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Bengali handwritten numeric character recognition using denoising autoencoders

机译：孟加拉手写的数字字符识别使用Denoising AutoEncoders

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摘要

This work describes the recognition of Bengali Handwritten Numeral Recognition using Deep Denoising Autoencoder using Multilayer Perceptron (MLP) trained through backpropagation algorithm (DDA). To bring the weights of the DDA to some good solution a layer wise pre-training is done with Denoising Autoencoders. Denoising Autoencoders using MLP trained through backpropagation algorithm are made by introducing masking noise at input to the Autoencoder to capture meaningful information while hidden layers are remain untouched at pre-training. Those pre-trained Denoising Autoencoders are then stacked to build a DDA. DDA is then converted to a Deep Classifier (DC) by using a final output layer. After a final fine-tune best DC is selected to discriminate classes. Performance of the DC using DDA is compared with the Deep Autoencoder using MLP trained through backpropagation (DA) and Support Vector Machines (SVM). From the experiment it is evident that recognition performance of DDA that is 98.9% is higher than DA and SVM those are 97.3% and 97%. Using their performance at validation set results are further combined to build a Hybrid Recognizer that gives a performance of 99.1%.
机译：这项工作描述了使用深度去噪自动化器使用通过BackPropagation算法（DDA）训练的多层的Perceptron（MLP）来识别Bengali手写数字识别。为了将DDA的重量与一些好的解决方案带来一层明智的预训练，通过去噪自动化。通过通过BackPropagation算法训练的MLP培训的去噪自动探测器是通过在IsiaNEncoder的输入中引入屏蔽噪声来捕获有意义的信息，而隐藏层在预训练中保持不受影响。然后堆叠预培训的去噪自身码器以构建DDA。然后使用最终输出层将DDA转换为深度分类器（DC）。选择最终的微调最佳DC以区分类别。使用DDA使用DDA的DC性能与通过BackPropagation（DA）培训的MLP培训并支持向量机（SVM）。从实验中明显看出，DDA的识别性能高于DA和SVM，SVM为97.3％和97％。在验证集中使用它们的性能，进一步组合以构建一个混合识别器，其性能为99.1％。

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