DewarpNet

This repository contains the codes for DewarpNet training.

Recent Updates

• [May, 2020] Added evaluation images and an important note about Matlab SSIM.
• [Dec, 2020] Added OCR evaluation details.
• [Sep, 2021] Released DewarpNet final models used in the paper.

Training

• Prepare Data: train.txt & val.txt. Contents should be like:

1/824_8-cp_Page_0503-7Ns0001
1/824_1-cp_Page_0504-2Cw0001

• Train Shape Network: python trainwc.py --arch unetnc --data_path ./data/DewarpNet/doc3d/ --batch_size 50 --tboard
• Train Texture Mapping Network: python trainbm.py --arch dnetccnl --img_rows 128 --img_cols 128 --img_norm --n_epoch 250 --batch_size 50 --l_rate 0.0001 --tboard --data_path ./DewarpNet/doc3d

Inference:

• Run: python infer.py --wc_model_path ./eval/models/unetnc_doc3d.pkl --bm_model_path ./eval/models/dnetccnl_doc3d.pkl --show

Evaluation (Image Metrics):

• We use the same evaluation code as DocUNet. To reproduce the quantitative results reported in the paper use the images available here.

• [Important note about Matlab version] We noticed that Matlab 2020a uses a different SSIM implementation which gives a better MS-SSIM score (0.5623). Whereas we have used Matlab 2018b. Please compare the scores according to your Matlab version.

Evaluation (OCR Metrics):

• The 25 images used for OCR evaluation is /eval/ocr_eval/ocr_files.txt
• The corresponding ground-truth text is given in /eval/ocr_eval/tess_gt.json
• For the OCR errors reported in the paper we had used cv2.blur as pre-processing which gives higher error in all the cases. For convenience, we provide the updated numbers (without using blur) in the following table:

Method	ED	CER	ED (no blur)	CER (no blur)
DocUNet	1975.86	0.4656(0.263)	1671.80	0.403 (0.256)
DocUNet on Doc3D	1684.34	0.3955 (0.272)	1296.00	0.294 (0.235)
DewarpNet	1288.60	0.3136 (0.248)	1007.28	0.249 (0.236)
DewarpNet (ref)	1114.40	0.2692 (0.234)	812.48	0.204 (0.228)

• We had used the Tesseract (v4.1.0) default configuration for evaluation with PyTesseract (v0.2.6).

Models:

• Pre-trained models are available here. These models are captured prior to end-to-end training, thus won't give you the end-to-end results reported in Table 2 of the paper. Use the images provided above to get the exact numbers as Table 2.
• Final models are available here. These models can be used to unwarp DocUNet images and reproduce the results in the ICCV paper.

Dataset:

• The doc3D dataset can be downloaded using the scripts here.

More Stuff:

• Demo
• Project Page
• Doc3D Rendering Codes

Citation:

If you use the dataset or this code, please consider citing our work-

@inproceedings{SagnikKeICCV2019, 
Author = {Sagnik Das*, Ke Ma*, Zhixin Shu, Dimitris Samaras, Roy Shilkrot}, 
Booktitle = {Proceedings of International Conference on Computer Vision}, 
Title = {DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks}, 
Year = {2019}}   

Acknowledgements:

• These codes are heavily structured on pytorch-semseg.