基于人脸识别的身份证件验证系统.zip

:boom: **Good news! Our new work [DocTr++: Deep Unrestricted Document Image Rectification](https://github.com/fh2019ustc/DocTr-Plus) comes out, capable of rectifying various distorted document images in the wild.** :boom: **Good news! Our new work exhibits state-of-the-art performances on the [DocUNet Benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html) dataset: [DocScanner: Robust Document Image Rectification with Progressive Learning](https://drive.google.com/file/d/1mmCUj90rHyuO1SmpLt361youh-07Y0sD/view?usp=share_link)** with [Repo](https://github.com/fh2019ustc/DocScanner). :boom: **Good news! A comprehensive list of [Awesome Document Image Rectification](https://github.com/fh2019ustc/Awesome-Document-Image-Rectification) methods is available.** # DocTr    > [DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction](https://arxiv.org/pdf/2110.12942v2.pdf) > ACM MM 2021 Oral Any questions or discussions are welcomed! ## Training DocTr consists of two main components: a geometric unwarping transformer (GeoTr) and an illumination correction transformer (IllTr). - For geometric unwarping, we train the GeoTr network using the [Doc3D](https://github.com/fh2019ustc/doc3D-dataset) and [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/) dataset. - For illumination correction, we train the IllTr network based on the [DocProj](https://github.com/xiaoyu258/DocProj) dataset. ## Inference 1. Download the pretrained models from [Google Drive](https://drive.google.com/drive/folders/1eZRxnRVpf5iy3VJakJNTKWw5Zk9g-F_0?usp=sharing) or [Baidu Cloud](https://pan.baidu.com/s/1Cq9bfyAJ9MWwxj0CarqmKw?pwd=jmy1), and put them to `$ROOT/model_pretrained/`. 2. Put the distorted images in `$ROOT/distorted/`. 3. Geometric unwarping. The rectified images are saved in `$ROOT/geo_rec/` by default. ``` python inference.py ``` 4. Geometric unwarping and illumination rectification. The rectified images are saved in `$ROOT/ill_rec/` by default. ``` python inference.py --ill_rec True ``` ## Evaluation - ***Important.*** In the [DocUNet Benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html), the '64_1.png' and '64_2.png' distorted images are rotated by 180 degrees, which do not match the GT documents. It is ingored by most of existing works. Before the evaluation, please make a check. - Note that the performances in our MM paper are computed with the two ***mistaken*** samples in [DocUNet Benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html). For reproducing the following quantitative performance on the ***corrected*** [DocUNet Benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html), please use the geometric rectified images available from [Google Drive](https://drive.google.com/drive/folders/1kJ34Nk18RVPwYK8mdfcQvU_67whD9tMe?usp=sharing) or [Baidu Cloud](https://pan.baidu.com/s/1Cq9bfyAJ9MWwxj0CarqmKw?pwd=jmy1). For the ***corrected*** performance of [other methods](https://github.com/fh2019ustc/Awesome-Document-Image-Rectification), please refer to our new work [DocScanner](https://drive.google.com/file/d/1mmCUj90rHyuO1SmpLt361youh-07Y0sD/view?usp=share_link). - ***Image Metrics:*** We use the same evaluation code for MS-SSIM and LD as [DocUNet Benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html) dataset based on Matlab 2019a. Please compare the scores according to your Matlab version. We provide our Matlab interface file at ```$ROOT/ssim_ld_eval.m```. - ***OCR Metrics:*** The index of 30 document (60 images) of [DocUNet Benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html) used for our OCR evaluation is ```$ROOT/ocr_img.txt``` (*Setting 1*). Please refer to [DewarpNet](https://github.com/cvlab-stonybrook/DewarpNet) for the index of 25 document (50 images) of [DocUNet Benchmark](https://www3.cs.stonybrook.edu/~cvl/docunet.html) used for their OCR evaluation (*Setting 2*). We provide the OCR evaluation code at ```$ROOT/OCR_eval.py```. The version of pytesseract is 0.3.8, and the version of [Tesseract](https://digi.bib.uni-mannheim.de/tesseract/) in Windows is recent 5.0.1.20220118. Note that in different operating systems, the calculated performance has slight differences. | Method | MS-SSIM | LD | ED (*Setting 1*) | CER | ED (*Setting 2*) | CER | |:----------------:|:------------:|:--------------:| :-------:|:--------------:|:-------:|:--------------:| | GeoTr | 0.5105 | 7.76 | 464.83 | 0.1746 | 724.84 | 0.1832 | ## Citation If you find this code useful for your research, please use the following BibTeX entry. ``` @inproceedings{feng2021doctr, title={DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction}, author={Feng, Hao and Wang, Yuechen and Zhou, Wengang and Deng, Jiajun and Li, Houqiang}, booktitle={Proceedings of the 29th ACM International Conference on Multimedia}, pages={273--281}, year={2021} } ``` ``` @article{feng2021docscanner, title={DocScanner: Robust Document Image Rectification with Progressive Learning}, author={Feng, Hao and Zhou, Wengang and Deng, Jiajun and Tian, Qi and Li, Houqiang}, journal={arXiv preprint arXiv:2110.14968}, year={2021} } ``` ## Acknowledgement The codes are largely based on [DocUNet](https://www3.cs.stonybrook.edu/~cvl/docunet.html), [DewarpNet](https://github.com/cvlab-stonybrook/DewarpNet), and [DocProj](https://github.com/xiaoyu258/DocProj). Thanks for their wonderful works. ## Contact For commercial usage, please contact Prof. Wengang Zhou and Prof. Houqiang Li via ([zhwg@ustc.edu.cn](zhwg@ustc.edu.cn)) and ([lihq@ustc.edu.cn](lihq@ustc.edu.cn)) .