Survey of Handwritten Character Recognition with MNIST and EMNIST
## 1 Introduction  
In recent years, deep learning-based techniques have been gaining significant interest in the research community for solving a variety of supervised, unsupervised and reinforcement learning problems. One of the most well-known and widely used techniques are convolutional neural networks (CNNs), a kind of neural networks which are able to automatically extract relevant features from input data.  
The properties of convolutional neural networks, including the fact that they are able to retrieve features from multidimensional inputs, turn them into a very interesting alternative for solving problems within the field of computer vision. In fact, computer vision has become a testbed for validating novel techniques and innovations in CNNs.  
More specifically, one dataset has been widely used for this purpose: the MNIST dataset. MNIST is a database of labeled handwritten digits, with separate training and test sets, and therefore is an easily interpretable domain that allows a fast comparison between different techniques. In fact, MNIST is so widely used that it is even used in \"hello-world\" tutorials of some deep learning frameworks, such as TensorFlow [1].  
Soon after the release of the MNIST dataset in 1998, some authors have released rankings including the performance of different works. In fact, the first ranking was published by [PERSON] et al. [2], the authors of the original MNIST paper, and has included works as recent as 2012. To the best of our knowledge, the most recent and exhaustive ranking has been released by [PERSON] [3], updated as of 2016. However, this ranking is still very incomplete and omits a substantial number of relevant works,as well as those published after 2016. Finally, many authors of papers reporting results on the MNIST dataset have compared their performance against related works; however, they often include only other works using similar techniques.  
As a consequence of this, there are not exhaustive and updated rankings of the MNIST database; and this paper intends to fill that gap. In particular, it gathers and summarizes all the works we have found reporting results on the MNIST dataset that are competitive; i.e., whose results are better than a 1% test error rate, classifying them according to two different criteria: whether they perform preprocessing and/or data augmentation or not, and whether the techniques used to obtain a given result involve convolutional neural networks or not. This paper has been written with completeness in mind, and therefore includes not only works published in journals and international conferences, but also those available in pre-print and source code repositories. However, results that are reported in works not published after a peer-review process are explicitly noted as so.  
Additionally, as of today, MNIST is widely considered a non-challenging database anymore. As a consequence of it, an extended version of MNIST, namely EMNIST dataset was released in April 2017. EMIST comprises both handwritten digits and handwritten letters, is significantly larger than MNIST and data comes from a different source. In this paper, we also describe the acquisition of the EMNIST dataset and a survey of the state-of-the-art.  
This paper is structured as follows: Section 2 describes the acquisition process for the MNIST database as well as the data format, and surveys the state of the art for the MNIST dataset. Then, Section 3 explains the acquisition process for the EMNIST database and the different subsets along with their format, later describing works using this dataset as well as NIST Special Database 19 (from which EMNIST is obtained). Finally, Section 4 summarizes the main findings found when surveying works on MNIST and EMNIST.