In spite of widely discussed, drawing order recovery (DOR)
from static images is still a great challenge task. Based on
the idea that drawing trajectories are able to be recovered
by connecting their trajectory components in correct orders,
this work proposes a novel DOR method from static images.
The method contains two steps: firstly, we adopt a convolution
neural network (CNN) to predict the next possible
drawing components, which is able to covert the components
in images to their reasonable sequences. We denote
this architecture as Im2Seq-CNN; secondly, considering possible
errors exist in the reasonable sequences generated by
the first step, we construct a sequence to order structure
(Seq2Order) to adjust the sequences to the correct orders.
The main contributions include: (1) the Img2Seq-CNN step
considers DOR from components instead of traditional pixels
one by one along trajectories, which contributes to static images
to component sequences; (2) the Seq2Order step adopts
image position codes instead of traditional points’ coordinates
in its encoder-decoder gated recurrent neural network
(GRU-RNN). The proposed method is experienced on two
well-known open handwriting databases, and yields robust
and competitive results on handwriting DOR tasks compared
to the state-of-arts.