Abstract
This study uses deep learning theory into the character recognition technology for Shui characters in ancient books, with the objectives of overcoming the instability of the high-pixel ancient Shui characters generative model and the need for large scale handwritten text data annotation among other issues. By constructing a multilayer adversarial neural network with a Laplacian structure, a clear generative model is established for original image data of Shui characters and a stable adversarial network model with multiple mapping relationships from coarse to fine is formed. Based on the analysis of the feature distance of Shui character image samples, the minimum inter-class spacing value and the optimal number of clusters are calculated. Combined with feedback from the classifier model, the optimal number of clusters in the clustering model is adjusted, an evaluation function with information entropy adjustment and clustering threshold convergence is constructed for the unsupervised labelling of Shui character image samples. In this paper, the feedback from the convolutional neural network is used to determine the algorithmic model of the hyperparameters for clustering annotation, and this structure is also designed to improve the recognition rate of handwritten Shui characters in ancient books.
Highlights
Shui characters are a surviving hieroglyph in China, along with the Dongba characters, their inheritance relies only on oral and handwritten forms, but most Shui characters are illegible and broken
The use of advanced information processing methods such as machine learning and big data analysis to break through the traditional digital protection methods of ancient documents and to effectively solve the key problems of image clarity processing, image category labelling, and handwritten character recognition in the process of digital protection of Shui characters and ancient books, along with the promotion of the level of intelligent digital processing of ancient Chinese literature, The associate editor coordinating the review of this manuscript and approving it for publication was Honghao Gao
This paper focuses on the difficulties and problems that are encountered in the process of Shui character recognition in ancient books in China and focuses on three key technologies: super-resolution image generation, image category labelling and handwritten character recognition
Summary
Shui characters (the characters of the language of the Shui nationality) are a surviving hieroglyph in China, along with the Dongba characters (the characters of the language of the Na xi nationality), their inheritance relies only on oral and handwritten forms, but most Shui characters are illegible and broken. B. AN UNSUPERVISED IMAGE CLUSTER LABELLING ALGORITHM A clustering algorithm based on the density peak and the distance is proposed for text and image annotation of ancient Shui characters, which can effectively overcome the problem that the existing classification model requires substantial manual intervention when it labels the Shui character, this method calculates the image distance based on the Mahalanobis distance to realize the feature extraction of image samples.The relation between the hyperparameters and the clustering accuracy in the clustering algorithm, which is FIGURE 2. Aiming at overcoming the problem that is encountered in the automatic selection of hyperparameters in the cluster labelling algorithm, this paper investigates how to use the training success rate of the convolutional neural network to construct the objective function for learning the clustering hyperparameters and to improve the annotation accuracy of handwritten Shui characters. The recognition accuracy of the convolutional neural network classifier that is based on feedback optimized sample annotation is evaluated
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