使用深度卷积神经网络的孟加拉手写字母识别

Sifat Ahmed, Fatima Tabsun, Abdus Sayef Reyadh, Asif Imtiaz Shaafi, F. Shah
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引用次数: 3

摘要

近年来,物体识别技术有了很大的发展。有很多模型在检测英文手写字母数字字符时表现出色。但是当涉及到孟加拉手写字符识别时,这些预定义的模型略微表现不佳。孟加拉语字符的复杂性和缺乏好的数据集是这些模型性能不佳的主要原因。所以这个问题仍然没有解决。但在孟加拉语数字识别中,一些模型表现得非常好。我们提出了一个深度卷积神经网络模型来识别孟加拉语手写字母。在这个实验中使用的数据集是相当新的,还没有经过DCNN模型的测试。到目前为止,我们的模型在识别字母方面达到了95%的准确率。
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Bengali Handwritten Alphabet Recognition using Deep Convolutional Neural Network
The technology of object recognition has advanced in recent times. There are so many models that performed brilliantly when used to detect English handwritten alphanumeric characters. But when it comes to Bengali handwritten character recognition, these predefined model slightly underperformed. The complexity of Bengali characters and unavailability of a good dataset are the main reasons for the underperformance of these models. So the problem still quite unsolved. But in Bengali digit recognition some of the models performed very well. We propose a Deep Convolutional Neural Network model to recognize Bengali handwritten alphabets. The dataset used in this experiment is pretty new and have not tested with DCNN models. Till now, our model achieves 95% accuracy in recognizing the alphabets.
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