|MRF Based Text Binarization in Complex Images using Stroke Feature|
|Wang Yanna; Shi Cunzhao; Wang Chunheng; Xiao Baihua
|Conference Name||International Conference on Document Analysis and Recognition (ICDAR)
|Abstract||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.|
|Corresponding Author||Shi Cunzhao|
|Affiliation||The State Key Laboratory of Management and Control for Complex Systems,Institute of Automation, Chinese Academy of Sciences|
Wang Yanna,Shi Cunzhao,Wang Chunheng,et al. MRF Based Text Binarization in Complex Images using Stroke Feature[C],2015.
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