network topology at CNN-RNN interface

[bertsky]

Calamari's network specs do not contain or require a reshaping/projection operation before the first LSTM layer, this seems to be added automatically.

However, other traditional CNN-RNN implementations offer an alternative element: an LSTM which takes the height axis as sequence and summarises into a single output vector per width position:

• Tesseract traditionally uses Lfys<h> layer, e.g. in the default VGSL 1,36,0,1 Ct3,3,16 Mp3,3 Lfys48 Lfx96 Lrx96 Lfx192
• Kraken offers that as well (but usually rather just reshapes via explicit S1(1x<h>)1,3 element)

Is it perhaps expected that the combination of reshape and CenterNormalizer will do a better job? I wonder whether this has ever been thoroughly investigated. Also, CenterNormalizer might degrade instead of improve horizontal statistics, esp. for handwriting (where some have even argued a need for deslanting), or with grayscale input.