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Madaline Neural Network for character recognition. Designing a Madaline (Multiple Adaline) Neural Network to recognize 3 characters 0, C and F supplied in a binary format and represented using a 7x7 grid. The Neural Network should be trained and test
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提出了一种复杂背景下的多车牌图像分割和识别方法，首先采用统计和特征匹配相结合的方法进行背景提取，将可能存在车辆的区域提取出来；然后分别对可能的车辆区域进行局部边缘检测，并使用车牌的先验知识确定车牌的位置和单个字符分割，包括车牌倾斜时的字符分割；最后使用PCA和神经网络相结合的方法精确识别车牌。-Proposed a multi-plate image segmentation and recognition method under a complex background, the first
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这是利用神经网络来实现手写字符识别，起准确率已经达到99.26 ，可以继续调整参数达到更深层次的效果。需要自己搭建opencv环境。后期工作可以利用cuda对其更深层次的加速-This is achieved using a neural network handwritten character recognition, since the exact rate has reached 99.26 percent, can continue to adjust the parameters t
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车牌识别算法，采用BP神经网络的方法进行车牌字符识别，并能把汉字识别出来。-License plate recognition algorithm, using the BP neural network method for license plate character recognition, and to recognize Chinese characters.
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使用BP神经网络实现手写字符库MNIST的识别。(The recognition of handwritten character library MNIST is realized by using BP neural network.)
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用matlab实现车牌试别，包括定位、分割和字符识别，识别方法是神经网络。(Using MATLAB to realize license plate classification, including location, segmentation and character recognition. The recognition method is neural network.)
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