Online Handwritten Bangla and Devanagari Character Recognition by using CNN: A Deep Learning Concept

Rajatsubhra Chakraborty, Debadrita Mukherjee, Ankan Bhattacharyya, Himadri Mukherjee, Monoj Kumar Sur, Shibaprasad Sen, K. Roy
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引用次数: 1

Abstract

The present experiment deals with online handwriting recognition for two regional languages Bangla and Devanagari using CNN (convolution neural network) a deep learning concept. Our proposed model consists of two convolution and pooling layers and a fully connected network. CNN model give relaxation of producing handcrafted features manually rather generates features, reduce feature dimension automatically and pass the features to a fully connected network for classification purpose. The current experiment is performed on 10000 Bangla basic characters and 1800 Devanagari characters. We have achieved 99.65% and 98.87% recognition accuracy for Bangla and Devanagari character datasets respectively.
使用CNN的在线手写孟加拉语和梵语字符识别:一个深度学习概念
本实验使用深度学习概念CNN(卷积神经网络)处理两种区域语言孟加拉语和德瓦纳加里语的在线手写识别。我们提出的模型由两个卷积和池化层和一个全连接网络组成。CNN模型不再需要手工生成特征,而是自动生成特征,自动降维,并将特征传递给全连通网络进行分类。目前的实验使用了1万个孟加拉语基本汉字和1800个梵语汉字。对孟加拉语和德文语字符数据集的识别准确率分别达到了99.65%和98.87%。
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