Place of feature extraction in optical character recognition

[PashaTurkish]

Hi all

My question is closer to OCR theory then to tesseract-ocr but I post it here because it anyway is related with ocr and ocr software.

I am learning OCR and reading this book https://www.amazon.com/Character-Recognition-Different-Languages-Computing/dp/3319502514

The authors define 8 processes to implement OCR that follow one by one (2 after 1, 3 after 2 etc):

1. Optical scanning
2. Location segmentation
3. Pre-processing
4. Segmentation
5. Representation
6. Feature extraction
7. Recognition
8. Post-processing

This is what they write about representation (#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.

This is what they write about feature extraction (#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.

Please, explain, why feature extraction is after representation, but not before it. As I understand at representation we get from image (!) certain model of character, so after that we must match this model to certain class. I don't understand what we do at feature extraction. Or I understand everything wrong. Please, help.

The question was also asked on SO https://stackoverflow.com/questions/44396721/place-of-feature-extraction-in-optical-character-recognition

Best regards, Pavel