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.
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:
Lfys<h>layer, e.g. in the default VGSL1,36,0,1 Ct3,3,16 Mp3,3 Lfys48 Lfx96 Lrx96 Lfx192S1(1x<h>)1,3element)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.