摘要
该文提出了一种基于反馈的手写体字符识别方法 .该方法将人工神经网络结构及学习算法运用于系统反馈机制中 ,并从理论上证明了该学习方法是可收敛的 ,保证了算法的有效性 .同时给出反馈的可视化约束及反馈的判别准则 .试验结果证明了该方法大大降低了高噪音手写体数字的识别率 .
A new algorithm of handwritten character recognition is proposed to increase the system's accuracy without decreasing the recognition rate, which is based on the combination of the feedback concept in the cybernetic field with the BP algorithm in the ANN architecture. The proposed system is designed as an effective neural network by adding confidence back propagation and input modification (feedback) to the ANN model and the learning algorithm, so that preprocessing and recognition parts are integrated closely. The feedback signal is acquired through the difference of supposed output (predefined) and real output, and is back propagated to modify the input image signal in the feature space using the gradient descent algorithm. The modification can be considered as a step of the preprocessing phase in the whole system architecture, which can get rid of many kinds of noise showed in the experiment part of this paper. In order to get rid of the interference of the models, specified feedback network is trained for various models. During the recognition phase, only the candidate models in the predefined similarity set are processed by the feedback network to decrease the computational complexity. The final result is given by the model with minimum feedback cost. The proof of the convergence of the feedback algorithm is presented in this paper, and detailed experiments are made on sample sets with different characteristics, which showed that the error rate in such result feedback neural network architecture can be greatly reduced and the robustness to environmental noise be increased.
出处
《计算机学报》
EI
CSCD
北大核心
2002年第5期476-482,共7页
Chinese Journal of Computers
基金
国家自然科学基金 (69982 0 0 5 )
国家"九七三"重点基础研究发展规划项目(G19980 3 0 5 0 70 3 )
高等学校骨干教师资助计划资助
关键词
手写体字符识别
神经网络
学习算法
计算机
Computational complexity
Feedback control
Recurrent neural networks
Robustness (control systems)
