Handwritten Digits Recognition With Fuzzy Clustering

This article will be offline handwritten numbers as the object of study, in conclusion, in the past digital handwriting recognition on the basis of the introduction of fuzzy - means clustering (FCM), the implementation of specific methods such as offline handwritten digital identification system.In image processing, FCM analysis of the clustering algorithm can be effectively used for image processing, but in the algorithmimplemented, because it requires a lot of computing time limits its practical application.for this presented a new algorithm to overcome the FCM etc algorithm in image processing application.against the FCM convergence on the initial value, easy - to - sensitive results into local minima the shortcomings of this article addresses the two - stage fuzzy clustering algorithm solves the FCM to the initial value problem.Arbitrary handwritten numbers identify issues, which was moved on a real-time, with the rotation, the size of the invariability smart identification method.the focus of the Zernike moments and Wavelet moment.based on Zernike moments and Wavelet moment looking for efficient extraction of contours and oncontour pharyngogastric.establishing the Pan, the size of the teversal invariability a sample of the characteristics of the library, and finally improved FCM blur recognition.Tn the in-depth analysis of the characteristics, clustering and recognition of the relationship between on the basis of the establishment of a measure of ystems characteristics of standards.the fuzzy set of neamess and ambiguity related to fuzzy c - means clustering to overcome the fuzzy c - means clustering algorithm uses a lot of time, the calculation of clustering effect does not distinguish between degrees higher.the adoption of the largest correlation analysis, similar to neamess, similar to the principal component analysis and image reconstruction methods such as numerals Zernike moment of the filter selected characteristics of the maximum extent possible on behalf of the handwritten numbers and characteristics of the separation of degree, easy to fuzzy c-means clustering algorithm for classification and identification. Handwritten-numbers samples Gallery and based on the establishment of the MATLAB language handwriting recognition system.First, based on structural characteristics and statistical characteristics of numerals to identify the recognition result for standard sample library.Secondly, for the moment of recognition again to expand on the standard sample library, the resulting Samples Gallery and then add some special handwritten digital samples of usingσ- addible neamess and improvement of c - means clustering to fuzzy figures to identify the recognition result for the new standard sample library.the first two steps are required at the expense of the overall recognition rate figures to ensure that digital identification with high accuracy (= 99%), and then, on the expanded sample of the library of samples for Wavelet (nonorthogonal) (Hu) moment Zernike, transform, and to establish a pan, the size of the teversal invariability a sample of the characteristics of the library.