  Stroke order and velocity are helpful features in the fields of signature
verification, handwriting recognition, and handwriting synthesis. Recovering
these features from offline handwritten text is a challenging and well-studied
problem. We propose a new model called TRACE (Trajectory Recovery by an
Adaptively-trained Convolutional Encoder). TRACE is a differentiable approach
that uses a convolutional recurrent neural network (CRNN) to infer temporal
stroke information from long lines of offline handwritten text with many
characters and dynamic time warping (DTW) to align predictions and ground truth
points. TRACE is perhaps the first system to be trained end-to-end on entire
lines of text of arbitrary width and does not require the use of dynamic
exemplars. Moreover, the system does not require images to undergo any
pre-processing, nor do the predictions require any post-processing.
Consequently, the recovered trajectory is differentiable and can be used as a
loss function for other tasks, including synthesizing offline handwritten text.
  We demonstrate that temporal stroke information recovered by TRACE from
offline data can be used for handwriting synthesis and establish the first
benchmarks for a stroke trajectory recovery system trained on the IAM online
handwriting dataset.
