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Convolutional neural networks have been commonly used in computer vision research and applications [12] due to their ability to process a large amount of data and extract meaningful and powerful representations from it [13–15]. Before the era of CNNs, computer vision tasks largely relied on handcrafted features and mathematical modeling. There a large number of applications that relies on features Gabor wavelets [16–18], fractal dimensions [19–21], symmetric axis chords [22].

However, when it comes to handwritten character classification for low resource languages, the deep neural network’s this ability becomes more of a limitation, as not much of labeled training data available.

An ideal solution for handwritten character recognition should be based on zero-shot learning, where no previous sample used to classify or one- shot learning, where only one or few samples are used for training [23]. Several attempts have been made to modify different deep neural networks to match requirements of one-shot learning [24–26].