基于神经网络信息融合的印刷体字符识别研究

针对印刷体字符识别,提出一种基于神经网络信息融合的方法.在对待识别目标提取特征后,分别采用2种反向传播算法的改进算法和遗传算法构造神经网络分类器模型,并进行网络的训练和识别工作.通过实验数据着重分析和比较了3种算法的特点,将此3种分类器得出的分类结果进行决策级的信息融合,最终得出识别结果.实验结果表明,此方法简单可行,具有较高的鲁棒性和识别率.

Research of printed character recognition based on neural network information fusion

Considering the status of the printed character recognition, this paper proposed a new approach which based on the neural network information fusion. After extracting the features of the target, using two back-propagation algorithm and genetic algo-rithms to build the neural network models to train and recognize the targets. Analyzes and compares the characteristic of the three al-gorithms through experiment data, take identification results from the three classifiers to the decision-making level information fusion and get the final results. The experiments results show that this method is simple and feasible with high robustness and recognition rate.