Deep Convolutional Network for Handwritten Chinese Character Recognition
In this project we explored the performance of deep convolutional neural network on recognizing handwritten Chinese characters. We ran experiments on a 200-class and a 3755-class dataset using convolutional networks with different depth and filter numbers. Experimental results show that deeper network with larger filter numbers give better test accuracy. We also provide a visualization of the learned network on the handwritten Chinese characters.
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