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README.md
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README.md
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# 简介
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PaddleOCR旨在打造一套丰富、领先、且实用的文字检测、识别模型/工具库，助力使用者训练出更好的模型，并应用落地。
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## 文档教程
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- [快速安装](./doc/installation.md)
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- [文本识别模型训练/评估/预测](./doc/detection.md)
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- [文本预测模型训练/评估/预测](./doc/recognition.md)
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PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
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## 特性：
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- 超轻量级模型
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- (检测模型4.1M + 识别模型4.5M = 8.6M)
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- 支持竖排文字
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- 支持竖排文字识别
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- (单模型同时支持横排和竖排文字识别)
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- 支持长文本识别
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- 支持中英文数字组合识别
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- 提供训练代码
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- 支持模型部署
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## 文档教程
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- [快速安装](./doc/installation.md)
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- [快速开始]()
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- [文本识别模型训练/评估/预测](./doc/detection.md)
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- [文本预测模型训练/评估/预测](./doc/recognition.md)
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- [基于inference model预测](./doc/)
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## 文本检测算法:
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PaddleOCR开源的文本检测算法列表：
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- [EAST](https://arxiv.org/abs/1704.03155)
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- [DB](https://arxiv.org/abs/1911.08947)
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- [SAST](https://arxiv.org/abs/1908.05498)
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- [x] [EAST](https://arxiv.org/abs/1704.03155)
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- [x] [DB](https://arxiv.org/abs/1911.08947)
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- [x] [SAST](https://arxiv.org/abs/1908.05498)
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- []
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算法效果：
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|模型|骨干网络|数据集|Hmean|
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|EAST|ResNet50_vd|ICDAR2015|85.85%|
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|EAST|MobileNetV3|ICDAR2015|79.08%|
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|DB|ResNet50_vd|ICDAR2015|83.30%|
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|DB|MobileNetV3|ICDAR2015|73.00%|
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|模型|骨干网络|Hmean|
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|EAST^[1]^|ResNet50_vd|85.85%|
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|EAST^[1]^|MobileNetV3|79.08%|
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|DB^[2]^|ResNet50_vd|83.30%|
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|DB^[2]^|MobileNetV3|73.00%|
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PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
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## 文本识别算法:
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PaddleOCR开源的文本识别算法列表：
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- (CRNN)[https://arxiv.org/abs/1507.05717]
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- [CRNN](https://arxiv.org/abs/1507.05717)
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- [Rosetta](https://arxiv.org/abs/1910.05085)
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- [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
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- [RARE](https://arxiv.org/abs/1603.03915v1)
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@ -59,7 +64,8 @@ PaddleOCR开源的文本识别算法列表：
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PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition.md)。
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## 端到端算法
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## TODO
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**端到端OCR算法**
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PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
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- End2End-PSL (comming soon)
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@ -67,7 +73,7 @@ PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.o
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# 参考文献
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```
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EAST:
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1. EAST:
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@inproceedings{zhou2017east,
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title={EAST: an efficient and accurate scene text detector},
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author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
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@ -76,7 +82,7 @@ EAST:
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year={2017}
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}
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DB:
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2. DB:
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@article{liao2019real,
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title={Real-time Scene Text Detection with Differentiable Binarization},
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author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
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@ -84,7 +90,7 @@ DB:
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year={2019}
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}
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DTRB:
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3. DTRB:
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@inproceedings{baek2019wrong,
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title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
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author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
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@ -93,7 +99,7 @@ DTRB:
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year={2019}
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}
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SAST:
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4. SAST:
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@inproceedings{wang2019single,
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title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
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author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
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@ -102,7 +108,7 @@ SAST:
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year={2019}
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}
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SRN:
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5. SRN:
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@article{yu2020towards,
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title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
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author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
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@ -110,7 +116,7 @@ SRN:
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year={2020}
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}
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end2end-psl:
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6. end2end-psl:
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@inproceedings{sun2019chinese,
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title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
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author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
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# 文字检测
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本节以icdar15数据集为例，介绍PaddleOCR中检测模型的使用方式。
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本节以icdar15数据集为例，介绍PaddleOCR中检测模型的训练、评估与测试。
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## 数据准备
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icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到，首次下载需注册。
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@ -26,8 +26,9 @@ wget -P /PaddleOCR/train_data/ 测试标注文件链接
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" 图像文件名 json.dumps编码的图像标注信息"
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ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
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```
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json.dumps编码前的图像标注信息是包含多个字典的list，字典中的points表示文本框的位置，如果您想在其他数据集上训练PaddleOCR,
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可以按照上述形式构建标注文件。
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json.dumps编码前的图像标注信息是包含多个字典的list，字典中的$points$表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。
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$transcription$表示当前文本框的文字，在文本检测任务中并不需要这个信息。
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如果您想在其他数据集上训练PaddleOCR，可以按照上述形式构建标注文件。
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## 快速启动训练
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运行如下代码，根据配置文件det_db_mv3.yml中save_res_path指定的测试集检测结果文件，计算评估指标。
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```
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python3 tools/eval.py -c configs/det/det_db_mv3.yml -o checkpoints="./output/best_accuracy"
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python3 tools/eval.py -c configs/det/det_db_mv3.yml -o Gloabl.checkpoints="./output/best_accuracy"
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```
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## 测试检测效果
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@ -25,10 +25,3 @@ cd PaddleOCR
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pip3 install --upgrade pip
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pip3 install -r requirements.txt
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```
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## 快速运行
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```
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python3 tools/infer/predict_eval.py --image_file="./"
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```
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【可视化运行结果】
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@ -22,6 +22,7 @@ import string
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from ppocr.utils.utility import create_module
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from tools.infer.utility import get_image_file_list
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import time
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@ -72,16 +73,8 @@ class EvalTestReader(object):
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self.params)
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batch_size = self.params['test_batch_size_per_card']
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flag_test_single_img = False
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if mode == "test":
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single_img_path = self.params['single_img_path']
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if single_img_path is not None:
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flag_test_single_img = True
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img_list = []
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if flag_test_single_img:
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img_list.append([single_img_path, single_img_path])
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else:
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if mode != "test":
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img_set_dir = self.params['img_set_dir']
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img_name_list_path = self.params['label_file_path']
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with open(img_name_list_path, "rb") as fin:
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img_name = line.decode().strip("\n").split("\t")[0]
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img_path = img_set_dir + "/" + img_name
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img_list.append([img_path, img_name])
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else:
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img_path = self.params['single_img_path']
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img_list = get_image_file_list(img_path)
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def batch_iter_reader():
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batch_outs = []
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return parser.parse_args()
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def get_image_file_list(image_dir):
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image_file_list = []
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if image_dir is None:
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return image_file_list
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if os.path.isfile(image_dir):
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image_file_list = [image_dir]
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elif os.path.isdir(image_dir):
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for single_file in os.listdir(image_dir):
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image_file_list.append(os.path.join(image_dir, single_file))
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return image_file_list
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def get_image_file_list(img_file):
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imgs_lists = []
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
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if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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if single_file.split('.')[-1] in img_end:
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imgs_lists.append(os.path.join(img_file, single_file))
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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return imgs_lists
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def create_predictor(args, mode):
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logger.info("The detected Image saved in {}".format(save_path))
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def simple_reader(img_file, config):
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imgs_lists = []
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
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if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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if single_file.split('.')[-1] in img_end:
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imgs_lists.append(os.path.join(img_file, single_file))
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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batch_size = config['Global']['test_batch_size_per_card']
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global_params = config['Global']
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params = deepcopy(config['TestReader'])
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params.update(global_params)
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reader_function = params['process_function']
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process_function = create_module(reader_function)(params)
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def batch_iter_reader():
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batch_outs = []
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for img_path in imgs_lists:
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img = cv2.imread(img_path)
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if img is None:
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logger.info("load image error:" + img_path)
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continue
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outs = process_function(img)
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outs.append(os.path.basename(img_path))
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print(outs[0].shape, outs[2])
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batch_outs.append(outs)
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if len(batch_outs) == batch_size:
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yield batch_outs
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batch_outs = []
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if len(batch_outs) != 0:
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yield batch_outs
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return batch_iter_reader
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def main():
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config = program.load_config(FLAGS.config)
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program.merge_config(FLAGS.opt)
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save_res_path = config['Global']['save_res_path']
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with open(save_res_path, "wb") as fout:
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# test_reader = reader_main(config=config, mode='test')
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single_img_path = config['TestReader']['single_img_path']
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test_reader = simple_reader(img_file=single_img_path, config=config)
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test_reader = reader_main(config=config, mode='test')
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tackling_num = 0
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for data in test_reader():
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img_num = len(data)
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