From 5df1f7e2e568c37a27a936720f4fd9f51e00a318 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 8 Jun 2020 04:48:13 +0000 Subject: [PATCH] add datasets & FAQ doc --- README.md | 26 +++++++------- doc/FAQ.md | 42 ++++++++++++++++++++++ doc/WeChat.jpeg | Bin 199125 -> 0 bytes doc/datasets.md | 59 +++++++++++++++++++++++++++++++ doc/datasets/ArT.jpg | Bin 0 -> 3268318 bytes doc/datasets/LSVT_1.jpg | Bin 0 -> 125625 bytes doc/datasets/LSVT_2.jpg | Bin 0 -> 96370 bytes doc/datasets/ch_doc1.jpg | Bin 0 -> 2226 bytes doc/datasets/ch_doc2.jpg | Bin 0 -> 2478 bytes doc/datasets/ch_doc3.jpg | Bin 0 -> 2117 bytes doc/datasets/ch_street_rec_1.png | Bin 0 -> 102648 bytes doc/datasets/ch_street_rec_2.png | Bin 0 -> 116907 bytes doc/datasets/rctw.jpg | Bin 0 -> 95307 bytes 13 files changed, 113 insertions(+), 14 deletions(-) create mode 100644 doc/FAQ.md delete mode 100644 doc/WeChat.jpeg create mode 100644 doc/datasets.md create mode 100644 doc/datasets/ArT.jpg create mode 100644 doc/datasets/LSVT_1.jpg create mode 100644 doc/datasets/LSVT_2.jpg create mode 100644 doc/datasets/ch_doc1.jpg create mode 100644 doc/datasets/ch_doc2.jpg create mode 100644 doc/datasets/ch_doc3.jpg create mode 100644 doc/datasets/ch_street_rec_1.png create mode 100644 doc/datasets/ch_street_rec_2.png create mode 100644 doc/datasets/rctw.jpg diff --git a/README.md b/README.md index a19f07ee..3da8066e 100644 --- a/README.md +++ b/README.md @@ -95,6 +95,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_mode - [文本检测模型训练/评估/预测](./doc/detection.md) - [文本识别模型训练/评估/预测](./doc/recognition.md) - [基于预测引擎推理](./doc/inference.md) +- [数据集](./doc/datasets.md) ## 文本检测算法 @@ -161,25 +162,22 @@ PaddleOCR文本识别算法的训练和使用请参考文档教程中[文本识 ![](doc/imgs_results/chinese_db_crnn_server/8.jpg) ## FAQ -1. 预测报错:got an unexpected keyword argument 'gradient_clip' +1. **预测报错:got an unexpected keyword argument 'gradient_clip'** +安装的paddle版本不对,目前本项目仅支持paddle1.7,近期会适配到1.8。 - 安装的paddle版本不对,目前本项目仅支持paddle1.7,近期会适配到1.8。 +2. **转换attention识别模型时报错:KeyError: 'predict'** +基于Attention损失的识别模型推理还在调试中。对于中文文本识别,建议优先选择基于CTC损失的识别模型,实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。 -2. 转换attention识别模型时报错:KeyError: 'predict' +3. **关于推理速度** +图片中的文字较多时,预测时间会增,可以使用--rec_batch_num设置更小预测batch num,默认值为30,可以改为10或其他数值。 - 基于Attention损失的识别模型推理还在调试中。对于中文文本识别,建议优先选择基于CTC损失的识别模型,实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。 +4. **服务部署与移动端部署** +预计6月中下旬会先后发布基于Serving的服务部署方案和基于Paddle Lite的移动端部署方案,欢迎持续关注。 -3. 关于推理速度 +5. **自研算法发布时间** +自研算法SAST、SRN、End2End-PSL都将在6-7月陆续发布,敬请期待。 - 图片中的文字较多时,预测时间会增,可以使用--rec_batch_num设置更小预测batch num,默认值为30,可以改为10或其他数值。 - -4. 服务部署与移动端部署 - - 预计6月中下旬会先后发布基于Serving的服务部署方案和基于Paddle Lite的移动端部署方案,欢迎持续关注。 - -5. 自研算法发布时间 - - 自研算法SAST、SRN、End2End-PSL都将在6-7月陆续发布,敬请期待。 +[more](./doc/FAQ.md) ## 欢迎加入PaddleOCR技术交流群 加微信:paddlehelp,备注OCR,小助手拉你进群~ diff --git a/doc/FAQ.md b/doc/FAQ.md new file mode 100644 index 00000000..f4e59473 --- /dev/null +++ b/doc/FAQ.md @@ -0,0 +1,42 @@ +## FAQ + +1. **预测报错:got an unexpected keyword argument 'gradient_clip'** +安装的paddle版本不对,目前本项目仅支持paddle1.7,近期会适配到1.8。 + +2. **转换attention识别模型时报错:KeyError: 'predict'** +基于Attention损失的识别模型推理还在调试中。对于中文文本识别,建议优先选择基于CTC损失的识别模型,实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。 + +3. **关于推理速度** +图片中的文字较多时,预测时间会增,可以使用--rec_batch_num设置更小预测batch num,默认值为30,可以改为10或其他数值。 + +4. **服务部署与移动端部署** +预计6月中下旬会先后发布基于Serving的服务部署方案和基于Paddle Lite的移动端部署方案,欢迎持续关注。 + +5. **自研算法发布时间** +自研算法SAST、SRN、End2End-PSL都将在6-7月陆续发布,敬请期待。 + +6. **如何在Windows或Mac系统上运行** +PaddleOCR已完成Windows和Mac系统适配,运行时注意两点:1、在[快速安装](installation.md)时,如果不想安装docker,可跳过第一步,直接从第二步安装paddle开始。2、inference模型下载时,如果没有安装wget,可直接点击模型链接或将链接地址复制到浏览器进行下载,并解压放置到相应目录。 + +7. **超轻量模型和通用OCR模型的区别** +目前PaddleOCR开源了2个中文模型,分别是8.6M超轻量中文模型和通用中文OCR模型。两者对比信息如下: + - 相同点:两者使用相同的**算法**和**训练数据**; + - 不同点:不同之处在于**骨干网络**和**通道参数**,超轻量模型使用MobileNetV3作为骨干网络,通用模型使用Resnet50_vd作为检测模型backbone,Resnet34_vd作为识别模型backbone,具体参数差异可对比两种模型训练的配置文件。 +|模型|骨干网络|检测训练配置|识别训练配置| +|-|-|-|-| +|8.6M超轻量中文OCR模型|MobileNetV3+MobileNetV3|det_mv3_db.yml|rec_chinese_lite_train.yml| +|通用中文OCR模型|Resnet50_vd+Resnet34_vd|det_r50_vd_db.yml|rec_chinese_common_train.yml| + +8. **是否有计划开源仅识别数字或仅识别英文+数字的模型** +暂不计划开源仅数字、仅数字+英文、或其他小垂类专用模型。PaddleOCR开源了多种检测、识别算法供用户自定义训练,两种中文模型也是基于开源的算法库训练产出,有小垂类需求的小伙伴,可以按照教程准备好数据,选择合适的配置文件,自行训练,相信能有不错的效果。训练有任何问题欢迎提issue或在交流群提问,我们会及时解答。 + +9. **开源模型使用的训练数据是什么,能否开源** +目前开源的模型,数据集和量级如下: + - 检测: + 英文数据集,ICDAR2015 + 中文数据集,LSVT街景数据集训练数据3w张图片 + - 识别: + 英文数据集,MJSynth和SynthText合成数据,数据量上千万。 + 中文数据集,LSVT街景数据集根据真值将图crop出来,并进行位置校准,总共30w张图像。此外基于LSVT的语料,合成数据500w。 + + 其中,公开数据集都是开源的,用户可自行搜索下载,也可参考[中文数据集](datasets.md),合成数据暂不开源,用户可使用开源合成工具自行合成,可参考的合成工具包括[text_renderer](https://github.com/Sanster/text_renderer)、[SynthText](https://github.com/ankush-me/SynthText)、[TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator)等。 \ No newline at end of file diff --git a/doc/WeChat.jpeg b/doc/WeChat.jpeg deleted file mode 100644 index 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zbnR$aLH+HedJ6dR>T8~iy$0;^CAhTO20*cl&djy-6n9k%INUV-I3WGvUe%Mr>vqcT zl~;uW;fciG-1kY-S|h=yXU&O>t#B>1QrQIezLF`874hgp&&bcYM7(=B;>I_Q08i!$ z5Uogs9{l-0ChPm-QF7*hxP4XIEe&%&Zx6Af+BwqP&B?R35@~1D8X&c^Lve~K%$OKB z8(zUyV~W;)aXxKvYfK5J%&rXQpnqyF2mVPRtT3h^sc!N|yO0vtFA0VThc?M%7M?Ye z6r))_&4)&DHlfRMC47PMxft6L*1e~iEW*+C475CC^A6tBw;XL)nYVVuM@1`jbeMyZP zD5*Nx-tEb +#### 1、ICDAR2019-LSVT +- **数据来源**:https://ai.baidu.com/broad/introduction?dataset=lsvt +- **数据简介**: 共45w中文街景图像,包含5w(2w测试+3w训练)全标注数据(文本坐标+文本内容),40w弱标注数据(仅文本内容),如下图所示: + ![](datasets/LSVT_1.jpg) + (a) 全标注数据 + ![](datasets/LSVT_2.jpg) + (b) 弱标注数据 +- **下载地址**:https://ai.baidu.com/broad/download?dataset=lsvt + + +#### 2、ICDAR2017-RCTW-17 +- **数据来源**:https://rctw.vlrlab.net/ +- **数据简介**:共包含12,000+图像,大部分图片是通过手机摄像头在野外采集的。有些是截图。这些图片展示了各种各样的场景,包括街景、海报、菜单、室内场景和手机应用程序的截图。 + ![](datasets/rctw.jpg) +- **下载地址**:https://rctw.vlrlab.net/dataset/ + + +#### 3、中文街景文字识别 +- **数据来源**:https://aistudio.baidu.com/aistudio/competition/detail/8 +- **数据简介**:共包括29万张图片,其中21万张图片作为训练集(带标注),8万张作为测试集(无标注)。数据集采自中国街景,并由街景图片中的文字行区域(例如店铺标牌、地标等等)截取出来而形成。所有图像都经过一些预处理,将文字区域利用仿射变化,等比映射为一张高为48像素的图片,如图所示: + ![](datasets/ch_street_rec_1.png) + (a) 标注:魅派集成吊顶 + ![](datasets/ch_street_rec_2.png) + (b) 标注:母婴用品连锁 +- **下载地址** +https://aistudio.baidu.com/aistudio/datasetdetail/8429 + + +#### 4、中文文档文字识别 +- **数据来源**:https://github.com/YCG09/chinese_ocr +- **数据简介**: + - 共约364万张图片,按照99:1划分成训练集和验证集。 + - 数据利用中文语料库(新闻 + 文言文),通过字体、大小、灰度、模糊、透视、拉伸等变化随机生成 + - 包含汉字、英文字母、数字和标点共5990个字符(字符集合:https://github.com/YCG09/chinese_ocr/blob/master/train/char_std_5990.txt ) + - 每个样本固定10个字符,字符随机截取自语料库中的句子 + - 图片分辨率统一为280x32 + + ![](datasets/ch_doc1.jpg) + ![](datasets/ch_doc2.jpg) + ![](datasets/ch_doc3.jpg) +- **下载地址**:https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw (密码:lu7m) + + +#### 5、ICDAR2019-ArT +- **数据来源**:https://ai.baidu.com/broad/introduction?dataset=art +- **数据简介**:共包含10,166张图像,训练集5603图,测试集4563图。由Total-Text、SCUT-CTW1500、Baidu Curved Scene Text三部分组成,包含水平、多方向和弯曲等多种形状的文本。 + ![](datasets/ArT.jpg) +- **下载地址**:https://ai.baidu.com/broad/download?dataset=art \ No newline at end of file diff --git a/doc/datasets/ArT.jpg b/doc/datasets/ArT.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7855ba4ac631e11c3366b76ab827bd1abab34ff0 GIT binary patch literal 3268318 zcmagE1yEeUvp0$b2^JtY3j|1T_r-%O1P|`+5Ii^pch@EC;_mKl!Civ8>*D_MzxR9J zcVE4#_fFNBnbXt1?$guNHQjSgxRL@0^F7IXI5;>=8EJ{{aB#>paBzr6sL1~)c%8ff zaBvv3R^sAHGUDQtN>29XR<>qvaMIz)8Yr47Lj+Krgt)j#1e`crPrQ7M;%vzeaacTG zai!_lzaZfVv=nV9!Y^_0EJQl~nH`8NURPsnfnw&dtziUslKYMZB z8|8oFcQfxGhMUdhH+JMHfftRT)4;_e`6KsD!6*R;kKjkrg}3O~^xaX^#RcgX$MtJr zTPwU>wr+ju(_`~nXT+CS)?I{O65<&HiB8Ux!WeL81NgE|@RZl0?SG?m+505^;9AO3 ziiM5s@`;BF?eNKmQ-9=fWM>Lffmh0Q;!D7Qo84(k^H{++0ND}uVNpSqkQ}M|I&|Sf zQ7$dQK)X9~ftsr%#>aK%&!*447E4u%ShOZYjLyno-{O=Q#`i{_MMf?=Khx^)eC);G zDG|jbpq|juZ!ELgbdn3c51?b_cyG)cR*B~E-izOtDlWJmd&zI(n^u+iFL=0byN&@p zpLl*PswM%5*^BPv2}7zrh17$#gPKqHb~Te|!33NK6l43cfhM7Bpq#MJjr1vI{U{iZ 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