A Novel Semantic Segmentation Model for Chinese Characters

Character segmentation plays an important role in optical character recognition (OCR). Due to the limitations of feature representation, traditional image analyzing based methods cannot well segment characters with connected or broken strokes, especially for the Chinese characters which usually have complex structures. To solve this issue, this paper proposes a novel segmentation model based on fully convolutional neural networks (FCN). The model first uses convolutional neural networks to extract spatial features, then shares them throughout the whole model. Two FCNs are used to extract character information to form a score map. Finally, character features are reused to adjust the accurate segmentation points in the score map. What’s more, to strengthen the ability of feature representation, a novel compound character feature which can well describe the characters’ outline is also proposed. The proposed method is validated on two datasets: GBSD and CASIA-HWDB-MT, against the methods proposed in the literature. Experimental results show that the proposed model outperforms state-of-the-art methods.