Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks
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In spite of advances in object recognition technology, Handwritten Bangla Characters Recognition (HBCR) (such as alpha-numeric and special) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessive cursive in Bangla handwritings. Even the best existing recognizers do not lead to satisfactory performance for practical applications, and have much lower performance than those developed for English alpha-numeric characters. To improve the performance of HBCR, we herein present Bangla handwritten characters recognition methods by employing the state-of-the-art Deep Convolutional Neural Networks (DCNN) including VGG Network, All Convolution Network (All-Conv Net), Network in Network (NiN), Residual Network, FractalNet, and DenseNet. The deep learning approaches have the advantage of extracting and using feature information, improving the recognition of 2D shapes with a high degree of invariance to translation, scaling and other distortions. We systematically evaluated the performance of DCNN models on publicly available Bangla handwritten character dataset called CMATERdb, and achieved the state-of-the-art recognition accuracy when using DCNN models. Such improvement fills a significant gap between practical requirements and the actual performance of Bangla handwritten characters recognizers.
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