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

A digital recognition and point density technology, applied in the field of electronic information, can solve the problems of high space complexity, inability to cope with large-scale data collection, large amount of calculation, etc., to reduce complexity, save computing time, and reduce requirements.

Active Publication Date: 2015-01-21
XIDIAN UNIV
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However, there are still disadvantages such as large amount of calcula...

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

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[0039] The present invention realizes the problem of recognizing handwritten digits through an unsupervised clustering method, mainly because the number of target objects is large, and the memory space required by the computer cannot meet the memory requirements of the original algorithm. The online method scans the data one by one and passes One scan completes the determination of categories, so as to realize the recognition of handwritten digits, and the realization environment is MATLAB2008b. There are many ways to realize the problem of large-scale handwritten digit recognition by the method of unsupervised clustering. The present invention adopts the clustering method based on point density weighted fuzzy C-means, and processes the data points entered sequentially in the stream data scanning mode. For the data points that do not meet the update conditions, put them into the temporary data pool pool. The specific implementation of the pool is to define a matrix with the sam...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/20G06K9/62
CPCG06V30/244G06F18/23213
Inventor 李阳阳焦李成杨果利马文萍马晶晶尚荣华侯彪杨淑媛
Owner XIDIAN UNIV
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