  Recently, a series of decomposition-based scene text detection methods has
achieved impressive progress by decomposing challenging text regions into
pieces and linking them in a bottom-up manner. However, most of them merely
focus on linking independent text pieces while the context information is
underestimated. In the puzzle game, the solver often put pieces together in a
logical way according to the contextual information of each piece, in order to
arrive at the correct solution. Inspired by it, we propose a novel
decomposition-based method, termed Puzzle Networks (PuzzleNet), to address the
challenging scene text detection task in this work. PuzzleNet consists of the
Segment Proposal Network (SPN) that predicts the candidate text segments
fitting arbitrary shape of text region, and the two-branch Multiple-Similarity
Graph Convolutional Network (MSGCN) that models both appearance and geometry
correlations between each segment to its contextual ones. By building segments
as context graphs, MSGCN effectively employs segment context to predict
combinations of segments. Final detections of polygon shape are produced by
merging segments according to the predicted combinations. Evaluations on three
benchmark datasets, ICDAR15, MSRA-TD500 and SCUT-CTW1500, have demonstrated
that our method can achieve better or comparable performance than current
state-of-the-arts, which is beneficial from the exploitation of segment context
graph.
