Multi-feature fusion and deep learning network extraction-based handwritten digit recognition method

Abstract

The present invention discloses a multi-feature fusion and deep learning network extraction-based handwritten digit recognition method. The method includes the following steps that: the image data of handwritten digits are read, vectorization pre-processing is performed on the data; multi-feature fusion is performed on the processed data by using the principal component analysis (PCA) technique and the histogram of oriented gradients (HOG) technique, so that shallow compound features can be constructed; secondary feature extraction is performed on the data which have been subjected to multi-feature fusion through using a shallow stack auto-encoder (SAE) model, so that a deep learning network can be constructed to perform high-level and deep learning and processing on the shallow compound features; and the Softmax classifier is used to test classification effects. According to the multi-feature fusion and deep learning network extraction-based handwritten digit recognition method of the invention, the multi-feature fusion method is adopted, the PCA technique and the HOG technique are integrated, so that the shallow compound features can be constructed; the SAE model is adopted to perform secondary feature extraction, so that the deep learning network can be constructed, and simpler and more efficient feature samples can be obtained; and the Softmax classifier is used to test the classification effects; and therefore, the recognition accuracy rate of handwritten digits can be increased to 99.2%.

Description

technical field [0001] The invention relates to the technical field of handwritten digit recognition, in particular to a handwritten digit recognition method based on multi-feature fusion and deep learning network extraction. Background technique [0002] Handwritten digit recognition belongs to the category of pattern recognition and artificial intelligence in the discipline, and is a branch of optical character recognition technology. It mainly studies how to use electronic computers to automatically recognize handwritten Arabic numerals. With the rapid development of the economy, the gradually informationized society has to deal with numbers in all aspects, and a handwritten number recognition method with high accuracy is particularly important at this time. [0003] Although there are only 10 categories of handwritten digits, and its recognition has been studied for a long time, and great progress has been made, but the recognition accuracy of today's handwritten digits ...

AI Technical Summary

Problems solved by technology

[0003] Although there are only 10 categories of handwritten digits, and its recognition has been studied for a long time, an

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