From 1a024c69c7625fbce8e31ce2fc26096502445a33 Mon Sep 17 00:00:00 2001 From: grasswolfs Date: Sat, 19 Sep 2020 23:34:37 +0800 Subject: [PATCH] test=develop, test=document_fix --- README.md | 46 +++++++++++++++++++++++--------------- doc/doc_ch/update.md | 5 +++-- doc/imgs_results/1106.jpg | Bin 0 -> 290918 bytes doc/imgs_results/1110.jpg | Bin 133200 -> 249459 bytes doc/imgs_results/1112.jpg | Bin 104269 -> 149214 bytes doc/ppocr_framework.png | Bin 0 -> 591954 bytes 6 files changed, 31 insertions(+), 20 deletions(-) create mode 100644 doc/imgs_results/1106.jpg create mode 100644 doc/ppocr_framework.png diff --git a/README.md b/README.md index 099a43b5..3586148e 100644 --- a/README.md +++ b/README.md @@ -4,25 +4,22 @@ 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** +- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型,整体模型3.5M(详见[PP-OCR Pipline](#PP-OCR)),适合在移动端部署使用。[模型下载](#模型下载) - 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型,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) -- 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) -- 2020.7.23 发布7月21日B站直播课回放和PPT,课节1,PaddleOCR开源大礼包全面解读,[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519) -- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO,支持iOS和Android系统 -- [more](./doc/doc_ch/update.md) +- [More](./doc/doc_ch/update.md) ## 特性 - 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(2.6M)+方向分类器(0.9M - )+ 识别(4.6M)= 8.1M + - 超轻量ppocr_mobile移动端系列:检测(2.6M)+方向分类器(0.9M)+ 识别(4.6M)= 8.1M - 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) + - 超轻量压缩ppocr_mobile_slim系列:检测(1.4M)+方向分类器(0.5M)+ 识别(1.6M)= 3.5M - 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) +- 支持多语言识别:韩语、日语、德语、法语 - 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 @@ -46,44 +43,47 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力 -- 代码体验:可以直接进入[快速安装](./doc/doc_ch/installation.md) +- 代码体验:从[快速安装](./doc/doc_ch/installation.md) 开始 ## 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(8.1M) | 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| | || +| 中英文超轻量OCR模型(8.1M) | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](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)|[推理模型](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) |[推理模型](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) |[推理模型](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) |[推理模型](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 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb)| | || -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) +更多模型下载(包括多语言),可以参考[PP-OCR v1.1 系列模型下载](./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 | 识别模型 | 支持空格的识别模型 | | | ------------ | ---------------------- | -------- | ---------- | -------- | ---- | -| 超轻量中英文OCR模型(8.6M) | chinese_db_crnn_mobile_xx |[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_xx|[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)| | +| 超轻量中英文OCR模型(8.6M) | chinese_db_crnn_mobile_xx |[推理模型](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)|[推理模型](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)|[推理模型](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_xx|[推理模型](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)|[推理模型](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)|[推理模型](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) +- [代码组织结构](./doc/doc_ch/tree.md) - 算法介绍 - [文本检测](./doc/doc_ch/algorithm_overview.md) - [文本识别](./doc/doc_ch/algorithm_overview.md) - - PP-OCR (coming soon) + - [PP-OCR Pipline](#PP-OCR) - 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) - 预测部署 - - [基于Python预测引擎推理](./doc/doc_ch/inference.md) + - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md) + - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md) - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md) - [服务化部署](./doc/doc_ch/serving.md) - [端侧部署](./deploy/lite/readme.md) - - 模型量化压缩(coming soon) + - [模型量化](./deploy/slim/quantization/README.md) + - [模型裁剪](./deploy/slim/prune/README_ch.md) - [Benchmark](./doc/doc_ch/benchmark.md) - 数据集 - [通用中英文OCR数据集](./doc/doc_ch/datasets.md) @@ -101,12 +101,22 @@ 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) + +## PP-OCR Pipline +
+ +
+ +PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面,采用19个有效策略,对各个模块的模型进行效果调优和瘦身,最终得到整体大小为3.4M的超轻量中英文OCR和2M的英文数字OCR。更多细节请参考PP-OCR技术文章(Arxiv文章链接生成中)。 + + ## 效果展示 [more](./doc/doc_ch/visualization.md)
+ diff --git a/doc/doc_ch/update.md b/doc/doc_ch/update.md index 55442c8d..017cd947 100644 --- a/doc/doc_ch/update.md +++ b/doc/doc_ch/update.md @@ -1,6 +1,7 @@ # 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) +- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型,整体模型3.5M(详见[PP-OCR Pipline](#PP-OCR)),适合在移动端部署使用。[模型下载](#模型下载) +- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型,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](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/imgs_results/1106.jpg b/doc/imgs_results/1106.jpg new file 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