毕设&课程作业_“国土资源智能文档解析系统”项目.zip

## 使用手册 1.首先生成训练和验证数据 ``` python3 create_lmdb_dataset.py --inputPath data/ --gtFile data/labelfile --outputPath result/ ``` 生成数据后缀名为.mdb 2.运行如下命令进行训练 ``` python3 train.py --train_data datapath --valid_data datapath --saved_model modelpath --saved_model modelpath ``` datapath就是第一步生成数据的路径，saved_model可指定检查点 可根据需要添加参数，参数含义参考下面的文档 3.验证训练结果 ``` python3 demo.py --image_folder imgpath --saved_model modelpath --labelFilePath path ``` labelFilePath指定预测图片标注文件，如果要统计预测结果或可视化就需指定 4.直接在代码中设定的参数（更改default就行了） ``` train.py: num_iter: 训练轮数 valInterval： 中间结果验证相隔的轮数 workers：加载数据的并行进程数，设置为非0值可能会报错 demo.py: visual: 结果可视化 ``` 5.模型下载地址 链接：https://pan.baidu.com/s/1YDRVt82JtIQCfXNHxM4BZw 提取码：z81d # What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis | [paper](https://arxiv.org/abs/1904.01906) | [training and evaluation data](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | [failure cases and cleansed label](https://github.com/clovaai/deep-text-recognition-benchmark#download-failure-cases-and-cleansed-label-from-here) | [pretrained model](https://drive.google.com/drive/folders/15WPsuPJDCzhp2SvYZLRj8mAlT3zmoAMW) | [Baidu ver(passwd:rryk)](https://pan.baidu.com/s/1KSNLv4EY3zFWHpBYlpFCBQ) | Official PyTorch implementation of our four-stage STR framework, that most existing STR models fit into. <br> Using this framework allows for the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets. <br> Such analyses clean up the hindrance on the current comparisons to understand the performance gain of the existing modules. <br><br> <img src="./figures/trade-off.png" width="1000" title="trade-off"> ## Honors Based on this framework, we recorded the 1st place of [ICDAR2013 focused scene text](https://rrc.cvc.uab.es/?ch=2&com=evaluation&task=3), [ICDAR2019 ArT](https://rrc.cvc.uab.es/files/ICDAR2019-ArT.pdf) and 3rd place of [ICDAR2017 COCO-Text](https://rrc.cvc.uab.es/?ch=5&com=evaluation&task=2), [ICDAR2019 ReCTS (task1)](https://rrc.cvc.uab.es/files/ICDAR2019-ReCTS.pdf). <br> The difference between our paper and ICDAR challenge is summarized [here](https://github.com/clovaai/deep-text-recognition-benchmark/issues/13). ## Updates **Dec 27, 2019**: added [FLOPS](https://github.com/clovaai/deep-text-recognition-benchmark/issues/125) in our paper, and minor updates such as log_dataset.txt and [ICDAR2019-NormalizedED](https://github.com/clovaai/deep-text-recognition-benchmark/blob/86451088248e0490ff8b5f74d33f7d014f6c249a/test.py#L139-L165). <br> **Oct 22, 2019**: added [confidence score](https://github.com/clovaai/deep-text-recognition-benchmark/issues/82), and arranged the output form of training logs. <br> **Jul 31, 2019**: The paper is accepted at International Conference on Computer Vision (ICCV), Seoul 2019, as an oral talk. <br> **Jul 25, 2019**: The code for floating-point 16 calculation, check [@YacobBY's](https://github.com/YacobBY) [pull request](https://github.com/clovaai/deep-text-recognition-benchmark/pull/36) <br> **Jul 16, 2019**: added [ST_spe.zip](https://drive.google.com/drive/folders/192UfE9agQUMNq6AgU3_E05_FcPZK4hyt) dataset, word images contain special characters in SynthText (ST) dataset, see [this issue](https://github.com/clovaai/deep-text-recognition-benchmark/issues/7#issuecomment-511727025) <br> **Jun 24, 2019**: added gt.txt of failure cases that contains path and label of each image, see [image_release_190624.zip](https://drive.google.com/open?id=1VAP9l5GL5fgptgKDLio_h3nMe7X9W0Mf) <br> **May 17, 2019**: uploaded resources in Baidu Netdisk also, added [Run demo](https://github.com/clovaai/deep-text-recognition-benchmark#run-demo-with-pretrained-model). (check [@sharavsambuu's](https://github.com/sharavsambuu) [colab demo also](https://colab.research.google.com/drive/1PHnc_QYyf9b1_KJ1r15wYXaOXkdm1Mrk)) <br> **May 9, 2019**: PyTorch version updated from 1.0.1 to 1.1.0, use torch.nn.CTCLoss instead of torch-baidu-ctc, and various minor updated. ## Getting Started ### Dependency - This work was tested with PyTorch 1.3.1, CUDA 10.1, python 3.6 and Ubuntu 16.04. <br> You may need `pip3 install torch==1.3.1`. <br> In the paper, expriments were performed with **PyTorch 0.4.1, CUDA 9.0**. - requirements : lmdb, pillow, torchvision, nltk, natsort ``` pip3 install lmdb pillow torchvision nltk natsort ``` ### Download lmdb dataset for traininig and evaluation from [here](https://pan.baidu.com/s/1IJhghbBrQwcU3Cx2iXv7zw) 提取码：mlnc data_lmdb_release.zip contains below. <br> training datasets : [MJSynth (MJ)](http://www.robots.ox.ac.uk/~vgg/data/text/)[1] and [SynthText (ST)](http://www.robots.ox.ac.uk/~vgg/data/scenetext/)[2] \ validation datasets : the union of the training sets [IC13](http://rrc.cvc.uab.es/?ch=2)[3], [IC15](http://rrc.cvc.uab.es/?ch=4)[4], [IIIT](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)[5], and [SVT](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset)[6].\ evaluation datasets : benchmark evaluation datasets, consist of [IIIT](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)[5], [SVT](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset)[6], [IC03](http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2003_Robust_Reading_Competitions)[7], [IC13](http://rrc.cvc.uab.es/?ch=2)[3], [IC15](http://rrc.cvc.uab.es/?ch=4)[4], [SVTP](http://openaccess.thecvf.com/content_iccv_2013/papers/Phan_Recognizing_Text_with_2013_ICCV_paper.pdf)[8], and [CUTE](http://cs-chan.com/downloads_CUTE80_dataset.html)[9]. ### Run demo with pretrained model 1. Download pretrained model from [here](https://drive.google.com/drive/folders/15WPsuPJDCzhp2SvYZLRj8mAlT3zmoAMW) 2. Add image files to test into `demo_image/` 3. Run demo.py (add `--sensitive` option if you use case-sensitive model) ``` CUDA_VISIBLE_DEVICES=0 python3 demo.py --Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn --image_folder demo_image/ --saved_model TPS-ResNet-BiLSTM-Attn.pth ``` #### prediction results | demo images | [TPS-ResNet-BiLSTM-Attn](https://drive.google.com/open?id=1b59rXuGGmKne1AuHnkgDzoYgKeETNMv9) | [TPS-ResNet-BiLSTM-Attn (case-sensitive)](https://drive.google.com/open?id=1ajONZOgiG9pEYsQ-eBmgkVbMDuHgPCaY) | | --- | --- | --- | | <img src="./demo_image/demo_1.png" width="300"> | available | Available | | <img src="./demo_image/demo_2.jpg" width="300"> | shakeshack | SHARESHACK | | <img src="./demo_image/demo_3.png" width="300"> | london | Londen | | <img src="./demo_image/demo_4.png" width="300"> | greenstead | Greenstead | | <img src="./demo_image/demo_5.png" width="300" height="100"> | toast | TOAST | | <img src="./demo_image/demo_6.png" width="300" height="100"> | merry | MERRY | | <img src="./demo_image/demo_7.png" width="300"> | underground | underground | | <img src="./demo_image/demo_8.jpg" width="300"> | ronaldo | RONALDO | | <img src="./demo_image/demo_9.jpg" width="300" height="100"> | bally | BALLY | | <img src="./demo_image/demo_10.jpg" width="300" height="100"> | university | UNIVERSITY | ### Training and evaluation 1. Train CRNN[10] model ``` CUDA_VISIBLE_DEVICES=0 python3 train.py \ --train_data data_lmdb_release/training --valid_data data_lmdb_release/validation \ --select_data MJ-ST --batch_ratio 0.5-0.5 \ --Transformation None --Feat