the proposed capsule layers-based Siamese network model, the accuracy of the within language classifications depends on two factors: the number of characters in the alphabet and visual difference between characters. Some alphabets have visually similar characters. In such cases, although the number of characters in the alphabet is small, the classification accuracy becomes low. Thus, the system architecture can be improved with the representation of the image features using transfer learning. Here, features can be extracted from each character image, using a pre-trained deep neural network, and those images can pass to the Siamese network.
This study can be extended by integrating model in a complete OCR pipeline incorporating a character segmentation and reconstruction algorithm. Also, it is possible to analyse the applicability of the proposed model with complex datasets such as ImageNet [40] and COCO [41] by deepening the Siamese network. Additionally, the knowledge learnt from printed character classification can be used to classify handwritten characters. Further, the model classification accuracy can be improved by using printed characters to train the network at initial stages and then using handwritten characters. This will allow the network to understand the defining attributes of each character and such dataset can be generated easily.
2.5.3 Conclusion
Character recognition is a critical module in applications such as document scanning and optical character recognition. With the emergence of deep learning techniques, languages like English have achieved high classification accuracies. However, the applicability of those deep learning methods is constrained in low resource languages, because of the lack of well-developed datasets. This study has focused on implementing a viable method for classification of handwritten characters in low resource languages. Due to the restrictions on the size of available dataset, this problem is modelled as a one-shot learning problem and solved using Siamese networks based on Capsule networks. Siamese network is a de facto type of network use in one-shot learning, but when it comes to image-related tasks, they still need a large number of training dataset. However, the use of Capsule layers-based Siamese network, which can mitigate information losses in Convolutional neural networks allowed to train a Siamese network with a small number of parameters, datasets and get on par performance as a convolutional network. This model is tested with Omniglot dataset and achieved 30–85% accuracy for different alphabets. Further, the model has shown a classification accuracy of 74.5% for MNIST dataset.
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