Intelligent Recognition Algorithm for Calligraphy Fonts Based on Texture Mapping

From the visual sense, the visual feeling of each font is different, so the different writing methods of each font can be expressed by a texture feature. Therefore, the method of texture mapping can be applied to the study of calligraphy font recognition in this article. Because Convolutional Neural Network (CNN) can extract the deep features of characters in the process of font recognition, reduce the amount of calculation, and effectively solve the unique characteristics of calligraphy fonts, this article can effectively recognize characters by CNN. In this article, the intelligent recognition algorithm of calligraphy font based on texture mapping and CAD is studied, and the rapid recognition model of calligraphy font features is constructed by CNN, and the extraction method of this feature parameter is improved. The experimental results based on MNIST data set show that the recognition accuracy of the improved calligraphy font recognition method based on deep learning (DL) proposed in this article reaches 98.6% in the test set, which effectively improves the recognition accuracy of the original method. Applying CNN model based on texture mapping and CAD to font recognition can not only solve the problem of fast recognition of calligraphy fonts, but also broaden the application field of neural network. © 2024 CAD Solutions, LLC.