  Handwritten text recognition is challenging because of the virtually infinite
ways a human can write the same message. Our fully convolutional handwriting
model takes in a handwriting sample of unknown length and outputs an arbitrary
stream of symbols. Our dual stream architecture uses both local and global
context and mitigates the need for heavy preprocessing steps such as symbol
alignment correction as well as complex post processing steps such as
connectionist temporal classification, dictionary matching or language models.
Using over 100 unique symbols, our model is agnostic to Latin-based languages,
and is shown to be quite competitive with state of the art dictionary based
methods on the popular IAM and RIMES datasets. When a dictionary is known, we
further allow a probabilistic character error rate to correct errant word
blocks. Finally, we introduce an attention based mechanism which can
automatically target variants of handwriting, such as slant, stroke width, or
noise.
