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Article analysis:

The article is generally reliable and trustworthy, as it provides a comprehensive overview of the research and application of instrument reading recognition algorithms based on deep learning. It cites relevant sources to back up its claims, such as Sablatnig et al., Alegria et al., Kucherukv et al., Xu Peng et al., She Shizhou et al., Xing Haoqiang et al., Zhang Haibo's team from Shandong Normal University, and Cai Mengqian et al. Furthermore, the article does not appear to be biased or one-sided in its reporting; rather, it presents both sides equally by discussing both foreign and domestic research teams' contributions to this field. Additionally, the article does not appear to contain any promotional content or partiality towards any particular group or individual.

However, there are some points that could be improved upon in terms of trustworthiness and reliability. For example, while the article mentions potential risks associated with instrument reading recognition algorithms based on deep learning (such as environmental factors affecting color features), it does not provide any evidence for these claims or explore counterarguments that may exist regarding these risks. Additionally, while the article discusses possible applications for this technology (e.g., power station maintenance), it does not provide any details about how these applications would work in practice or what potential benefits they could bring about. Finally, while the article mentions that manual intervention is necessary for calibration of dial images, it does not explain why this is necessary or how it affects the accuracy of readings taken from instruments using this technology.