Handwritten digit recognition method and system

Abstract

An embodiment of the invention discloses a handwritten digit recognition method and system. During dimensionality reduction of handwritten digits, all image data are represented linearly through K neighbors, weighting coefficients of all image data when the image data are represented linearly through the K neighbors are obtained through an orthogonal matching algorithm, besides, weighting coefficient matrixes are established to perform dimensionality reduction on training image data, and dimensionality reduction is performed on images to be recognized through weighting coefficient vectors and vector data of the K neighbors of the images after dimensionality reduction. According to experiments, by the aid of the handwritten digit recognition method, the recognition rate of handwritten digit recognition is improved.

Description

Technical field [0001] The invention relates to the field of pattern recognition, and more specifically, to a handwritten number recognition method and system. Background technique [0002] With the rapid development of computer technology and digital image processing technology, handwritten number recognition has been practically applied in e-commerce, automatic machine input and other occasions. [0003] However, handwritten digits are high-dimensional data. If they are directly recognized, it will not only take a long time, but also have a large computational complexity. Therefore, handwritten digits are often recognized after dimensionality reduction. Based on this, saul et al. Digital recognition method based on local linear embedding. However, this method will use the least squares method to solve the local linear representation coefficients of the data, which involves finding the inverse of the matrix. If the matrix is ​​a singular matrix and there is no inverse matrix for ...

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Problems solved by technology

[0003] However, handwritten digits are high-dimensional data. If it is directly recognized, it will not only take a long time, but also have a large computational complexity. Therefore, handwritten digits are often recognized after dimensionality reduction. Based on this, Saul et al. proposed a method based on linear Digital Recognition Method Based on Localized Linear Embedding

However, this method will use the least squares method to solve the local linear representation coefficient of the data, which involves finding the inverse of the matrix. If the matrix is ​​a singular matrix, and the singular matrix does not have an inverse matrix, then the least squares method is used to solve the data. There will be no solution when the coefficient is represented locally linearly, which makes the recognition rate of handwritten digit recognition low

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