[1,1,0,48 Lbx256 O1c105]
As layer descriptions: (Input layer is at the bottom, output at the top.)
O1c105: Output layer produces 1-d (sequence) output, trained with CTC,
outputting 105 classes.
Lbx256: Bi-directional LSTM in x with 256 outputs
1,1,0,48: Input is a batch of 1 image of height 48 pixels in greyscale, treated
as a 1-dimensional sequence of vertical pixel strips.
[]: The network is always expressed as a series of layers.
This network works well for OCR, as long as the input image is carefully normalized in the vertical direction, with the baseline and meanline in constant places.
[1,0,0,1 Ct5,5,16 Mp3,3 Lfys64 Lfx128 Lrx128 Lfx256 O1c105]
As layer descriptions: (Input layer is at the bottom, output at the top.)
O1c105: Output layer produces 1-d (sequence) output, trained with CTC,
outputting 105 classes.
Lfx256: Forward-only LSTM in x with 256 outputs
Lrx128: Reverse-only LSTM in x with 128 outputs
Lfx128: Forward-only LSTM in x with 128 outputs
Lfys64: Dimension-summarizing LSTM, summarizing the y-dimension with 64 outputs
Mp3,3: 3 x 3 Maxpool
Ct5,5,16: 5 x 5 Convolution with 16 outputs and tanh non-linearity
1,0,0,1: Input is a batch of 1 image of variable size in greyscale
[]: The network is always expressed as a series of layers.
I have no idea .. why I type [ ] these charcter put in there . Take place an error
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