An ancient font classification method based on a convolutional neural network

Abstract

The invention discloses an ancient font classification method based on a convolutional neural network. According to the method, firstly, an ancient font category image data set is crawled by using a crawler technology; through data expansion, training set samples tend to be balanced; graying processing is carried out on the balanced training set sample and setting an image size to a target image size; histogram equalization processing is performed on the sample set, isolated noise points are removed in the image through an N8 connected noise reduction algorithm, and finally binarization processing is performed on the image based on a fuzzy set theory and by using a Shannon entropy function, so that detail features of the image are well reserved; based on the objective function of the classification task. The center loss function and the traditional cross entropy loss function are matched for use. The inter-class distance is increased. The intra-class distance is reduced. The distinguishing capability of features is improved to a certain extent, preprocessed images are trained through a pre-defined network model, and the accuracy of a classification result is evaluated through a confusion matrix. According to the method. The preprocessing effect on the degraded ancient font image is remarkable, and a more accurate ancient font classification effect is achieved by optimizing parameter setting and utilizing appropriate training skills to train the convolutional neural network model.

Description

technical field [0001] The invention relates to the field of image processing of traditional Chinese characters, in particular to a method for classifying ancient font calligraphy based on a convolutional neural network. Background technique [0002] Chinese characters, as traditional Chinese characters, have a history of thousands of years. At the same time, Chinese characters are also an important part of Chinese traditional art and culture. However, time has caused weathering and damage to old calligraphic works, so it is necessary to utilize advanced technology to protect these works. We will provide a preprocessing (denoising) algorithm for ancient Chinese calligraphy works, and on this basis, use a convolutional neural network to classify the data set to achieve a better classification accuracy. Most ancient fonts (traditional Chinese calligraphy) are written with traditional Chinese brushes. The handwriting in these traditional brushes is much thicker than that of ha...

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Problems solved by technology

[0005] The present invention solves the font style classification problem by applying the convolutiona

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