摘要
本文针对文本图像首先提出了一种基于小波域多状态隐马尔科夫树模型的自适应文本图像分割算法(Context-Adapted wavelet-domain Hidden Markov Tree,简称为CAHMT),该算法具有较高的分割质量和较低的计算复杂度.其次,为了进一步提高CAHMT算法分割的效果,将该算法与微分算子、尺度系数相结合提出了两种新的文本图像分割算法.最后通过实例阐明了这些算法的有效性.
This paper presents a new document segmentation algorithm, called context-adapted wavelet-domain hidden Markov tree (CAHMT) model, which extends a recently emerged wavelet-domain hidden Markov tree (HMT) model[1]. The proposed CAHMT can achieve more accurate quality in document segmentation with low computation complexity. In addition to further improving the segmenting performance, we combine differential operator and the lowest frequency subband (called scale coefficients in wavelet transform) with CAHMT and produce much better visually segmentation quality than the HMT does.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2007年第1期118-122,共5页
Acta Electronica Sinica
基金
国家自然科学基金委员会与中国工程物理研究院联合基金(No.10576013)
河南省自然科学基金(No.0611053200)
关键词
文本分割
小波变换
隐马尔科夫树模型
自适应
微分算子
尺度系数
document segmentation
wavelet transform
hidden Markov tree model
context-adapted
differential operator
scale coefficients