- Task 1 - E2E Complex Entity Linking
- Task 2 - E2E Complex Entity Labeling
- Task 3 - E2E Zero-shot Structured Text Extraction
- Task 4 - E2E Few-shot Structured Text Extraction
method: LayoutLMv3&StrucText2023-03-24
Authors: Mei Jiang(姜媚),Minhui Wu(伍敏慧),Chen Li(李琛),Jing Lv(吕静),Haoxi Li(李昊曦),Lifu Wang(王立夫),Sicong Liu(刘思聪)
Affiliation: TencentOCR
Description: Based on a large pretrained model and LayoutLMv3 and StrucText architecture, with some pre/post processing methods.
method: LayoutLMv3&StrucText2023-03-24
Authors: Mei Jiang(姜媚),Minhui Wu(伍敏慧),Chen Li(李琛),Jing Lv(吕静),Haoxi Li(李昊曦),Lifu Wang(王立夫),Sicong Liu(刘思聪)
Affiliation: TencentOCR
Description: Based on a large pretrained model and LayoutLMv3 and StrucText architecture, with some pre/post processing methods.
method: sample-12023-03-25
Authors: Zhenrong Zhang, Lei Jiang, Youhui Guo, Jianshu Zhang, Jun Du
Affiliation: University of Science and Technology of China (USTC), iFLYTEK AI Research
Email: zzr666@mail.ustc.edu.cn
Description: 1. We use the UniLM[2] and LiLT[3] as decoder to utilize text and layout information, OCR results with manual-rule sorting are fed into decoder to predict target.
2. We assemble DocPrompt[1], UniLM[2] and LiLT[3].
[1] https://github.com/PaddlePaddle/PaddleNLP/blob/develop/model_zoo/ernie-layout/README_ch.md [2] https://github.com/microsoft/unilm/blob/master/s2s-ft/
Date | Method | score | score1 | score2 | |||
---|---|---|---|---|---|---|---|
2023-03-24 | LayoutLMv3&StrucText | 84.43% | 87.14% | 73.59% | |||
2023-03-24 | LayoutLMv3&StrucText | 82.51% | 85.15% | 71.93% | |||
2023-03-25 | sample-1 | 82.13% | 85.24% | 69.68% | |||
2023-03-23 | LayoutLMv3&StrucText | 81.88% | 84.60% | 70.96% | |||
2023-03-24 | task4-base | 74.90% | 78.57% | 60.21% | |||
2023-03-24 | Fewshot-brain_v1 | 74.39% | 77.81% | 60.71% | |||
2023-03-24 | Dao Xianghu light of TianQuan | 68.19% | 71.48% | 55.03% | |||
2023-03-25 | GRGBanking | 43.52% | 45.44% | 35.83% | |||
2023-03-25 | Task4_gpt | 0.00% | 0.00% | 0.00% |