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The goal of this study is classifying characters in new alphabets. After fine-tuning the model for the verification job, we expect that it has learned a general enough function to distinguish between any two images. Hence, we could model character classification as a one-shot learning task that uses only one sample to learn or perform a particular task [6]. This study creates a reference set for all the possible classifications with only one image and then feed the verification model with the pairs created by using test image and one image from the reference set and predict a class using the similarity score given by the model. This approach is further extended to improve accuracy and testing purposes, as explained in Section 2.4.