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Online study system null for the character recognition system which is based on the neural

机译:基于神经网络的字符识别系统在线学习系统null

摘要

A neural network based improving the performance of an omni-font classifier by using recognized characters for additional training is presented. The invention applies the outputs of the hidden layer nodes of the neural net as the feature vector. Characters that are recognized with high confidence are used to dynamically train a secondary classifier. After the secondary classifier is trained, it is combined with the original main classifier. The invention can re-adjust the partition or boundary of feature space, based on on-line learning, by utilizing the secondary classifier data to form an alternative partition location. The new partition can be referred to when a character conflict exists during character recognition.
机译:提出了一种基于神经网络的神经网络,该网络通过使用识别的字符进行额外的训练来改善全字体分类器的性能。本发明将神经网络的隐藏层节点的输出用作特征向量。高可信度识别的字符用于动态训练辅助分类器。二级分类器经过训练后,将与原始主分类器组合在一起。本发明可以基于在线学习,通过利用次级分类器数据形成替代的分区位置,来重新调整特征空间的分区或边界。在字符识别期间存在字符冲突时,可以引用新分区。

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