通过对神经网络集成的理论分析，提出了一种多级神经网络结构的手写体汉字识别模型。
By analyzing the theory of neural network integration, I developed a multi-level neural network model for recognition handwritten Chinese characters.
在手写汉字识别的研究中，鲜有研究者提出建立手写汉字的数学模型，本文在这方面作了一些探讨。
In the research of handwriting Chinese character recognition, no researcher raised a mathematical model of handwriting Chinese characters which is discussed in this paper.
文章提出了一种基于多重隐马尔可夫模型和区域投影变换的手写体汉字识别新方法。
A new approach for handwritten Chinese character recognition based on multiple hidden Markov model classifiers and sub-region projection transform is proposed in the thesis.
本文提出一种联机识别自然手写体汉字的多分类器集成模型。
In the paper, a new multiple classifiers integrated model of online recognizing natural handwritten Chinese character is presented.
本文描述了采用这种网络模型实现的印刷体汉字识别系统。
The paper describes a printed Chinese character recognition system by such network.
手写体汉字识别系统提取反映待识别手写体汉字本身的定性与定量相结合的各种全局特征信息构成该对象的多模式模态特征模型。
The system extracts kinds of global features to constitute the object's multi-modal feature model which integrates handwritten Chinese characters 'quantitative and qualitative features.
手写体汉字识别系统提取反映待识别手写体汉字本身的定性与定量相结合的各种全局特征信息构成该对象的多模式模态特征模型。
The system extracts kinds of global features to constitute the object's multi-modal feature model which integrates handwritten Chinese characters 'quantitative and qualitative features.
应用推荐