光学字符识别中的表示是什么？
What is representation in optical character recognition?
我正在学习 OCR 并正在阅读这本书 https://www.amazon.com/Character-Recognition-Different-Languages-Computing/dp/3319502514
作者定义了8个过程来实现OCR，一个接一个（2接1，3接2等）：
- 光学扫描
- 位置分割
- 预处理
- 分段
- 代表
- 特征提取
- 认可
- Post-处理中
这就是他们写的关于表示的内容 (#5)
The fifth OCR component is representation. The image representation
plays one of the most important roles in any recognition system. In
the simplest case, gray level or binary images are fed to a
recognizer. However, in most of the recognition systems in order to
avoid extra complexity and to increase the accuracy of the algorithms,
a more compact and characteristic representation is required. For this
purpose, a set of features is extracted for each class that helps
distinguish it from other classes while remaining invariant to
characteristic differences within the class.The character image
representation methods are generally categorized into three major
groups: (a) global transformation and series expansion (b) statistical
representation and (c) geometrical and topological representation.
这是他们写的关于特征提取的内容 (#6)
The sixth OCR component is feature extraction. The objective of
feature extraction is to capture essential characteristics of symbols.
Feature extraction is accepted as one of the most difficult problems
of pattern recognition. The most straight forward way of describing
character is by actual raster image. Another approach is to extract
certain features that characterize symbols but leaves the unimportant
attributes. The techniques for extraction of such features are divided
into three groups’ viz. (a) distribution of points (b) transformations
and series expansions and (c) structural analysis.
我完全糊涂了。我不明白什么是代表。据我了解，分割后我们必须从图像中提取一些特征，例如像弗里曼链码这样的拓扑结构，并且必须与在学习阶段模型中保存的一些特征相匹配——即进行识别。换句话说——分割——特征提取——识别。我不明白在代表阶段必须做什么。请解释一下。
表示组件采用分割产生的光栅图像并将其转换为更简单的格式（"representation"），保留 类 的特征属性。这是为了降低后面识别过程的复杂度。您提到的弗里曼链码就是这样一种表示。
一些（大多数？）作者将表示和特征提取合并为一个步骤，但您的书的作者选择将它们分开处理。更改表示形式不是强制性的，但这样做可以降低训练和识别步骤的复杂性，从而提高准确性。
正是从这个更简单的表示中，在特征提取步骤中提取了特征。提取哪些特征将取决于所选择的表示。这篇论文 - Feature Extraction Methods for Character Recognition - A Survey - 描述了 11 种不同的特征提取方法，这些方法可以应用于 4 种不同的表示。
提取的特征是传递给训练器或识别器的特征。
我正在学习 OCR 并正在阅读这本书 https://www.amazon.com/Character-Recognition-Different-Languages-Computing/dp/3319502514
作者定义了8个过程来实现OCR，一个接一个（2接1，3接2等）：
- 光学扫描
- 位置分割
- 预处理
- 分段
- 代表
- 特征提取
- 认可
- Post-处理中
这就是他们写的关于表示的内容 (#5)
The fifth OCR component is representation. The image representation plays one of the most important roles in any recognition system. In the simplest case, gray level or binary images are fed to a recognizer. However, in most of the recognition systems in order to avoid extra complexity and to increase the accuracy of the algorithms, a more compact and characteristic representation is required. For this purpose, a set of features is extracted for each class that helps distinguish it from other classes while remaining invariant to characteristic differences within the class.The character image representation methods are generally categorized into three major groups: (a) global transformation and series expansion (b) statistical representation and (c) geometrical and topological representation.
这是他们写的关于特征提取的内容 (#6)
The sixth OCR component is feature extraction. The objective of feature extraction is to capture essential characteristics of symbols. Feature extraction is accepted as one of the most difficult problems of pattern recognition. The most straight forward way of describing character is by actual raster image. Another approach is to extract certain features that characterize symbols but leaves the unimportant attributes. The techniques for extraction of such features are divided into three groups’ viz. (a) distribution of points (b) transformations and series expansions and (c) structural analysis.
我完全糊涂了。我不明白什么是代表。据我了解，分割后我们必须从图像中提取一些特征，例如像弗里曼链码这样的拓扑结构，并且必须与在学习阶段模型中保存的一些特征相匹配——即进行识别。换句话说——分割——特征提取——识别。我不明白在代表阶段必须做什么。请解释一下。
表示组件采用分割产生的光栅图像并将其转换为更简单的格式（"representation"），保留 类 的特征属性。这是为了降低后面识别过程的复杂度。您提到的弗里曼链码就是这样一种表示。
一些（大多数？）作者将表示和特征提取合并为一个步骤，但您的书的作者选择将它们分开处理。更改表示形式不是强制性的，但这样做可以降低训练和识别步骤的复杂性，从而提高准确性。
正是从这个更简单的表示中，在特征提取步骤中提取了特征。提取哪些特征将取决于所选择的表示。这篇论文 - Feature Extraction Methods for Character Recognition - A Survey - 描述了 11 种不同的特征提取方法，这些方法可以应用于 4 种不同的表示。
提取的特征是传递给训练器或识别器的特征。