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(searched for: doi:10.34104/ajeit.022.013026)
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Australian Journal of Engineering and Innovative Technology pp 27-31; https://doi.org/10.34104/ajeit.022.027031

Abstract:
In the Bangla language, there are 50 complex-shaped characters and working with this huge amount of characters with an appropriate set of features is a tough problem to recognize handwritten characters. Moreover, ambiguity and precision errors are common in handwritten words. Furthermore, among a large number of complex-shaped letters, some are quite similar in shape, making handwritten Bangla characters difficult to recognize. In this work, we proposed a convolutional neural network-based approach for recognizing the handwritten Bangla alphabet. In character recognition, the convolutional neural network (CNN) outperforms most of the other models. However, to guarantee a satisfactory performance, CNNs usually need a great number of samples. Bangla handwriting recognition has been a hot topic for several years, but due to the similarity of many Bangla characters, it's difficult to achieve good results. By training and testing on Bangla character datasets, the model gets a 90.22% validation accuracy for Bangalekha isolated dataset and 93.22% validation accuracy for the Ekush dataset.
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