From 55bbc758156d029f720dbf8088ab9abfecbbab20 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Wed, 12 Oct 2022 14:52:33 +0800 Subject: [PATCH] add arxiv pps (#7893) * support reconaug * rename ppstructurev2 and add arxiv link * fix link --- README.md | 12 +++---- README_ch.md | 12 +++---- ppstructure/README.md | 17 +++++----- ppstructure/README_ch.md | 16 +++++----- ...tion.md => PP-StructureV2_introduction.md} | 30 +++++++++--------- ppstructure/docs/inference.md | 4 +-- ppstructure/docs/inference_en.md | 4 +-- ppstructure/docs/ppstructurev2_pipeline.png | Bin 1045704 -> 0 bytes ppstructure/kie/how_to_do_kie.md | 6 ++-- ppstructure/kie/how_to_do_kie_en.md | 6 ++-- ppstructure/table/README.md | 2 +- ppstructure/table/README_ch.md | 2 +- 12 files changed, 55 insertions(+), 56 deletions(-) rename ppstructure/docs/{PP-Structurev2_introduction.md => PP-StructureV2_introduction.md} (96%) delete mode 100644 ppstructure/docs/ppstructurev2_pipeline.png diff --git a/README.md b/README.md index 62cc8536..b8996346 100644 --- a/README.md +++ b/README.md @@ -27,11 +27,11 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools ## Recent updates - **🔥2022.8.24 Release PaddleOCR [release/2.6](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.6)** - - Release [PP-Structurev2](./ppstructure/),with functions and performance fully upgraded, adapted to Chinese scenes, and new support for [Layout Recovery](./ppstructure/recovery) and **one line command to convert PDF to Word**; + - Release [PP-StructureV2](./ppstructure/),with functions and performance fully upgraded, adapted to Chinese scenes, and new support for [Layout Recovery](./ppstructure/recovery) and **one line command to convert PDF to Word**; - [Layout Analysis](./ppstructure/layout) optimization: model storage reduced by 95%, while speed increased by 11 times, and the average CPU time-cost is only 41ms; - [Table Recognition](./ppstructure/table) optimization: 3 optimization strategies are designed, and the model accuracy is improved by 6% under comparable time consumption; - [Key Information Extraction](./ppstructure/kie) optimization:a visual-independent model structure is designed, the accuracy of semantic entity recognition is increased by 2.8%, and the accuracy of relation extraction is increased by 9.1%. - + - **🔥2022.7 Release [OCR scene application collection](./applications/README_en.md)** - Release **9 vertical models** such as digital tube, LCD screen, license plate, handwriting recognition model, high-precision SVTR model, etc, covering the main OCR vertical applications in general, manufacturing, finance, and transportation industries. @@ -129,7 +129,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel - [Text recognition](./doc/doc_en/algorithm_overview_en.md) - [End-to-end OCR](./doc/doc_en/algorithm_overview_en.md) - [Table Recognition](./doc/doc_en/algorithm_overview_en.md) - - [Key Information Extraction](./doc/doc_en/algorithm_overview_en.md) + - [Key Information Extraction](./doc/doc_en/algorithm_overview_en.md) - [Add New Algorithms to PaddleOCR](./doc/doc_en/add_new_algorithm_en.md) - Data Annotation and Synthesis - [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md) @@ -181,7 +181,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel
-PP-Structurev2 +PP-StructureV2 - layout analysis + table recognition
@@ -192,7 +192,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel
- +
@@ -204,7 +204,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel - RE (Relation Extraction)
-
+
diff --git a/README_ch.md b/README_ch.md index 24a925f6..f7338c07 100755 --- a/README_ch.md +++ b/README_ch.md @@ -28,14 +28,14 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力 ## 近期更新 - **🔥2022.8.24 发布 PaddleOCR [release/2.6](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.6)** - - 发布[PP-Structurev2](./ppstructure/),系统功能性能全面升级,适配中文场景,新增支持[版面复原](./ppstructure/recovery),支持**一行命令完成PDF转Word**; + - 发布[PP-StructureV2](./ppstructure/),系统功能性能全面升级,适配中文场景,新增支持[版面复原](./ppstructure/recovery),支持**一行命令完成PDF转Word**; - [版面分析](./ppstructure/layout)模型优化:模型存储减少95%,速度提升11倍,平均CPU耗时仅需41ms; - [表格识别](./ppstructure/table)模型优化:设计3大优化策略,预测耗时不变情况下,模型精度提升6%; - [关键信息抽取](./ppstructure/kie)模型优化:设计视觉无关模型结构,语义实体识别精度提升2.8%,关系抽取精度提升9.1%。 - + - **🔥2022.8 发布 [OCR场景应用集合](./applications)** - 包含数码管、液晶屏、车牌、高精度SVTR模型、手写体识别等**9个垂类模型**,覆盖通用,制造、金融、交通行业的主要OCR垂类应用。 - + - **2022.5.9 发布 PaddleOCR [release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)** - 发布[PP-OCRv3](./doc/doc_ch/ppocr_introduction.md#pp-ocrv3),速度可比情况下,中文场景效果相比于PP-OCRv2再提升5%,英文场景提升11%,80语种多语言模型平均识别准确率提升5%以上; - 发布半自动标注工具[PPOCRLabelv2](./PPOCRLabel):新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能; @@ -220,11 +220,11 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力
- +
- + - RE(关系提取)
@@ -237,7 +237,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力
- +
diff --git a/ppstructure/README.md b/ppstructure/README.md index f3f2d4a9..9d503ca8 100644 --- a/ppstructure/README.md +++ b/ppstructure/README.md @@ -15,15 +15,15 @@ English | [简体中文](README_ch.md) PP-Structure is an intelligent document analysis system developed by the PaddleOCR team, which aims to help developers better complete tasks related to document understanding such as layout analysis and table recognition. -The pipeline of PP-Structurev2 system is shown below. The document image first passes through the image direction correction module to identify the direction of the entire image and complete the direction correction. Then, two tasks of layout information analysis and key information extraction can be completed. +The pipeline of PP-StructureV2 system is shown below. The document image first passes through the image direction correction module to identify the direction of the entire image and complete the direction correction. Then, two tasks of layout information analysis and key information extraction can be completed. - In the layout analysis task, the image first goes through the layout analysis model to divide the image into different areas such as text, table, and figure, and then analyze these areas separately. For example, the table area is sent to the form recognition module for structured recognition, and the text area is sent to the OCR engine for text recognition. Finally, the layout recovery module restores it to a word or pdf file with the same layout as the original image; - In the key information extraction task, the OCR engine is first used to extract the text content, and then the SER(semantic entity recognition) module obtains the semantic entities in the image, and finally the RE(relationship extraction) module obtains the correspondence between the semantic entities, thereby extracting the required key information. - + -More technical details: 👉 [PP-Structurev2 Technical Report](docs/PP-Structurev2_introduction.md) +More technical details: 👉 [PP-StructureV2 Technical Report](https://arxiv.org/abs/2210.05391) -PP-Structurev2 supports independent use or flexible collocation of each module. For example, you can use layout analysis alone or table recognition alone. Click the corresponding link below to get the tutorial for each independent module: +PP-StructureV2 supports independent use or flexible collocation of each module. For example, you can use layout analysis alone or table recognition alone. Click the corresponding link below to get the tutorial for each independent module: - [Layout Analysis](layout/README.md) - [Table Recognition](table/README.md) @@ -32,7 +32,7 @@ PP-Structurev2 supports independent use or flexible collocation of each module. ## 2. Features -The main features of PP-Structurev2 are as follows: +The main features of PP-StructureV2 are as follows: - Support layout analysis of documents in the form of images/pdfs, which can be divided into areas such as **text, titles, tables, figures, formulas, etc.**; - Support common Chinese and English **table detection** tasks; - Support structured table recognition, and output the final result to **Excel file**; @@ -43,7 +43,7 @@ The main features of PP-Structurev2 are as follows: ## 3. Results -PP-Structurev2 supports the independent use or flexible collocation of each module. For example, layout analysis can be used alone, or table recognition can be used alone. Only the visualization effects of several representative usage methods are shown here. +PP-StructureV2 supports the independent use or flexible collocation of each module. For example, layout analysis can be used alone, or table recognition can be used alone. Only the visualization effects of several representative usage methods are shown here. ### 3.1 Layout analysis and table recognition @@ -59,7 +59,7 @@ The following figure shows the effect of layout recovery based on the results of * SER -Different colored boxes in the figure represent different categories. +Different colored boxes in the figure represent different categories.
@@ -91,7 +91,7 @@ In the figure, the red box represents `Question`, the blue box represents `Answe
-
+
@@ -114,4 +114,3 @@ For structural analysis related model downloads, please refer to: For OCR related model downloads, please refer to: - [PP-OCR Model Zoo](../doc/doc_en/models_list_en.md) - diff --git a/ppstructure/README_ch.md b/ppstructure/README_ch.md index 87a9c625..050740b3 100644 --- a/ppstructure/README_ch.md +++ b/ppstructure/README_ch.md @@ -16,14 +16,15 @@ PP-Structure是PaddleOCR团队自研的智能文档分析系统,旨在帮助开发者更好的完成版面分析、表格识别等文档理解相关任务。 -PP-Structurev2系统流程图如下所示,文档图像首先经过图像矫正模块,判断整图方向并完成转正,随后可以完成版面信息分析与关键信息抽取2类任务。 +PP-StructureV2系统流程图如下所示,文档图像首先经过图像矫正模块,判断整图方向并完成转正,随后可以完成版面信息分析与关键信息抽取2类任务。 - 版面分析任务中,图像首先经过版面分析模型,将图像划分为文本、表格、图像等不同区域,随后对这些区域分别进行识别,如,将表格区域送入表格识别模块进行结构化识别,将文本区域送入OCR引擎进行文字识别,最后使用版面恢复模块将其恢复为与原始图像布局一致的word或者pdf格式的文件; - 关键信息抽取任务中,首先使用OCR引擎提取文本内容,然后由语义实体识别模块获取图像中的语义实体,最后经关系抽取模块获取语义实体之间的对应关系,从而提取需要的关键信息。 - -更多技术细节:👉 [PP-Structurev2技术报告](docs/PP-Structurev2_introduction.md) + -PP-Structurev2支持各个模块独立使用或灵活搭配,如,可以单独使用版面分析,或单独使用表格识别,点击下面相应链接获取各个独立模块的使用教程: +更多技术细节:👉 PP-StructureV2技术报告 [中文版](docs/PP-StructureV2_introduction.md),[英文版](https://arxiv.org/abs/2210.05391)。 + +PP-StructureV2支持各个模块独立使用或灵活搭配,如,可以单独使用版面分析,或单独使用表格识别,点击下面相应链接获取各个独立模块的使用教程: - [版面分析](layout/README_ch.md) - [表格识别](table/README_ch.md) @@ -33,7 +34,7 @@ PP-Structurev2支持各个模块独立使用或灵活搭配,如,可以单独 ## 2. 特性 -PP-Structurev2的主要特性如下: +PP-StructureV2的主要特性如下: - 支持对图片/pdf形式的文档进行版面分析,可以划分**文字、标题、表格、图片、公式等**区域; - 支持通用的中英文**表格检测**任务; - 支持表格区域进行结构化识别,最终结果输出**Excel文件**; @@ -44,7 +45,7 @@ PP-Structurev2的主要特性如下: ## 3. 效果展示 -PP-Structurev2支持各个模块独立使用或灵活搭配,如,可以单独使用版面分析,或单独使用表格识别,这里仅展示几种代表性使用方式的可视化效果。 +PP-StructureV2支持各个模块独立使用或灵活搭配,如,可以单独使用版面分析,或单独使用表格识别,这里仅展示几种代表性使用方式的可视化效果。 ### 3.1 版面分析和表格识别 @@ -102,7 +103,7 @@ PP-Structurev2支持各个模块独立使用或灵活搭配,如,可以单独
-
+
## 4. 快速体验 @@ -119,4 +120,3 @@ PP-Structurev2支持各个模块独立使用或灵活搭配,如,可以单独 OCR相关模型下载可以参考: - [PP-OCR 模型库](../doc/doc_ch/models_list.md) - diff --git a/ppstructure/docs/PP-Structurev2_introduction.md b/ppstructure/docs/PP-StructureV2_introduction.md similarity index 96% rename from ppstructure/docs/PP-Structurev2_introduction.md rename to ppstructure/docs/PP-StructureV2_introduction.md index e337b563..efaf35f2 100644 --- a/ppstructure/docs/PP-Structurev2_introduction.md +++ b/ppstructure/docs/PP-StructureV2_introduction.md @@ -1,4 +1,4 @@ -# PP-Structurev2 +# PP-StructureV2 ## 目录 @@ -16,11 +16,11 @@ 现实场景中包含大量的文档图像,它们以图片等非结构化形式存储。基于文档图像的结构化分析与信息抽取对于数据的数字化存储以及产业的数字化转型至关重要。基于该考虑,PaddleOCR自研并发布了PP-Structure智能文档分析系统,旨在帮助开发者更好的完成版面分析、表格识别、关键信息抽取等文档理解相关任务。 -近期,PaddleOCR团队针对PP-Structurev1的版面分析、表格识别、关键信息抽取模块,进行了共计8个方面的升级,同时新增整图方向矫正、文档复原等功能,打造出一个全新的、效果更优的文档分析系统:PP-Structurev2。 +近期,PaddleOCR团队针对PP-Structurev1的版面分析、表格识别、关键信息抽取模块,进行了共计8个方面的升级,同时新增整图方向矫正、文档复原等功能,打造出一个全新的、效果更优的文档分析系统:PP-StructureV2。 ## 2. 简介 -PP-Structurev2在PP-Structurev1的基础上进一步改进,主要有以下3个方面升级: +PP-StructureV2在PP-Structurev1的基础上进一步改进,主要有以下3个方面升级: * **系统功能升级** :新增图像矫正和版面复原模块,图像转word/pdf、关键信息抽取能力全覆盖! * **系统性能优化** : @@ -29,7 +29,7 @@ PP-Structurev2在PP-Structurev1的基础上进一步改进,主要有以下3个 * 关键信息抽取:设计视觉无关模型结构,语义实体识别精度提升**2.8%**,关系抽取精度提升**9.1%**。 * **中文场景适配** :完成对版面分析与表格识别的中文场景适配,开源**开箱即用**的中文场景版面结构化模型! -PP-Structurev2系统流程图如下所示,文档图像首先经过图像矫正模块,判断整图方向并完成转正,随后可以完成版面信息分析与关键信息抽取2类任务。版面分析任务中,图像首先经过版面分析模型,将图像划分为文本、表格、图像等不同区域,随后对这些区域分别进行识别,如,将表格区域送入表格识别模块进行结构化识别,将文本区域送入OCR引擎进行文字识别,最后使用版面恢复模块将其恢复为与原始图像布局一致的word或者pdf格式的文件;关键信息抽取任务中,首先使用OCR引擎提取文本内容,然后由语义实体识别模块获取图像中的语义实体,最后经关系抽取模块获取语义实体之间的对应关系,从而提取需要的关键信息。 +PP-StructureV2系统流程图如下所示,文档图像首先经过图像矫正模块,判断整图方向并完成转正,随后可以完成版面信息分析与关键信息抽取2类任务。版面分析任务中,图像首先经过版面分析模型,将图像划分为文本、表格、图像等不同区域,随后对这些区域分别进行识别,如,将表格区域送入表格识别模块进行结构化识别,将文本区域送入OCR引擎进行文字识别,最后使用版面恢复模块将其恢复为与原始图像布局一致的word或者pdf格式的文件;关键信息抽取任务中,首先使用OCR引擎提取文本内容,然后由语义实体识别模块获取图像中的语义实体,最后经关系抽取模块获取语义实体之间的对应关系,从而提取需要的关键信息。
@@ -62,7 +62,7 @@ PP-Structurev2系统流程图如下所示,文档图像首先经过图像矫正 ## 3. 整图方向矫正 -由于训练集一般以正方向图像为主,旋转过的文档图像直接输入模型会增加识别难度,影响识别效果。PP-Structurev2引入了整图方向矫正模块来判断含文字图像的方向,并将其进行方向调整。 +由于训练集一般以正方向图像为主,旋转过的文档图像直接输入模型会增加识别难度,影响识别效果。PP-StructureV2引入了整图方向矫正模块来判断含文字图像的方向,并将其进行方向调整。 我们直接调用PaddleClas中提供的文字图像方向分类模型-[PULC_text_image_orientation](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/PULC/PULC_text_image_orientation.md),该模型部分数据集图像如下所示。不同于文本行方向分类器,文字图像方向分类模型针对整图进行方向判别。文字图像方向分类模型在验证集上精度高达99%,单张图像CPU预测耗时仅为`2.16ms`。 @@ -76,7 +76,7 @@ PP-Structurev2系统流程图如下所示,文档图像首先经过图像矫正 版面分析指的是对图片形式的文档进行区域划分,定位其中的关键区域,如文字、标题、表格、图片等,PP-Structurev1使用了PaddleDetection中开源的高效检测算法PP-YOLOv2完成版面分析的任务。 -在PP-Structurev2中,我们发布基于PP-PicoDet的轻量级版面分析模型,并针对版面分析场景定制图像尺度,同时使用FGD知识蒸馏算法,进一步提升模型精度。最终CPU上`41ms`即可完成版面分析过程(仅包含模型推理时间,数据预处理耗时大约50ms左右)。在公开数据集PubLayNet 上,消融实验如下: +在PP-StructureV2中,我们发布基于PP-PicoDet的轻量级版面分析模型,并针对版面分析场景定制图像尺度,同时使用FGD知识蒸馏算法,进一步提升模型精度。最终CPU上`41ms`即可完成版面分析过程(仅包含模型推理时间,数据预处理耗时大约50ms左右)。在公开数据集PubLayNet 上,消融实验如下: | 实验序号 | 策略 | 模型存储(M) | mAP | CPU预测耗时(ms) | |:------:|:------:|:------:|:------:|:------:| @@ -95,7 +95,7 @@ PP-Structurev2系统流程图如下所示,文档图像首先经过图像矫正 | 模型 | mAP | CPU预测耗时 | |-------------------|-----------|------------| | layoutparser (Detectron2) | 88.98% | 2.9s | -| PP-Structurev2 (PP-PicoDet) | **94%** | 41.2ms | +| PP-StructureV2 (PP-PicoDet) | **94%** | 41.2ms | [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet)数据集是一个大型的文档图像数据集,包含Text、Title、Tale、Figure、List,共5个类别。数据集中包含335,703张训练集、11,245张验证集和11,405张测试集。训练数据与标注示例图如下所示: @@ -157,7 +157,7 @@ FGD(Focal and Global Knowledge Distillation for Detectors),是一种兼顾 ### 4.2 表格识别 -基于深度学习的表格识别算法种类丰富,PP-Structurev1中,我们基于文本识别算法RARE研发了端到端表格识别算法TableRec-RARE,模型输出为表格结构的HTML表示,进而可以方便地转化为Excel文件。PP-Structurev2中,我们对模型结构和损失函数等5个方面进行升级,提出了 SLANet (Structure Location Alignment Network) ,模型结构如下图所示: +基于深度学习的表格识别算法种类丰富,PP-Structurev1中,我们基于文本识别算法RARE研发了端到端表格识别算法TableRec-RARE,模型输出为表格结构的HTML表示,进而可以方便地转化为Excel文件。PP-StructureV2中,我们对模型结构和损失函数等5个方面进行升级,提出了 SLANet (Structure Location Alignment Network) ,模型结构如下图所示:
@@ -189,7 +189,7 @@ FGD(Focal and Global Knowledge Distillation for Detectors),是一种兼顾 **(1) CPU友好型轻量级骨干网络PP-LCNet** -PP-LCNet是结合Intel-CPU端侧推理特性而设计的轻量高性能骨干网络,该方案在图像分类任务上取得了比ShuffleNetV2、MobileNetV3、GhostNet等轻量级模型更优的“精度-速度”均衡。PP-Structurev2中,我们采用PP-LCNet作为骨干网络,表格识别模型精度从71.73%提升至72.98%;同时加载通过SSLD知识蒸馏方案训练得到的图像分类模型权重作为表格识别的预训练模型,最终精度进一步提升2.95%至74.71%。 +PP-LCNet是结合Intel-CPU端侧推理特性而设计的轻量高性能骨干网络,该方案在图像分类任务上取得了比ShuffleNetV2、MobileNetV3、GhostNet等轻量级模型更优的“精度-速度”均衡。PP-StructureV2中,我们采用PP-LCNet作为骨干网络,表格识别模型精度从71.73%提升至72.98%;同时加载通过SSLD知识蒸馏方案训练得到的图像分类模型权重作为表格识别的预训练模型,最终精度进一步提升2.95%至74.71%。 **(2)轻量级高低层特征融合模块CSP-PAN** @@ -199,7 +199,7 @@ PP-LCNet是结合Intel-CPU端侧推理特性而设计的轻量高性能骨干网 TableRec-RARE的TableAttentionHead如下图a所示,TableAttentionHead在执行完全部step的计算后拿到最终隐藏层状态表征(hiddens),随后hiddens经由SDM(Structure Decode Module)和CLDM(Cell Location Decode Module)模块生成全部的表格结构token和单元格坐标。但是这种设计忽略了单元格token和坐标之间一一对应的关系。 -PP-Structurev2中,我们设计SLAHead模块,对单元格token和坐标之间做了对齐操作,如下图b所示。在SLAHead中,每一个step的隐藏层状态表征会分别送入SDM和CLDM来得到当前step的token和坐标,每个step的token和坐标输出分别进行concat得到表格的html表达和全部单元格的坐标。此外,考虑到表格识别模型的单元格准确率依赖于表格结构的识别准确,我们将损失函数中表格结构分支与单元格定位分支的权重比从1:1提升到8:1,并使用收敛更稳定的Smoothl1 Loss替换定位分支中的MSE Loss。最终模型精度从75.68%提高至77.7%。 +PP-StructureV2中,我们设计SLAHead模块,对单元格token和坐标之间做了对齐操作,如下图b所示。在SLAHead中,每一个step的隐藏层状态表征会分别送入SDM和CLDM来得到当前step的token和坐标,每个step的token和坐标输出分别进行concat得到表格的html表达和全部单元格的坐标。此外,考虑到表格识别模型的单元格准确率依赖于表格结构的识别准确,我们将损失函数中表格结构分支与单元格定位分支的权重比从1:1提升到8:1,并使用收敛更稳定的Smoothl1 Loss替换定位分支中的MSE Loss。最终模型精度从75.68%提高至77.7%。
@@ -211,7 +211,7 @@ PP-Structurev2中,我们设计SLAHead模块,对单元格token和坐标之间 TableRec-RARE算法中,我们使用``和``两个单独的token来表示一个非跨行列单元格,这种表示方式限制了网络对于单元格数量较多表格的处理能力。 -PP-Structurev2中,我们参考TableMaster中的token处理方法,将``和``合并为一个token-``。合并token后,验证集中token长度大于500的图片也参与模型评估,最终模型精度降低为76.31%,但是端到端TEDS提升1.04%。 +PP-StructureV2中,我们参考TableMaster中的token处理方法,将``和``合并为一个token-``。合并token后,验证集中token长度大于500的图片也参与模型评估,最终模型精度降低为76.31%,但是端到端TEDS提升1.04%。 #### 4.2.2 中文场景适配 @@ -249,7 +249,7 @@ PP-Structurev2中,我们参考TableMaster中的token处理方法,将`` ### 4.3 版面恢复 -版面恢复指的是文档图像经过OCR识别、版面分析、表格识别等方法处理后的内容可以与原始文档保持相同的排版方式,并输出到word等文档中。PP-Structurev2中,我们版面恢复系统,包含版面分析、表格识别、OCR文本检测与识别等子模块。 +版面恢复指的是文档图像经过OCR识别、版面分析、表格识别等方法处理后的内容可以与原始文档保持相同的排版方式,并输出到word等文档中。PP-StructureV2中,我们版面恢复系统,包含版面分析、表格识别、OCR文本检测与识别等子模块。 下图展示了版面恢复的结果:
@@ -258,7 +258,7 @@ PP-Structurev2中,我们参考TableMaster中的token处理方法,将`` ## 5. 关键信息抽取 -关键信息抽取指的是针对文档图像的文字内容,提取出用户关注的关键信息,如身份证中的姓名、住址等字段。PP-Structure中支持了基于多模态LayoutLM系列模型的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。PP-Structurev2中,我们对模型结构以及下游任务训练方法进行升级,提出了VI-LayoutXLM(Visual-feature Independent LayoutXLM),具体流程图如下所示。 +关键信息抽取指的是针对文档图像的文字内容,提取出用户关注的关键信息,如身份证中的姓名、住址等字段。PP-Structure中支持了基于多模态LayoutLM系列模型的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。PP-StructureV2中,我们对模型结构以及下游任务训练方法进行升级,提出了VI-LayoutXLM(Visual-feature Independent LayoutXLM),具体流程图如下所示。
@@ -394,7 +394,7 @@ RE任务的可视化结果如下所示。 | 实验序号 | 策略 | F1-score | |:------:|:------:|:------:| | 1 | LayoutXLM | 82.28% | -| 2 | PP-Structurev2 SER | **87.79%** | +| 2 | PP-StructureV2 SER | **87.79%** | **RE任务结果** @@ -402,7 +402,7 @@ RE任务的可视化结果如下所示。 | 实验序号 | 策略 | F1-score | |:------:|:------:|:------:| | 1 | LayoutXLM | 53.13% | -| 2 | PP-Structurev2 SER | **74.87%** | +| 2 | PP-StructureV2 SER | **74.87%** | ## 6. Reference diff --git a/ppstructure/docs/inference.md b/ppstructure/docs/inference.md index 7aa2fd0d..a7efd5e8 100644 --- a/ppstructure/docs/inference.md +++ b/ppstructure/docs/inference.md @@ -18,13 +18,13 @@ cd ppstructure 下载模型 ```bash mkdir inference && cd inference -# 下载PP-Structurev2版面分析模型并解压 +# 下载PP-StructureV2版面分析模型并解压 wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout_infer.tar && tar xf picodet_lcnet_x1_0_layout_infer.tar # 下载PP-OCRv3文本检测模型并解压 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar # 下载PP-OCRv3文本识别模型并解压 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar && tar xf ch_PP-OCRv3_rec_infer.tar -# 下载PP-Structurev2表格识别模型并解压 +# 下载PP-StructureV2表格识别模型并解压 wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf ch_ppstructure_mobile_v2.0_SLANet_infer.tar cd .. ``` diff --git a/ppstructure/docs/inference_en.md b/ppstructure/docs/inference_en.md index 1bb683a6..69ca0b20 100644 --- a/ppstructure/docs/inference_en.md +++ b/ppstructure/docs/inference_en.md @@ -20,13 +20,13 @@ download model ```bash mkdir inference && cd inference -# Download the PP-Structurev2 layout analysis model and unzip it +# Download the PP-StructureV2 layout analysis model and unzip it wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout_infer.tar && tar xf picodet_lcnet_x1_0_layout_infer.tar # Download the PP-OCRv3 text detection model and unzip it wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar # Download the PP-OCRv3 text recognition model and unzip it wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar && tar xf ch_PP-OCRv3_rec_infer.tar -# Download the PP-Structurev2 form recognition model and unzip it +# Download the PP-StructureV2 form recognition model and unzip it wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf ch_ppstructure_mobile_v2.0_SLANet_infer.tar cd .. ``` diff --git a/ppstructure/docs/ppstructurev2_pipeline.png b/ppstructure/docs/ppstructurev2_pipeline.png deleted file mode 100644 index b53a290a6dbc396449374cc694dd01c304325739..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1045704 zcmeFYWl&sA_b-aOyK8U@u3-r7?iwJtYk&ZQyCwv8cMIX_ipa`Lf>!ksG#88Kf$8he7GGN+!x8+m%dqC|wM*-@~7hr;^dQ!xs!PM3{dmyL#i zbj3_JrM-P7pIO!y4s!U0BY@m-Zu=>5ASZ;);{&ZhiVO-yM{SMasq0?<060tugva#^`ljqgo z`UVeH)P6fR>N7U-7}yluXc_1OrMQn~CO|)_p0m$yDGS zECNZO4x#5vVP4Sft_|s*IRQnydCnN{Xr=@zbB5 z$(rdhNON6ozz8AT?Q9vr4u%v02E!2BkzLRc%;<``r zPG6)5%262~kWI%Hp`bn(Nw|MWv#WWvH7>@p6@b+Pha$k$E13X0P-TQ}beUpHk9HY= z_8FGiS=$alyYPNmc8eDqFAzY51M{@iwgu6nW(;}e2-g4gi}>^1G{Jq%2)qjQ6rvgx zCPe$T<{O%i0<=o5G#K+jMBwrLhKNAY&8smW_odiWLa9N&far2$fu#0b7>+~)_Q3mm 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