So in the paper for OCR pr LaTex formula extraction from image What You Get Is What You See: A Visual Markup Decompiler, they pass the features of the CNN into RNN Encoder. But there is problem that rather than passing the features directly, they have proposed a solution to change it into the grid.
Extract the features from the CNN and then arrange those extracted features in a grid to pass into an RNN encoder. This is the exact language they have used.
What is meant by that? Theoratically speaking, if I have an CNN
without any Dense/Fully Connected layer and produces an output of [batch,m*n*C]
, then how can I change it in the form of a grid
?? Please see the picture below. So after getting the output from the CNN
, they have chnged it somehow before passing it to RNN
. What is the method that one can use to get this transformation?
So if I have to pass something to keras.layers.RNN()(that_desired_grid_format)
, what should be this grid format and how can I change it?