Feature Extraction and Identification of Calligraphy Style Based on Dual Channel Convolution Network

Abstract

To improve the effect of calligraphy style feature extraction and identification, this study proposes a calligraphy style feature extraction and identification technology based on two-channel convolutional neural network and constructs an intelligent calligraphy style feature extraction and identification system. Moreover, this paper improves the C3D network model and retains 2 fully connected layers. In addition, by extracting the outline skeleton and stroke features of calligraphy characters, this paper calculates the feature weight and authenticity determination function and constructs an authenticity identification system. The experimental study shows that the calligraphy style feature extraction and identification system based on the dual-channel convolutional neural network proposed in this paper has a good performance in calligraphy style feature extraction and identification.

Retracted: Feature Extraction and Identification of Calligraphy Style Based on Dual Channel Convolution Network

“…Security and Communication Networks has retracted the article titled "Feature Extraction and Identifcation of Calligraphy Style Based on Dual Channel Convolution Network" [1] due to concerns that the peer review process has been compromised. Following an investigation conducted by the Hindawi Research Integrity team [2], signifcant concerns were identifed with the peer reviewers assigned to this article; the investigation has concluded that the peer review process was compromised.…”