method: GraphDoc+Classify+Merge2023-05-24
Authors: Yan Wang, Jiefeng Ma, Zhenrong Zhang, Pengfei Hu, Jianshu Zhang, Jun Du
Affiliation: University of Science and Technology of China (USTC), iFLYTEK AI Research
Description: We pre-trained several GraphDoc models on provided unlabelled documents under different configurations. We then fine-tuned the models on the training set for 200-500 epochs. After classifying OCR boxes into various categories, we proposed a Merger module to handle the aggregation process.
We also used some pre/post-processing according to the text content and distances between OCR boxes. Finally, we adopted model ensembling to further enhance the system performance.
method: baseline - RoBERTa-base with synthetic pre-training2023-05-02
Authors: Organizers
Affiliation: Rossum.ai, Czech Technical University in Prague, University of La Rochelle
Description: Baseline method. Uses multi-label NER formulation with RoBERTa base as the backbone. It is pre-trained on the synthetic part of the DocILE dataset.
method: baseline - RoBERTa-base2023-05-02
Authors: Organizers
Affiliation: Rossum.ai, Czech Technical University in Prague, University of La Rochelle
Description: Baseline method. Uses multi-label NER formulation with RoBERTa base as the backbone.
| Date | Method | AP | F1 | Precision | Recall | |||
|---|---|---|---|---|---|---|---|---|
| 2023-05-24 | GraphDoc+Classify+Merge | 65.93% | 71.17% | 70.00% | 72.38% | |||
| 2023-05-02 | baseline - RoBERTa-base with synthetic pre-training | 49.93% | 63.60% | 64.04% | 63.18% | |||
| 2023-05-02 | baseline - RoBERTa-base | 48.28% | 61.97% | 62.07% | 61.87% | |||
| 2023-05-02 | baseline - LayoutLMv3 with unsupervised and synthetic pre-training | 46.50% | 61.39% | 62.47% | 60.35% | |||
| 2023-05-02 | baseline - LayoutLMv3 with unsupervised pre-training | 45.29% | 59.99% | 60.58% | 59.41% |