CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Scene Text Detection with Novel Superpixel Based Character Candidate Extraction
Wang C(王聪)1,2; Yin F(殷飞)1; Liu CL(刘成林)1,2
Conference NameThe 14th International Conference on Document Analysis and Recognition (ICDAR)
Conference DateNovember 10-15, 2017
Conference PlaceKyoto, Japan
Publication PlaceOsaka, Japan
Maximally stable extremal region (MSER) is popularly used for candidate character candidate extraction in scene text detection. Its requirement of maximum stability hinders high performance on images of high variability. In this paper, we propose a novel character candidate extraction method based on superpixel segmentation and hierarchical clustering. The proposed superpixel segmentation algorithm for scene text image takes advantage of the color consistency of characters and fuses color and edge information. Based on superpixel segmentation, character candidates are extracted by single-link clustering. To improve the accuracy of non-text candidate filtering, we use a deep convolutional neural networks (DCNN) classifier and double threshold strategy for classification. Experimental results on public datasets demonstrate that the proposed superpixel based method performs better than MSER in character candidate extraction, and the proposed system achieves competitive performance compared to state-of-the-art methods.
KeywordScene Text Detection Superpixel Hierarchical Clustering
Indexed ByEI
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang C,Yin F,Liu CL. Scene Text Detection with Novel Superpixel Based Character Candidate Extraction[C]. Osaka, Japan,2017:929-934.
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