MRF Based Text Binarization in Complex Images using Stroke Feature
Wang Yanna; Shi Cunzhao; Wang Chunheng; Xiao Baihua
2015
Conference NameInternational Conference on Document Analysis and Recognition (ICDAR)
Source PublicationICDAR
Conference Date2015.8.23-2015.8.26
Conference PlaceFrance
AbstractThis 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.
KeywordBinarization Text Image Stroke Sub-image Weight Mrf
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12089
Collection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
Corresponding AuthorShi Cunzhao
AffiliationThe State Key Laboratory of Management and Control for Complex Systems,Institute of Automation, Chinese Academy of Sciences
Recommended Citation
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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