method: GraphDoc+Classify+Merge2023-05-25
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 500-1000 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 | F1 | AP | Precision | Recall | |||
---|---|---|---|---|---|---|---|---|
2023-05-25 | GraphDoc+Classify+Merge | 65.93% | 44.45% | 76.43% | 57.97% | |||
2023-05-02 | baseline - RoBERTa-base with synthetic pre-training | 63.12% | 54.94% | 60.89% | 65.52% | |||
2023-05-02 | baseline - RoBERTa-base | 59.76% | 50.36% | 56.85% | 62.99% | |||
2023-05-02 | baseline - LayoutLMv3 with unsupervised and synthetic pre-training | 58.58% | 48.89% | 57.97% | 59.19% | |||
2023-05-02 | baseline - LayoutLMv3 with unsupervised pre-training | 54.18% | 37.51% | 54.89% | 53.48% | |||
2023-05-24 | YOLOv8X+Grid | 40.19% | 19.61% | 51.41% | 32.99% |