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This paper applies Convolutional Neural Networks (CNNs) for offline handwritten English character recognition. We use a modified LeNet-5 CNN model, with special settings of the number of neurons in each layer and the connecting way between some layers. Outputs of the CNN are set with error-correcting codes, thus the CNN has the ability to reject recognition(More)
This paper presents a novel segmentation-based and lexicon-driven handwritten English recognition systems. For the segmentation, a modified online segmentation method based on rules are applied. Then, convolutional neural networks are introduced for offline character recognition. Experiments are evaluated on UNIPEN lowercase data sets, with the word(More)
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