Summary of Ranlaynet: a Dataset For Document Layout Detection Used For Domain Adaptation and Generalization, by Avinash Anand et al.
RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization
by Avinash Anand, Raj Jaiswal, Mohit Gupta, Siddhesh S Bangar, Pijush Bhuyan, Naman Lal, Rajeev Singh, Ritika Jha, Rajiv Ratn Shah, Shin’ichi Satoh
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper proposes a synthetic document dataset called RanLayNet, which is designed to improve the performance of deep layout identification models by providing them with robustness and adaptability to diverse document formats. The dataset includes automatically assigned labels denoting spatial positions, ranges, and types of layout elements. By training a model on this dataset, researchers can achieve enhanced performance compared to traditional methods that rely solely on annotated instances from specific domains. The authors demonstrate the effectiveness of their approach through empirical experimentation, showing that models trained on RanLayNet outperform those trained on actual documents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special set of fake documents called RanLayNet to help computers learn how to read and understand different types of documents. It’s like teaching a computer to recognize different words and phrases in many different languages, so it can be better at understanding what’s written in documents. The authors made this dataset by adding labels that tell the computer where certain parts of the document are and what they mean. They found that when they trained a model using this new dataset, it was much better than other models that only learned from one specific type of document. |
