This code repository contains the our re-implementations of the method GCN-PN (ECCV2020).
If you want to train the model from scratch, please following these steps:
1.Firstly, prepare the dataset and datalist follows demo/reading_order_detection/datalist/DI/readme.md
2.Secondly, prepare the pretrained models from model_zoo of mmdetection:
- resnext101_64x4d-ee2c6f71.pth
3.Thirdly, direct run demo/reading_order_detection/GCN-PN/train.sh
Given the trained model, direct run demo/reading_order_detection/GCN-PN/test.sh
to test model.
For the released data is a subset, which smaller than paper reported. So the results might be slightly different from reported results. Moreover, paper takes sinkhorn method into training phase and get some improvements, but it works less in our implementation. Thus, we only release the base model.
Results on DI datasets and trained models are follows:
total_order_acc | DI_whole | DI_subset | Links |
---|---|---|---|
GCN-PN (report) | 79 | - | - |
GCN-PN | - | 72.23 | config, pth (Access Code:QDHU) |
@inproceedings{DBLP:conf/eccv/LiGBWYZ20,
author = {Liangcheng Li and
Feiyu Gao and
Jiajun Bu and
Yongpan Wang and
Zhi Yu and
Qi Zheng},
title = {An End-to-End {OCR} Text Re-organization Sequence Learning for Rich-Text
Detail Image Comprehension},
booktitle = {ECCV},
pages = {85--100},
year = {2020},
}
This project is released under the Apache 2.0 license
If there is any suggestion and problem, please feel free to contact the author with [email protected].