Structural pattern recognition is the main method of early Chinese character recognition research. Its main starting point is the composition of Chinese characters. From the standpoint of the composition of Chinese characters, Chinese characters are composed of strokes (points and columns, etc.) and radical radicals. It can also be assumed that Chinese characters are composed of smaller structural primitives. From these structural primitives and their interrelationships, it is possible to accurately describe the Chinese characters, just as an article is composed of words, words, phrases, and sentences according to grammatical rules. So this method is also called syntactic pattern recognition. In recognition, the above structural information and syntax analysis methods are used for identification, similar to a logical reasoner.
The statistical pattern recognition of Chinese characters is to treat the character lattice as a whole, and its features are obtained through a large number of statistics on the whole. Statistical characteristics are characterized by strong anti-interference, and matching and classification algorithms are simple and easy to implement. The disadvantage is that the subdivision ability is weak, and the ability to distinguish similar words is worse. Common statistical pattern recognition methods include:
(1) Using the method of transforming features. The character image is binary transformed (such as Walsh, Hardama transform) or more complex transform (such as Karhunen-Loeve, Fourier, Cosine, Slant transform, etc.), and the dimension of the transformed feature is greatly reduced. However, these transformations are not rotation-invariant, and therefore there is a large deviation in recognition of obliquely-deformed characters. Although the calculation of the binary transformation is simple, the transformed features have no obvious physical meaning. Although the KL transform is optimal from the perspective of minimum mean square error, the amount of computation is too large to be practical. In short, the transform features have high computational complexity and certain weaknesses.
(2) Template matching. Template matching does not require a feature extraction process. The image of the character is directly used as a feature. Compared with the template in the dictionary, the template class with the highest degree of similarity is the recognition result. This method is simple and easy, and can be processed in parallel; however, a template can only recognize characters of the same size and same type of font, and there is no good adaptability to slanting and thickening of strokes.
The statistical pattern recognition of Chinese characters is to treat the character lattice as a whole, and its features are obtained through a large number of statistics on the whole. Statistical characteristics are characterized by strong anti-interference, and matching and classification algorithms are simple and easy to implement. The disadvantage is that the subdivision ability is weak, and the ability to distinguish similar words is worse. Common statistical pattern recognition methods include:
(1) Using the method of transforming features. The character image is binary transformed (such as Walsh, Hardama transform) or more complex transform (such as Karhunen-Loeve, Fourier, Cosine, Slant transform, etc.), and the dimension of the transformed feature is greatly reduced. However, these transformations are not rotation-invariant, and therefore there is a large deviation in recognition of obliquely-deformed characters. Although the calculation of the binary transformation is simple, the transformed features have no obvious physical meaning. Although the KL transform is optimal from the perspective of minimum mean square error, the amount of computation is too large to be practical. In short, the transform features have high computational complexity and certain weaknesses.
(2) Template matching. Template matching does not require a feature extraction process. The image of the character is directly used as a feature. Compared with the template in the dictionary, the template class with the highest degree of similarity is the recognition result. This method is simple and easy, and can be processed in parallel; however, a template can only recognize characters of the same size and same type of font, and there is no good adaptability to slanting and thickening of strokes.
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