Title: Attention networks for image-to-text,
Abstract: The paper approaches the problem of image-to-text with attention-based
encoder-decoder networks that are trained to handle sequences of characters
rather than words. We experiment on lines of text from a popular handwriting
database with different attention mechanisms for the decoder. The model trained
with softmax attention achieves the lowest test error, outperforming several
other RNN-based models. Our results show that softmax attention is able to
learn a linear alignment whereas the alignment generated by sigmoid attention
is linear but much less precise.