MRF Based Text Binarization in Complex Images using Stroke Feature
Wang Yanna; Shi Cunzhao; Wang Chunheng; Xiao Baihua
2015
会议名称International Conference on Document Analysis and Recognition (ICDAR)
会议录名称ICDAR
会议日期2015.8.23-2015.8.26
会议地点France
摘要This paper presents a novel binarization technique for text images based on Markov Random Field (MRF) framework. We regard stroke as an obvious feature of text to produce clustering result, which will be optimized by MRF model combining color, texture, context features to get the final binarization. The main innovations of our method are: (1) the integrated image is split into sub-images on which we can automatically acquire seed pixels of foreground and background using stroke feature; and (2) diverse weights are attached to seed pixels according to their location information, then highly confident cluster centers of sub-image can be acquired by gathering weighted seeds. The experimental results show that our method is robust and accurate on both video and scene images.
关键词Binarization Text Image Stroke Sub-image Weight Mrf
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12089
专题复杂系统管理与控制国家重点实验室_影像分析与机器视觉
通讯作者Shi Cunzhao
作者单位The State Key Laboratory of Management and Control for Complex Systems,Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Wang Yanna,Shi Cunzhao,Wang Chunheng,et al. MRF Based Text Binarization in Complex Images using Stroke Feature[C],2015.
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