ABSTRACT General Optical Character Recognition system works on the base of several successive steps such as pre-processing, segmentation, feature extraction, classification and post-processing. Feature extraction plays here a major role. In this article, we present an overview and comparison of various methods and approaches for off-line recognition of machine written Latin characters. We assume that individual characters are already segmented in an image. To recognize characters and translate them to text requires that each character must be described by a feature vector, which is then classified into one of the 36 classes corresponding to the uppercase Latin alphabet letters and numbers.

中文翻译：

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车牌字符识别特征提取与分类方法比较研究

摘要 通用光学字符识别系统基于预处理、分割、特征提取、分类和后处理等几个连续步骤工作。特征提取在这里起着重要作用。在本文中，我们对机器书写的拉丁字符离线识别的各种方法和方法进行了概述和比较。我们假设单个字符已经在图像中被分割。为了识别字符并将它们翻译成文本，需要每个字符都必须用一个特征向量来描述，然后将其分类为与大写拉丁字母和数字对应的 36 个类别之一。