A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images.

6G is envisioned as the next generation of wireless communication technology, promising unprecedented data speeds, ultra-low Latency, and ubiquitous Connectivity. In tandem with these advancements, blockchain technology is leveraged to enhance computer vision applications' security, trustworthiness, and transparency. With the widespread use of mobile devices equipped with cameras, the ability to capture and recognize Chinese characters in natural scenes has become increasingly important. Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount, such as facial recognition or personal healthcare monitoring. Users can control their visual data and grant or revoke access as needed. Recognizing Chinese characters from images can provide convenience in various aspects of people's lives. However, traditional Chinese character text recognition methods often need higher accuracy, leading to recognition failures or incorrect character identification. In contrast, computer vision technologies have significantly improved image recognition accuracy. This paper proposed a Secure end-to-end recognition system (SE2ERS) for Chinese characters in natural scenes based on convolutional neural networks (CNN) using 6G technology. The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph (WHCC) of the secure data transmission in the 6G network with the blockchain model. The data transmission within the computer vision system, with a 6G gradient directional histogram (GDH), is employed for character estimation. With the deployment of WHCC and GDH in the constructed SE2ERS model, secure communication is achieved for the data transmission with the 6G network. The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication. Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88% for simple Chinese characters, compared to 81.2% with traditional methods. For complex Chinese characters, the average recognition accuracy improves to 84.4% with our system, compared to 72.8% with traditional methods. Additionally, deploying the WHCC model improves data security with the increased data encryption rate complexity of ~12 & higher than the traditional techniques. [ABSTRACT FROM AUTHOR]