Summary of Enhancing Document Ai Data Generation Through Graph-based Synthetic Layouts, by Amit Agarwal et al.
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts
by Amit Agarwal, Hitesh Patel, Priyaranjan Pattnayak, Srikant Panda, Bhargava Kumar, Tejaswini Kumar
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The development of robust Document AI models has been hindered by the lack of high-quality, labeled datasets due to data privacy concerns, scarcity, and the high cost of manual annotation. This paper proposes a novel approach to synthetic document layout generation using Graph Neural Networks (GNNs), which leverages graph-based learning to ensure structural coherence and semantic consistency. The method represents document elements as nodes in a graph and their spatial relationships as edges, allowing GNNs to generate realistic and diverse document layouts that outperform existing augmentation techniques. The proposed framework is evaluated on tasks such as document classification, named entity recognition (NER), and information extraction, demonstrating significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Document AI models are struggling because of limited access to labeled datasets. Researchers are trying to solve this problem by creating fake documents with Graph Neural Networks (GNNs). This new method helps make sure the generated documents look like real ones and are useful for training AI models. The results show that these synthetic documents can help train AI models better than traditional methods. |
Keywords
» Artificial intelligence » Classification » Named entity recognition » Ner
