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Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
Currently, owing to the ubiquity of mobile devices, online handwritten Chinese character recognition (HCCR) has become one of the suitable choice for feeding input to cell phones and tablet devices.
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
We design a nine-layer CNN for HCCR consisting of 3, 755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0. 21% drop in accuracy.
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