  Maximally stable extremal regions (MSER), which is a popular method to
generate character proposals/candidates, has shown superior performance in
scene text detection. However, the pixel-level operation limits its capability
for handling some challenging cases (e.g., multiple connected characters,
separated parts of one character and non-uniform illumination). To better
tackle these cases, we design a character proposal network (CPN) by taking
advantage of the high capacity and fast computing of fully convolutional
network (FCN). Specifically, the network simultaneously predicts characterness
scores and refines the corresponding locations. The characterness scores can be
used for proposal ranking to reject non-character proposals and the refining
process aims to obtain the more accurate locations. Furthermore, considering
the situation that different characters have different aspect ratios, we
propose a multi-template strategy, designing a refiner for each aspect ratio.
The extensive experiments indicate our method achieves recall rates of 93.88%,
93.60% and 96.46% on ICDAR 2013, SVT and Chinese2k datasets respectively using
less than 1000 proposals, demonstrating promising performance of our character
proposal network.
