Propose a joint convolutional and recursive neural network structure

bring the combinational feature that recursive neural networks can learn into convolutional neural networks

that is

the raw image is first passed through a convolutional neural network stage with filters trained by k-means clustering

the result is then passed through a convolutional and a recursive neural network stage simultaneously

at last

the obtained feature vector is classified by softmax classifier.Experimental result shows that even with weights randomly set for the second convolutional and recursive neural network

the network reaches a recognition rate of 98.28 %

compared to other network structures

it greatly reduces training time and requires no complex engineering tricks.