含源码和数据集，搭建SVM和CNN模型进行训练测试，准确率0.99，欢迎入门者参考学习

介绍了车牌图像的预处理方法，包括图像的灰度化处理，中值滤波，基于直方图均衡化的光影处理，图像增强处理以及图像锐化等多个方面。最后介绍了基于颜色模型的车牌定位算法，并结合图像校正计算获得了标准的车牌区域。通过一种改进的基于形态学轮廓特征和垂直映射的字符切割算法对提取的车牌图像进行字符分割，然后对分割后单个字符提取HOG特征，最后通过GRNN神经网络根据字符的HOG特征进行车牌识别。MATLAB仿真结果表明，本文所研究的车牌识别方法具有较高的识别正确率，其正确率可以达到90%以上。

以上帝的名义 LeNet-5 该存储库包含Tensorflow的 （手写字符识别）的实现，以及使用和进行测试的网络。 训练mnist数据集 要使用mnist数据集训练网络，请在命令提示符下键入以下命令： python3 ./train_mnist.py mnist数据集的样本图像： 结果 时代0 Train Accuracy = 0.121 Test Accuracy = 0.121 Validation Accuracy = 0.128 纪元1 Train Accuracy = 0.963 Test Accuracy = 0.966 Validation Accuracy = 0.964 纪元2 Train Accuracy = 0.976 Test Accuracy = 0.976 Validation Accuracy = 0.977 ... 纪元50 Trai

基于MATLAB发票动车票识别。可以识别的士发票的编号，金额，日期等。算法原理是模板匹配。步骤：预处理，定位，字符分割，字符识别。

卷积神经网络在字符识别方面的应用.doc

Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps using a sliding window-based method, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MC-FCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.10% and 97.15%, respectively, which are significantly better than the best result reported thus far in the literature.

matlab手写汉字，字符，数字，字母识别，神经网络bp

光学字符识别（OCR）的目的是对字母数字或其他字符对应的光学模式（通常包含在数字图像中）进行分类。OCR的过程包括几个步骤，包括分割、特征提取和分类。该程序使用图像处理工具箱来获取。

通用机打发票的识别程序，包括图像的预处理（拉伸、降噪、ROI提取），神经网络的训练与识别

光学字符识别 使用Pytorch特征提取的光学字符识别。