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Article summary:
1. This paper presents CascadeTabNet, a deep learning-based end-to-end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model.
2. The proposed model achieved 3rd rank in the International Conference on Document Analysis and Recognition (ICDAR) 2019 post-competition results for table detection, while attaining the best accuracy results for the 2013 and TableBank datasets.
3. Code and dataset have been made available online to enable CNNs to achieve very accurate table detection results.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence for its claims through experiments conducted on public datasets such as the International Conference on Document Analysis and Recognition (ICDAR) 2013, 2019, and TableBank datasets. The authors also provide code and dataset online to enable CNNs to achieve very accurate table detection results.
However, there are some potential biases that should be noted in this article. For example, the authors do not explore any counterarguments or present any alternative approaches to their proposed method. Additionally, they do not discuss any possible risks associated with their approach or consider any other points of view that may be relevant to their research topic. Furthermore, they do not provide any evidence for their claims beyond the experiments conducted on public datasets which could limit the generalizability of their findings.
In conclusion, while this article is generally reliable and trustworthy due to its evidence-based claims, there are some potential biases that should be taken into consideration when evaluating its trustworthiness and reliability.