RNN: the feature sequence contains 256 features per time-step, the RNN propagates relevant information through this sequence. While the image height is downsized by 2 in each layer, feature maps (channels) are added, so that the output feature map (or sequence) has a size of 32×256. Finally, a pooling layer summarizes image regions and outputs a downsized version of the input. Then, the non-linear RELU function is applied. First, the convolution operation, which applies a filter kernel of size 5×5 in the first two layers and 3×3 in the last three layers to the input. These layers are trained to extract relevant features from the image. OperationsĬNN: the input image is fed into the CNN layers. 1: The NN written as a mathematical function which maps an image M to a character sequence (c1, c2, …).
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