From a63d91c83f703345800f9e5fd3b51b0adf6eadf4 Mon Sep 17 00:00:00 2001 From: grasswolfs Date: Fri, 18 Sep 2020 23:13:28 +0800 Subject: [PATCH] test=develop, test=document_fix --- README.md | 159 ++++++++----------------------- doc/doc_ch/algorithm_overview.md | 78 +++++++++++++++ doc/doc_ch/tricks.md | 68 ------------- doc/doc_ch/update.md | 2 + doc/doc_ch/visualization.md | 55 +++++------ doc/imgs_results/1101.jpg | Bin 0 -> 83688 bytes doc/imgs_results/1102.jpg | Bin 0 -> 150619 bytes doc/imgs_results/1103.jpg | Bin 0 -> 127192 bytes doc/imgs_results/1104.jpg | Bin 0 -> 167542 bytes doc/imgs_results/1105.jpg | Bin 0 -> 140129 bytes doc/imgs_results/1110.jpg | Bin 0 -> 133200 bytes doc/imgs_results/1112.jpg | Bin 0 -> 104269 bytes 12 files changed, 143 insertions(+), 219 deletions(-) create mode 100644 doc/doc_ch/algorithm_overview.md delete mode 100644 doc/doc_ch/tricks.md create mode 100644 doc/imgs_results/1101.jpg create mode 100644 doc/imgs_results/1102.jpg create mode 100644 doc/imgs_results/1103.jpg create mode 100644 doc/imgs_results/1104.jpg create mode 100644 doc/imgs_results/1105.jpg create mode 100644 doc/imgs_results/1110.jpg create mode 100644 doc/imgs_results/1112.jpg diff --git a/README.md b/README.md index 4ebbf2f0..82600b34 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ 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。 **近期更新** +- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列系列中英文ocr模型,效果媲美商业效果。[模型下载](#模型下载) - 2020.8.26 更新OCR相关的84个常见问题及解答,具体参考[FAQ](./doc/doc_ch/FAQ.md) - 2020.8.24 支持通过whl包安装使用PaddleOCR,具体参考[Paddleocr Package使用说明](./doc/doc_ch/whl.md) - 2020.8.21 更新8月18日B站直播课回放和PPT,课节2,易学易用的OCR工具大礼包,[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519) @@ -14,51 +15,69 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力 ## 特性 -- 超轻量级中文OCR模型,总模型仅8.6M - - 单模型支持中英文数字组合识别、竖排文本识别、长文本识别 - - 检测模型DB(4.1M)+识别模型CRNN(4.5M) -- 实用通用中文OCR模型 -- 多种预测推理部署方案,包括服务部署和端侧部署 -- 多种文本检测训练算法,EAST、DB、SAST -- 多种文本识别训练算法,Rosetta、CRNN、STAR-Net、RARE、SRN + +- PPOCR系列高质量预训练模型,媲美商业效果 + - 超轻量ppocr_mobile系列:检测(2.5M)+方向分类器(0.9M + )+ 识别(4.5M)= 7.9M + - 通用ppocr_server系列:检测(47.2M)+方向分类器(0.9M)+ 识别(107M)= 155.1M + - 超轻量压缩ppocr_mobile_slim系列:(coming soon) +- 支持中英文数字组合识别、竖排文本识别、长文本识别 +- 支持多语言识别:韩语、日语、德语、法语 (coming soon) +- 支持用户自定义训练,提供丰富的预测推理部署方案 +- 支持PIP快速安装使用 - 可运行于Linux、Windows、MacOS等多种系统 -## 快速体验 +## 效果展示
- + +
上图是超轻量级中文OCR模型效果展示,更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。 -- 超轻量级中文OCR在线体验地址:https://www.paddlepaddle.org.cn/hub/scene/ocr -- 移动端DEMO体验(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统):[安装包二维码获取地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite) +## 快速体验 +- PC端:超轻量级中文OCR在线体验地址:https://www.paddlepaddle.org.cn/hub/scene/ocr + +- 移动端:[安装包DEMO下载地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统),Android手机也可以直接扫描下面二维码安装体验。 - Android手机也可以扫描下面二维码安装体验。
+- 代码体验:可以直接进入[快速安装](./doc/doc_ch/installation.md) -## 中文OCR模型列表 + +## PP-OCR 1.1系列模型列表(9月17日更新) + +| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 | | +| ------------ | --------------- | ----------------|---- | ---------- | -------- | ---- | +| 中英文超轻量OCR模型(7.9M) | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_train.tar) |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) | | +| 中英文通用OCR模型(155.1M) |ch_ppocr_server_v1.1_xx|服务器端 |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_train.tar) |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) | | +| 中英文超轻量压缩OCR模型 | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |即将开源 |即将开源|即将开源| | || + +更多V1.1版本模型下载,可以参考[OCR1.1模型列表](./doc/doc_ch/models_list.md) + +## PP-OCR 1.0系列模型列表(7月16日更新) + +| 模型简介 | 模型名称 | 检测模型 | 方向分类器 | 识别模型 | | +| ------------ | ---------------------- | -------- | ---------- | -------- | ---- | +| 超轻量中英文OCR模型(8.6M) | chinese_db_crnn_mobile |[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar) | | +|通用中文OCR模型(212M)|chinese_db_crnn_server|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)| | -|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址| -|-|-|-|-|-| -|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar) -|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar) ## 文档教程 - [快速安装](./doc/doc_ch/installation.md) - [中文OCR模型快速使用](./doc/doc_ch/quickstart.md) - 算法介绍 - - [文本检测](#文本检测算法) - - [文本识别](#文本识别算法) + - [文本检测](./doc/doc_ch/algorithm_overview.md) + - [文本识别](./doc/doc_ch/algorithm_overview.md) + - PP-OCR (coming soon) - 模型训练/评估 - [文本检测](./doc/doc_ch/detection.md) - [文本识别](./doc/doc_ch/recognition.md) - [yml参数配置文件介绍](./doc/doc_ch/config.md) - - [中文OCR训练预测技巧](./doc/doc_ch/tricks.md) - 预测部署 - [基于Python预测引擎推理](./doc/doc_ch/inference.md) - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md) @@ -72,10 +91,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力 - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md) - [常用数据标注工具](./doc/doc_ch/data_annotation.md) - [常用数据合成工具](./doc/doc_ch/data_synthesis.md) -- 效果展示 - - [超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示) - - [通用中文OCR效果展示](#通用中文OCR效果展示) - - [支持空格的中文OCR效果展示](#支持空格的中文OCR效果展示) +- [效果展示](#超轻量级中文OCR效果展示) - FAQ - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md) - [【理论篇】OCR通用21个问题](./doc/doc_ch/FAQ.md) @@ -85,104 +101,11 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力 - [许可证书](#许可证书) - [贡献代码](#贡献代码) - -## 算法介绍 - -### 1.文本检测算法 - -PaddleOCR开源的文本检测算法列表: -- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) -- [x] DB([paper](https://arxiv.org/abs/1911.08947)) -- [x] SAST([paper](https://arxiv.org/abs/1908.05498))(百度自研) - -在ICDAR2015文本检测公开数据集上,算法效果如下: - -|模型|骨干网络|precision|recall|Hmean|下载链接| -|-|-|-|-|-|-| -|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)| -|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)| -|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)| -|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)| -|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)| - -在Total-text文本检测公开数据集上,算法效果如下: - -|模型|骨干网络|precision|recall|Hmean|下载链接| -|-|-|-|-|-|-| -|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)| - -**说明:** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载:[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi) - - -使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据,训练中文检测模型的相关配置和预训练文件如下: - -|模型|骨干网络|配置文件|预训练模型| -|-|-|-|-| -|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)| -|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)| - -* 注: 上述DB模型的训练和评估,需设置后处理参数box_thresh=0.6,unclip_ratio=1.5,使用不同数据集、不同模型训练,可调整这两个参数进行优化 + -PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./doc/doc_ch/detection.md)。 - -### 2.文本识别算法 -PaddleOCR开源的文本识别算法列表: -- [x] CRNN([paper](https://arxiv.org/abs/1507.05717)) -- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) -- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) -- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1)) -- [x] SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研) -参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下: - -|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接| -|-|-|-|-|-| -|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)| -|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)| -|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)| -|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)| -|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)| -|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)| -|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)| -|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)| -|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)| - -**说明:** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广,增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载,提取码: y3ry。 -原始论文使用两阶段训练平均精度为89.74%,PaddleOCR中使用one-stage训练,平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。 - -使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据,进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型,相关配置和预训练文件如下: - -|模型|骨干网络|配置文件|预训练模型| -|-|-|-|-| -|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)| -|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)| - -PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./doc/doc_ch/recognition.md)。 - -## 效果展示 - - -### 1.超轻量级中文OCR效果展示 [more](./doc/doc_ch/visualization.md) - -
- -
- - -### 2.通用中文OCR效果展示 [more](./doc/doc_ch/visualization.md) - -
- -
- - -### 3.支持空格的中文OCR效果展示 [more](./doc/doc_ch/visualization.md) - -
- -
## 欢迎加入PaddleOCR技术交流群 diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md new file mode 100644 index 00000000..9c2499f3 --- /dev/null +++ b/doc/doc_ch/algorithm_overview.md @@ -0,0 +1,78 @@ + +## 算法介绍 +- [1.文本检测算法](#文本检测算法) +- [2.文本识别算法](#文本识别算法) + + +### 1.文本检测算法 + +PaddleOCR开源的文本检测算法列表: +- [x] DB([paper](https://arxiv.org/abs/1911.08947))(ppocr推荐) +- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) +- [x] SAST([paper](https://arxiv.org/abs/1908.05498)) + +在ICDAR2015文本检测公开数据集上,算法效果如下: + +|模型|骨干网络|precision|recall|Hmean|下载链接| +|-|-|-|-|-|-| +|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)| +|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)| +|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)| +|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)| +|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)| + +在Total-text文本检测公开数据集上,算法效果如下: + +|模型|骨干网络|precision|recall|Hmean|下载链接| +|-|-|-|-|-|-| +|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)| + +**说明:** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载:[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi) + + +使用[LSVT](./datasets.md#1icdar2019-lsvt)街景数据集共3w张数据,训练中文检测模型的相关配置和预训练文件如下: + +|模型|骨干网络|配置文件|预训练模型| +|-|-|-|-| +|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)| +|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)| + +* 注: 上述DB模型的训练和评估,需设置后处理参数box_thresh=0.6,unclip_ratio=1.5,使用不同数据集、不同模型训练,可调整这两个参数进行优化 + +PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./detection.md)。 + + +### 2.文本识别算法 + +PaddleOCR开源的文本识别算法列表: +- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))(ppocr推荐) +- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) +- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) +- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1)) +- [x] SRN([paper](https://arxiv.org/abs/2003.12294)) + +参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下: + +|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接| +|-|-|-|-|-| +|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)| +|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)| +|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)| +|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)| +|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)| +|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)| +|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)| +|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)| +|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)| + +**说明:** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广,增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载,提取码: y3ry。 +原始论文使用两阶段训练平均精度为89.74%,PaddleOCR中使用one-stage训练,平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。 + +使用[LSVT](./datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据,进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型,相关配置和预训练文件如下: + +|模型|骨干网络|配置文件|预训练模型| +|-|-|-|-| +|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)| +|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)| + +PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。 diff --git a/doc/doc_ch/tricks.md b/doc/doc_ch/tricks.md deleted file mode 100644 index b6852bc9..00000000 --- a/doc/doc_ch/tricks.md +++ /dev/null @@ -1,68 +0,0 @@ -## 中文OCR训练预测技巧 -这里整理了一些中文OCR训练预测技巧,持续更新中,欢迎各位小伙伴贡献OCR炼丹秘籍~ -- [更换骨干网络](#更换骨干网络) -- [中文长文本识别](#中文长文本识别) -- [空格识别](#空格识别) - - -#### 1、更换骨干网络 -- **问题描述** - - 目前PaddleOCR中使用的骨干网络有ResNet_vd系列和MobileNetV3系列,更换骨干网络是否有助于效果提升?更换时需要注意什么? - -- **炼丹建议** - - - 无论是文字检测,还是文字识别,骨干网络的选择是预测效果和预测效率的权衡。一般,选择更大规模的骨干网络,例如ResNet101_vd,则检测或识别更准确,但预测耗时相应也会增加。而选择更小规模的骨干网络,例如MobileNetV3_small_x0_35,则预测更快,但检测或识别的准确率会大打折扣。幸运的是不同骨干网络的检测或识别效果与在ImageNet数据集图像1000分类任务效果正相关。[**飞桨图像分类套件PaddleClas**](https://github.com/PaddlePaddle/PaddleClas)汇总了ResNet_vd、Res2Net、HRNet、MobileNetV3、GhostNet等23种系列的分类网络结构,在上述图像分类任务的top1识别准确率,GPU(V100和T4)和CPU(骁龙855)的预测耗时以及相应的[**117个预训练模型下载地址**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。 - - 文字检测骨干网络的替换,主要是确定类似与ResNet的4个stages,以方便集成后续的类似FPN的检测头。此外,对于文字检测问题,使用ImageNet训练的分类预训练模型,可以加速收敛和效果提升。 - - 文字识别的骨干网络的替换,需要注意网络宽高stride的下降位置。由于文本识别一般宽高比例很大,因此高度下降频率少一些,宽度下降频率多一些。可以参考PaddleOCR中[MobileNetV3骨干网络](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/modeling/backbones/rec_mobilenet_v3.py)的改动。 - - -#### 2、中文长文本识别 -- **问题描述** - - 中文识别模型训练时分辨率最大是[3,32,320],如果待识别的文本图像太长,如下图所示,该如何适配? - -
- -
- -- **炼丹建议** - - 在中文识别模型训练时,并不是采用直接将训练样本缩放到[3,32,320]进行训练,而是先等比例缩放图像,保证图像高度为32,宽度不足320的部分补0,宽高比大于10的样本直接丢弃。预测时,如果是单张图像预测,则按上述操作直接对图像缩放,不做宽度320的限制。如果是多张图预测,则采用batch方式预测,每个batch的宽度动态变换,采用这个batch中最长宽度。[参考代码如下](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/tools/infer/predict_rec.py): - - ``` - def resize_norm_img(self, img, max_wh_ratio): - imgC, imgH, imgW = self.rec_image_shape - assert imgC == img.shape[2] - if self.character_type == "ch": - imgW = int((32 * max_wh_ratio)) - h, w = img.shape[:2] - ratio = w / float(h) - if math.ceil(imgH * ratio) > imgW: - resized_w = imgW - else: - resized_w = int(math.ceil(imgH * ratio)) - resized_image = cv2.resize(img, (resized_w, imgH)) - resized_image = resized_image.astype('float32') - resized_image = resized_image.transpose((2, 0, 1)) / 255 - resized_image -= 0.5 - resized_image /= 0.5 - padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) - padding_im[:, :, 0:resized_w] = resized_image - return padding_im - ``` - - -#### 3、空格识别 -- **问题描述** - - 如下图所示,对于中英文混合场景,为了便于阅读和使用识别结果,往往需要将单词之间的空格识别出来,这种情况如何适配? - -
- -
- -- **炼丹建议** - - 空格识别可以考虑以下两种方案:(1)优化文本检测算法。检测结果在空格处将文本断开。这种方案在检测数据标注时,需要将含有空格的文本行分成好多段。(2)优化文本识别算法。在识别字典里面引入空格字符,然后在识别的训练数据中,如果用空行,进行标注。此外,合成数据时,通过拼接训练数据,生成含有空格的文本。PaddleOCR目前采用的是第二种方案。 - \ No newline at end of file diff --git a/doc/doc_ch/update.md b/doc/doc_ch/update.md index 23a47df5..55442c8d 100644 --- a/doc/doc_ch/update.md +++ b/doc/doc_ch/update.md @@ -1,4 +1,6 @@ # 更新 +- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列系列中英文ocr模型,效果媲美商业效果。[模型下载](./models_list.md) +- 2020.8.26 更新OCR相关的84个常见问题及解答,具体参考[FAQ](./FAQ.md) - 2020.8.24 支持通过whl包安装使用PaddleOCR,具体参考[Paddleocr Package使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md) - 2020.8.21 更新8月18日B站直播课回放和PPT,课节2,易学易用的OCR工具大礼包,[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519) - 2020.8.16 开源文本检测算法[SAST](https://arxiv.org/abs/1908.05498)和文本识别算法[SRN](https://arxiv.org/abs/2003.12294) diff --git a/doc/doc_ch/visualization.md b/doc/doc_ch/visualization.md index 5a711fe9..70d4321f 100644 --- a/doc/doc_ch/visualization.md +++ b/doc/doc_ch/visualization.md @@ -1,45 +1,47 @@ # 效果展示 -- [超轻量级中文OCR效果展示](#超轻量级中文OCR) -- [通用中文OCR效果展示](#通用中文OCR) -- [支持空格的中文OCR效果展示](#支持空格的中文OCR) +- PP-OCR 1.1系列模型效果 + - [通用ppocr_server_1.1效果展示](#通用ppocr_server_1.1效果展示) + - [通用ppocr_server_1.1效果展示(待补充)]() +- PP-OCR 1.0系列模型效果 + - [超轻量ppocr_mobile_1.0效果展示](#超轻量ppocr_mobile_1.0效果展示) + - [通用ppocr_server_1.0效果展示](#通用ppocr_server_1.0效果展示) - -## 超轻量级中文OCR效果展示 + +## 通用ppocr_server_1.1效果展示
- + + + + + +
-
- -
-
- -
+ + +## 超轻量ppocr_mobile_1.0效果展示
- +
- +
- +
-
- -
- -## 通用中文OCR效果展示 + +## 通用ppocr_server_1.0效果展示
@@ -52,16 +54,3 @@
- - -## 支持空格的中文OCR效果展示 - -### 轻量级模型 -
- -
- -### 通用模型 -
- -
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