Abstract

The image structuring process was mainly divided into three stages: model training, model prediction, and report structuring. In the report structure stage, based on the feature annotation sequence, this paper associated the text sequence with the corresponding table structure and stored the text sequence in the corresponding database in the background. In dataset 1, the accuracy rate of removing visual information submodel was 30

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Cite this article

@article{cai-6590,
  author  = {Jing Jia and Jing Hua},
  title   = {Image Structured Annotation Based on Deep Neural Network Natural Language Processing},
  journal = {Computing and Informatics},
  volume  = {43},
  number  = {4},
  pages   = {926--943},
  year    = {2024},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2024_4_926}
}
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