针对传统马尔科夫特征计算差值矩阵的方式单一、拼接检测鲁棒性不强的问题,提出彩色多残差马尔科夫特征拼接检测模型。该模型引入隐写检测模型(Rich Models for Steganalysis,SRM)中的多种残差类型来改进传统马尔科夫特征,从R,G,B 3个...针对传统马尔科夫特征计算差值矩阵的方式单一、拼接检测鲁棒性不强的问题,提出彩色多残差马尔科夫特征拼接检测模型。该模型引入隐写检测模型(Rich Models for Steganalysis,SRM)中的多种残差类型来改进传统马尔科夫特征,从R,G,B 3个通道分别提取10种不同类型的马尔科夫特征,训练30个独立的SVM分类器,最后通过决策判断进行分类预测。该方法在哥伦比亚大学彩色拼接检测库上达到了95.40%的准确率。展开更多
Unconstrained offiine handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document im...Unconstrained offiine handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons （MLPs） in either a hybrid or a tandem fashion into hidden Markov models （HMMs）. However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offiine handwriting recognition. In the proposed model, deep belief networks arc adopted to learn the compact representations of sequential data, while HMMs are applied for （sub-）word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari （all Indian script）. Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs taudem approaches.展开更多
文摘针对传统马尔科夫特征计算差值矩阵的方式单一、拼接检测鲁棒性不强的问题,提出彩色多残差马尔科夫特征拼接检测模型。该模型引入隐写检测模型(Rich Models for Steganalysis,SRM)中的多种残差类型来改进传统马尔科夫特征,从R,G,B 3个通道分别提取10种不同类型的马尔科夫特征,训练30个独立的SVM分类器,最后通过决策判断进行分类预测。该方法在哥伦比亚大学彩色拼接检测库上达到了95.40%的准确率。
基金the National Natural Science Foundation of China （No. 61403353）
文摘Unconstrained offiine handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons （MLPs） in either a hybrid or a tandem fashion into hidden Markov models （HMMs）. However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offiine handwriting recognition. In the proposed model, deep belief networks arc adopted to learn the compact representations of sequential data, while HMMs are applied for （sub-）word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari （all Indian script）. Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs taudem approaches.
