Character segmentation is a challenging task for optical character recognition systems. Traditional methods usually utilize rule-based algorithms but most of them are not applicable in modern intelligent recognition a...
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Character segmentation is a challenging task for optical character recognition systems. Traditional methods usually utilize rule-based algorithms but most of them are not applicable in modern intelligent recognition applications that require high accuracy. It is especially the case for text containing Eastern Asian language characters with complex pictograph structures, such as Chinese. To alleviate this problem, this study proposes an encoder-decoder structure-based multiscale fully convolutional network (MSFCN) model for optical character segmentation. Comparing with other methods, MSFCN can not only effectively extract semantic details from images but also exploit boundary information of intervals between characters, thereby distinguishing characters from a background in pixel level. Extensive experiments have been conducted on two benchmark data sets of ICDAR2013 and MLCS. Obtained results prove that MSFCN achieves state-of-the-art segmentation performance and indicated its practical application value.
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