![]() ![]() Furthermore, some complex handwriting scripts comprise different styles for writing words. The main challenge in handwritten character recognition is to deal with the enormous variety of handwriting styles by different writers. ![]() IntroductionĪutomatic handwriting character recognition has many academic and commercial interests. ![]() The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. ![]()
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