# A Survey of Handwritten Character Recognition with MNIST and EMNIST
## 3 EMNIST Database
### State of the Art  
In the original EMNIST paper [67], authors included a baseline using a linear classifier and another technique named OPIUM (Online Pseudo-Inverse Update Method), a classifier introduced by [PERSON] and [PERSON] [70]. Authors declared to have chosen this technique because it generates an analytical solution which is deterministic, and their aim was not to obtain cutting-edge results. In fact, they report an accuracy of 85.15% in the Letters dataset and 95.90% in the Digits dataset using OPIUM. Since [PERSON] et al. [67] reported the performance in terms of accuracy instead of error rate, we have decided to do so as well in this survey. Techniques not belonging to the field of deep learning have been tested on EMNIST with successful results. For example, [PERSON] et al. [71] discrete wavelet transform (DWT) and discrete cosine transform (DCT) were hybridized to extract features from the handwritten samples, later using K-NN and SVM for classification. Authors report that SVM outperform K-NN in all scenarios, attaining an accuracy of up to 89.51% in EMNIST Letters and 97.74% in EMNIST Digits. As it happened with MNIST, outstanding achievements have been possible in EMNIST when testing deep learning techniques. This can be seen in a recent paper by [PERSON] et al. [72] comparing a multi-layer perceptron with CNNs in EMNIST. They report a maximum accuracy of 89.47% using a multi-layer perceptron, and an accuracy of 99.2% using a CNN, although the test set used by authorsis not standard. An early attempt to use deep learning to EMNIST was presented by [PERSON] and [PERSON] [73], who described the application of Markov random field-based CNNs reporting accuracies ranging between 87.77% and 99.75%. [PERSON] et al. [74] have used a CNN with three convolutional layers and two dense layers, reporting an accuracy in EMNIST Digits of 99.62%. [PERSON] et al. [75] used a CNN with two convolutional layers and one dense layer for classifying EMNIST with the purpose of developing a handwritten text classification app for Android, obtaining an accuracy of 87.1% over EMNIST By_Class. An interesting work using capsule layers (a concept recently introduced by [PERSON] et al. [76]) is TextCaps [77], a CNN with 3 convolutional layers and two capsule layers, which obtain accuracies of 95.36% in EMNIST Letters and 99.79% in EMNIST Digits. Another interesting work was published by [PERSON] et al. [78], where they proposed the use of deep convolutional extreme learning machine, where gradient descent is not used to train the network, allowing a very fast learning stage (only 21 min using CPU, according to authors). They tested their proposal only with EMNIST Digits, attaining an accuracy of 99.775%. Finally, [PERSON] and [PERSON] [79] presented a method to generate a hierarchical classifier by automatically studying the confusion matrix of a \"flat\" (regular) classifier. They test logistic regression, multi-layer perceptrons and CNNs, and report the best results for the CNN, although with this technique the hierarchical classification do not beat the flat setting. They report an accuracy of 99.46% for EMNIST Digits and 93.63% for EMNIST Letters. Recently, some authors have made used of neural architecture search for automatically optimizing the hyperparameters of CNN classifiers. For example, [PERSON] and [PERSON] [80] presented EDEN, where they applied neuroevolution obtaining an accuracy of 88.3% in EMNIST Balanced and 99.3% in EMNIST Digits. More recently, [64] have used committees of neuroevolved CNNs using topology transfer learning, obtaining an accuracy of 95.35% in EMNIST Letters and 99.77% in EMNIST Digits. EMNIST database has been used in some application papers as well. As an example, the dataset has also been used by [PERSON] et al. [81] for training neuromorphic deep neural networks and by [PERSON] et al. [82] for the sake of pairwise classification. However, due to the way in which the dataset was used, results are not comparable other works. [PERSON] et al.