A Handwriting Recognition Method and System Based on Robust Metrics

Abstract

The invention discloses a method and system for handwriting recognition based on robust metrics. Through similarity learning of handwriting training samples, a weighted similarity graph is constructed, and all the components are kept while compacting local intra-class divergence and separating local inter-class divergence. Local properties of the training samples. In order to improve the robustness of handwriting description, the 1‑norm metric is proposed to be applied to the semi-supervised feature learning model, and a robust handwriting recognition method and system are designed, which can be used for in-sample and out-of-sample handwriting image feature extraction. Projection matrix P. The induction of the out-of-sample image projects the test sample to the projection matrix P, and then inputs the extracted features into an efficient label propagation classifier for classification, and takes the position of the maximum probability in the soft label of the corresponding category to determine the test sample categories to get the most accurate character recognition results. At the same time, by establishing a ratio model, the model parameters are reduced, and the projection matrix P satisfies the orthogonality 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 data information, there is still a lot of room for improvement in the accuracy of the results

In addition, some empirical parameters in the algorithm are also very difficult to optimally determine

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