Most recently, due to the advent of powerful computational systems such as GPUs and TPUs, more solutions have been proposed, especially, with Deep learning.  In [21] for instance, the authors made a case for Online digit recognition using deep learning. They developed a software application to record a dataset which included user information such as age, sex, nationality, and handedness. Thereafter they presented a 1D and 2D ConvNet model which obtained results of 95.86% (using distance and angle), and 98.50% respectively.
Unfortunately, as deep learning methods have yielded exceptional results, they have also empowered Adversarial systems. It was shown by [22] that the changing of 1 pixel can lead to significant misclassification rates. The
authors  showed
that 70.97% of the natural images can be perturbed to at least one target class simply by modifying a single pixel with 97.47% confidence on average. Further information can be found from academic resources.
Methods such as Restricted Boltzmann Machines (RBMs) [28], SVM with inverse fringe feature [29], Echo state networks [30], Discrete Cosine S-Transform (DCST) features with Artificial Neural Networks classifier [31], Neural Dynamics Classification algorithm [32], Bat Algorithm-Optimized SVM [33] have been applied.