Knowledge Commons of Institute of Automation,CAS
Page Segmentation Using Convolutional Neural Network and Graphical Model | |
Li, Xiao-Hui1,2; Yin, Fei1; Liu, Cheng-Lin1,2,3 | |
2020 | |
会议名称 | The 14th IAPR International Workshop on Document Analysis Systems |
会议日期 | 2020-7 |
会议地点 | 视频会议 |
出版者 | Springer |
摘要 | Page segmentation of document images remains a challenge due to complex layout and heterogeneous image contents. Existing deep learning based methods usually follow the general semantic segmentation or object detection frameworks, without plentiful exploration of document image characteristics. In this paper, we propose an effective method for page segmentation using convolutional neural network (CNN) and graphical model, where the CNN is powerful for extracting visual features and the graphical model explores the relationship (spatial context) between visual primitives and regions. A page image is represented as a graph whose nodes represent the primitives and edges represent the relationships between neighboring primitives. We consider two types of graphical models: graph attention network (GAT) and conditional random field (CRF). Using a convolutional feature pyramid network (FPN) for feature extraction, its parameters can be estimated jointly with the GAT. The CRF can be used for joint prediction of primitive labels, and combined with the CNN and GAT. Experimental results on the PubLayNet dataset show that our method can extract various page regions with precise boundaries. The comparison of different configurations show that GAT improves the performance when using shallow backbone CNN, but the improvement with deep backbone CNN is not evident, while CRF is always effective to improve, even when combining on top of GAT. |
关键词 | Page segmentation Conditional random field Feature pyramid network Graph attention network |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61733007] |
语种 | 英语 |
七大方向——子方向分类 | 文字识别与文档分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44423 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Liu, Cheng-Lin |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, P.R. China 2.School of Articial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, P.R. China 3.CAS Center for Excellence of Brain Science and Intelligence Technology, Beijing, P.R. China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Li, Xiao-Hui,Yin, Fei,Liu, Cheng-Lin. Page Segmentation Using Convolutional Neural Network and Graphical Model[C]:Springer,2020. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Page Segmentation Us(6979KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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