Handwritten numeral recognition method based on point density weighting online FCM clustering

Abstract

The invention discloses a handwritten numeral recognition method based on point density weighting online FCM clustering. The method is used for processing the large-scale offline handwritten numeral recognition problem. The method includes the steps that (1), all handwritten numeral image sets are preprocessed; (2), clustering centers are initialized, and data points are made to sequentially enter processing procedures; (3), the membership degree of the current data point and all the clustering centers is calculated; (4), if the membership degree reaches a threshold value, the position of the nearest clustering center is updated; (5), if the membership degree does not reach the threshold value, the current data point is not processed and is temporarily placed in a to-be-processed region; (6), when the to-be-processed region reaches certain standards, data in the to-be-processed region are clustered through a point density weighting FCM algorithm, and the clustering centers are updated; (7), circulation continues until all the data points are processed; (8), the membership degrees of all the data points are calculated through acquired clustering center blocks, the data points are divided into different classes, and data classification is finished through scanning at a time. According to the method, the space complexity and the time complexity can be lowered from the aspect of processing the large-scale handwritten numeral recognition problem.

Description

technical field [0001] The invention belongs to the technical field of electronic information, and relates to handwritten digit recognition based on point density weighted online fuzzy C-means (Weighted-FCM) clustering, in particular to a handwritten digit recognition method based on point density weighted online FCM clustering. Background technique [0002] Handwritten digit recognition is a branch of optical character recognition technology. It mainly studies how to automatically recognize Arabic numerals written by people on paper and other media by computer. This technology has broad application prospects in many fields such as banking, finance, and self-identification of zip codes. In addition, handwritten digit recognition, as an important problem in the field of pattern recognition, also has important theoretical value. Today, this technology has become a research hotspot in the field of image processing and pattern recognition. There are generally two types of handw...

AI Technical Summary

Problems solved by technology

However, there are still disadvantages such as large amount of calculation and high space complexity, so that it cannot cope with larger data sets.

Images