Scene Text Detection with Novel Superpixel Based Character Candidate Extraction
文献类型：会议论文
| 作者 | Wang C(王聪)1,2 ; Yin F(殷飞)1; Liu CL(刘成林)1,2; Yin, Fei; Wang, Cong; Liu, Cheng-Lin
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| 出版日期 | 2017 |
| 会议日期 | November 10-15, 2017 |
| 会议地点 | Kyoto, Japan |
| 关键词 | Scene Text Detection Superpixel Hierarchical Clustering |
| 页码 | 929-934 |
| 英文摘要 |
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. |
| 会议录出版地 | Osaka, Japan |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20029]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
| 推荐引用方式 GB/T 7714 | Wang C,Yin F,Liu CL,et al. Scene Text Detection with Novel Superpixel Based Character Candidate Extraction[C]. 见:. Kyoto, Japan. November 10-15, 2017. |
入库方式： OAI收割
来源：自动化研究所
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