Adaptive Scaling for Archival Table Structure Recognition | |
Li, Xiao-Hui1,2; Yin, Fei2; Zhang, Xu-Yao1,2; Liu, Cheng-Lin1,2,3 | |
2021-09 | |
会议日期 | 2021-9 |
会议地点 | Lausanne, Switzerland |
关键词 | Archival document Table detection Table structure recognition Adaptive scaling |
英文摘要 | Table detection and structure recognition from archival document images remain challenging due to diverse table structures, complex document layouts, degraded image qualities and inconsistent table scales. In this paper, we propose an instance segmentation based approach for archival table structure recognition which utilizes both foreground cell content and background ruling line information. To overcome the influence from inconsistent table scales, we design an adaptive image scaling method based on average cell size and density of ruling lines inside each document image. Different from previous multi-scale training and testing approaches which usually slow down the speed of the whole system, our adaptive scaling resizes each image to a single optimal size which can not only improve overall model performance but also reduce memory and computing overhead on average. Extensive experiments on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method. |
会议录出版者 | Springer |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/45030] |
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 |
通讯作者 | Liu, Cheng-Lin |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, P.R. China 2.National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, P.R. China 3.CAS Center for Excellence of Brain Science and Intelligence Technology, Beijing, P.R. China |
推荐引用方式 GB/T 7714 | Li, Xiao-Hui,Yin, Fei,Zhang, Xu-Yao,et al. Adaptive Scaling for Archival Table Structure Recognition[C]. 见:. Lausanne, Switzerland. 2021-9. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论