Robust measurement based handwriting recognition method and system

Abstract

The invention discloses a robust measurement based handwriting identification method and system. The method comprises the steps of: constructing a weighted similar map by performing similarity learning on a handwriting training sample; and keeping local features of all training samples while compacting a local intra-class divergence and separating a local inter-class divergence. In order to improve robustness of handwriting description, 1-norm measurement is proposed to be applied in a semi-supervised learning model, so as to design a performance robustness handwriting identification method and system, and output a projection matrix P that can be used for handwriting image feature extraction within a sample and outside the sample. Induction on images other than the sample comprises the steps of: projecting a test sample to the projection matrix P to input an extracted feature into an effective label propagation classifier for classification; and selecting a place of maximum probability in a corresponding category of soft label to determine a category of the test sample, so as to obtain a most accurate character recognition result. Meanwhile, by establishing a ratio model, model parameters are reduced, and the projection matrix P meets the orthogonal property.

Description

technical field [0001] The invention relates to the technical fields of computer vision and image recognition, in particular to a handwritten body recognition method and system based on robust metrics. Background technique [0002] Today is an era of information explosion, and there is a large amount of valuable multimedia high-dimensional information in our daily life. Offline handwriting recognition is an example of feature extraction and utilization of some high-dimensional information. It uses a computer to digitize paper images to obtain computer-stored character images, and then uses a series of machine learning methods to extract image features, classify and other operations to finally recognize characters. Once an efficient and accurate method for character recognition is obtained, it can be applied to fields such as office automation and machine translation, which can bring huge social and economic benefits. However, because the process of effectively extracting h...

AI Technical Summary

Problems solved by technology

These algorithms do make the results more robust, but since there are currently only unsupervised and fully supervised algorithms, which cannot make full use of labeled data and unlabeled ...

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