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Maximally stable extremal regions (MSER), which is a popular method togenerate character proposals/candidates, has shown superior performance inscene text detection. However, the pixel-level operation limits its capabilityfor handling some challenging cases (e.g., multiple connected characters,separated parts of one character and non-uniform illumination). To bettertackle these cases, we design a character proposal network (CPN) by takingadvantage of the high capacity and fast computing of fully convolutionalnetwork (FCN). Specifically, the network simultaneously predicts characternessscores and refines the corresponding locations. The characterness scores can beused for proposal ranking to reject non-character proposals and the refiningprocess aims to obtain the more accurate locations. Furthermore, consideringthe situation that different characters have different aspect ratios, wepropose 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 usingless than 1000 proposals, demonstrating promising performance of our characterproposal network.

Character Proposal Network for Robust Text Extraction