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# PP-Structure 系列模型列表
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- [PP-Structure 系列模型列表](#pp-structure-系列模型列表)
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- [1. 版面分析模型](#1-版面分析模型)
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- [2. OCR和表格识别模型](#2-ocr和表格识别模型)
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- [2.1 OCR](#21-ocr)
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- [2.2 表格识别模型](#22-表格识别模型)
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- [3. VQA模型](#3-vqa模型)
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- [4. KIE模型](#4-kie模型)
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- [1. 版面分析模型](#1-版面分析模型)
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- [2. OCR和表格识别模型](#2-ocr和表格识别模型)
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- [2.1 OCR](#21-ocr)
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- [2.2 表格识别模型](#22-表格识别模型)
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- [3. VQA模型](#3-vqa模型)
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- [4. KIE模型](#4-kie模型)
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<a name="1"></a>
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# PP-Structure Model list
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- [PP-Structure Model list](#pp-structure-model-list)
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- [1. Layout Analysis](#1-layout-analysis)
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- [2. OCR and Table Recognition](#2-ocr-and-table-recognition)
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- [2.1 OCR](#21-ocr)
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- [2.2 Table Recognition](#22-table-recognition)
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- [3. VQA](#3-vqa)
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- [4. KIE](#4-kie)
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- [1. Layout Analysis](#1-layout-analysis)
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- [2. OCR and Table Recognition](#2-ocr-and-table-recognition)
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- [2.1 OCR](#21-ocr)
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- [2.2 Table Recognition](#22-table-recognition)
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- [3. VQA](#3-vqa)
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- [4. KIE](#4-kie)
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<a name="1"></a>
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python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../models/ser_LayoutXLM_xfun_zh/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --image_dir=docs/vqa/input/zh_val_42.jpg
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python3.7 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=models/re_LayoutXLM_xfun_zh/ Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=models/ser_LayoutXLM_xfun_zh/
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English | [简体中文](README_ch.md)
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- [Document Visual Question Answering](#document-visual-question-answering)
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- [1 Introduction](#1-introduction)
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- [2. Performance](#2-performance)
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- [3. Effect demo](#3-effect-demo)
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- [3.1 SER](#31-ser)
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- [3.2 RE](#32-re)
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- [4. Install](#4-install)
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- [4.1 Install dependencies](#41-install-dependencies)
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- [5.3 RE](#53-re)
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- [6. Reference Links](#6-reference-links)
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- [License](#license)
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- [1 Introduction](#1-introduction)
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- [2. Performance](#2-performance)
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- [3. Effect demo](#3-effect-demo)
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- [3.1 SER](#31-ser)
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- [3.2 RE](#32-re)
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- [4. Install](#4-install)
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- [4.1 Install dependencies](#41-install-dependencies)
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- [5.3 RE](#53-re)
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- [6. Reference Links](#6-reference-links)
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- [License](#license)
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# Document Visual Question Answering
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The converted model will be stored in the directory specified by the `Global.save_inference_dir` field.
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* `OCR + SER` tandem prediction based on prediction engine
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Use the following command to complete the tandem prediction of `OCR + SER` based on the prediction engine, taking the SER model based on LayoutXLM as an example:
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```shell
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@ -268,7 +267,7 @@ python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Archi
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Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`.
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* export model
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cooming soon
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* `OCR + SER + RE` tandem prediction based on prediction engine
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[English](README.md) | 简体中文
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- [文档视觉问答（DOC-VQA）](#文档视觉问答doc-vqa)
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- [1. 简介](#1-简介)
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- [2. 性能](#2-性能)
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- [3. 效果演示](#3-效果演示)
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- [3.1 SER](#31-ser)
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- [3.2 RE](#32-re)
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- [4. 安装](#4-安装)
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- [4.1 安装依赖](#41-安装依赖)
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- [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa)
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- [5. 使用](#5-使用)
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- [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
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- [5.2 SER](#52-ser)
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- [5.3 RE](#53-re)
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- [6. 参考链接](#6-参考链接)
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- [License](#license)
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- [1. 简介](#1-简介)
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- [2. 性能](#2-性能)
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- [3. 效果演示](#3-效果演示)
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- [3.1 SER](#31-ser)
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- [3.2 RE](#32-re)
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- [4. 安装](#4-安装)
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- [4.1 安装依赖](#41-安装依赖)
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- [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa)
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- [5. 使用](#5-使用)
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- [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
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- [5.2 SER](#52-ser)
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- [5.3 RE](#53-re)
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- [6. 参考链接](#6-参考链接)
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- [License](#license)
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# 文档视觉问答（DOC-VQA）
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转换后的模型会存放在`Global.save_inference_dir`字段指定的目录下。
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* 基于预测引擎的`OCR + SER`串联预测
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使用如下命令即可完成基于预测引擎的`OCR + SER`的串联预测, 以基于LayoutXLM的SER模型为例:
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```shell
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最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
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* 模型导出
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cooming soon
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* 基于预测引擎的`OCR + SER + RE`串联预测
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