Abstract
In this article, we study recognition of handwritten Assamese alphabet when Convolutional Neural Networks (CNN) are applied. First, we generate a dataset, consisting of the 52 characters, with over 12k images in total. To this dataset we apply LeNet-5, ResNet-50, InceptionV3, and DenseNet-201 models. Transfer learning is used for faster training. Best accuracy of over 94.62% is obtained on the test data. This is, currently, state of the art performance in Assamese character recognition.
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Notes
- 1.
This dataset is available from: Srinivasan Natesan at natesan@iitg.ac.in.
- 2.
Available from the UCI Machine Learning repository [5].
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Yadav, M., Mangal, D., Natesan, S., Paprzycki, M., Ganzha, M. (2022). Assamese Character Recognition Using Convolutional Neural Networks. In: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds) Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_70
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