Similarly, promising results from numerous algorithms have prompted the extension to numerous languages and characters. Indian numerals were treated in [23], Persian digits in [24], Bangla Digits [25], Hindu and Arabic digits in [26], Sindhi Numerals [27].
 
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.
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