Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks,

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	Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks,
	Yue Xu; Fei Yin; Zhaoxiang Zhang; Chenglin Liu
	2018-07
会议日期	13-19
会议地点	Stockholm, Sweden,
英文摘要	Layout analysis is a fundamental process in document image analysis and understanding. It contains three key sub-processes which are page segmentation, text line segmentation and baseline detection. In this paper, we propose a multi-task layout analysis method that uses a single FCN model to solve the above three problems simultaneously. In our work, a multi-task FCN is trained to segment the document image into different regions (background, main text, comment and decoration), circle the contour of text lines and detect the centerlines of text lines by classifying pixels into different categories. By supervised learning on document images with pixel-wise labeled, the FCN can extract discriminative features and perform pixel-wise classification accurately. Based on the above results, text lines can be segmented and the baseline of each text line can be determined. After that, post-processing steps are taken to reduce noises, correct wrong segmentations and produce the final results. Experimental results on the public dataset DIVA-HisDB [Simistira et al., 2016] containing challenging medieval manuscripts demonstrate the effectiveness and superiority of the proposed method.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/22091]
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	Institute of Automation of Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Yue Xu,Fei Yin,Zhaoxiang Zhang,et al. Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks,[C]. 见:. Stockholm, Sweden,. 13-19.