init commit

deploy/Jetson/readme.md

+English | [简体中文](readme_ch.md)
+
+# Jetson Deployment for PaddleOCR
+
+This section introduces the deployment of PaddleOCR on Jetson NX, TX2, nano, AGX and other series of hardware.
+
+
+## 1. Prepare Environment
+
+You need to prepare a Jetson development hardware. If you need TensorRT, you need to prepare the TensorRT environment. It is recommended to use TensorRT version 7.1.3;
+
+1. Install PaddlePaddle in Jetson
+
+The PaddlePaddle download [link](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
+Please select the appropriate installation package for your Jetpack version, cuda version, and trt version. Here, we download paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl.
+
+Install PaddlePaddle：
+```shell
+pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl
+```
+
+
+2. Download PaddleOCR code and install dependencies
+
+Clone the PaddleOCR code:
+```
+git clone https://github.com/PaddlePaddle/PaddleOCR
+```
+
+and install dependencies：
+```
+cd PaddleOCR
+pip3 install -r requirements.txt
+```
+
+*Note: Jetson hardware CPU is poor, dependency installation is slow, please wait patiently*
+
+## 2. Perform prediction
+
+Obtain the PPOCR model from the [document](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/ppocr_introduction_en.md#6-model-zoo) model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on Jetson:
+
+Download and unzip the PP-OCRv3 models.
+```
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar xf ch_PP-OCRv3_det_infer.tar
+tar xf ch_PP-OCRv3_rec_infer.tar
+```
+
+The text detection inference:
+```
+cd PaddleOCR
+python3 tools/infer/predict_det.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/  --image_dir=./doc/imgs/french_0.jpg  --use_gpu=True
+```
+
+After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the `./inference_results/` directory.
+![](./images/det_res_french_0.jpg)
+
+
+The text recognition inference:
+```
+python3 tools/infer/predict_det.py --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/  --image_dir=./doc/imgs_words/en/word_2.png  --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+After executing the command, the predicted information will be printed on the terminal, and the output is as follows:
+```
+[2022/04/28 15:41:45] root INFO: Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.98084533)
+```
+
+The text  detection and text recognition inference:
+
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/00057937.jpg --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the `./inference_results/` directory.
+![](./images/00057937.jpg)
+
+To enable TRT prediction, you only need to set `--use_tensorrt=True` on the basis of the above command:
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/  --rec_image_shape="3,48,320" --use_gpu=True --use_tensorrt=True
+```
+
+For more ppocr model predictions, please refer to[document](../../doc/doc_en/models_list_en.md)

deploy/Jetson/readme_ch.md

+[English](readme.md) | 简体中文
+
+# Jetson部署PaddleOCR模型
+
+本节介绍PaddleOCR在Jetson NX、TX2、nano、AGX等系列硬件的部署。
+
+
+## 1. 环境准备
+
+需要准备一台Jetson开发板，如果需要TensorRT预测，需准备好TensorRT环境，建议使用7.1.3版本的TensorRT；
+
+1. Jetson安装PaddlePaddle
+
+PaddlePaddle下载[链接](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
+请选择适合的您Jetpack版本、cuda版本、trt版本的安装包。
+
+安装命令：
+```shell
+# 安装paddle，以paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl 为例
+pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl
+```
+
+
+2. 下载PaddleOCR代码并安装依赖
+
+首先 clone PaddleOCR 代码：
+```
+git clone https://github.com/PaddlePaddle/PaddleOCR
+```
+
+然后，安装依赖：
+```
+cd PaddleOCR
+pip3 install -r requirements.txt
+```
+
+*注：jetson硬件CPU较差，依赖安装较慢，请耐心等待*
+
+
+## 2. 执行预测
+
+从[文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/ppocr_introduction.md#6-%E6%A8%A1%E5%9E%8B%E5%BA%93) 模型库中获取PPOCR模型，下面以PP-OCRv3模型为例，介绍在PPOCR模型在jetson上的使用方式：
+
+下载并解压PP-OCRv3模型
+```
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar xf ch_PP-OCRv3_det_infer.tar
+tar xf ch_PP-OCRv3_rec_infer.tar
+```
+
+执行文本检测预测：
+```
+cd PaddleOCR
+python3 tools/infer/predict_det.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/  --image_dir=./doc/imgs/french_0.jpg  --use_gpu=True
+```
+
+执行命令后在终端会打印出预测的信息，并在 `./inference_results/` 下保存可视化结果。
+![](./images/det_res_french_0.jpg)
+
+
+执行文本识别预测：
+```
+python3 tools/infer/predict_det.py --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/  --image_dir=./doc/imgs_words/en/word_2.png  --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+执行命令后在终端会打印出预测的信息，输出如下：
+```
+[2022/04/28 15:41:45] root INFO: Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.98084533)
+```
+
+执行文本检测+文本识别串联预测：
+
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/ --use_gpu=True --rec_image_shape="3,48,320"
+```
+
+执行命令后在终端会打印出预测的信息，并在 `./inference_results/` 下保存可视化结果。
+![](./images/00057937.jpg)
+
+开启TRT预测只需要在以上命令基础上设置`--use_tensorrt=True`即可：
+```
+python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/00057937.jpg --use_gpu=True --use_tensorrt=True --rec_image_shape="3,48,320"
+```
+
+更多ppocr模型预测请参考[文档](../../doc/doc_ch/models_list.md)

deploy/README.md

+English | [简体中文](README_ch.md)
+
+# PP-OCR Deployment
+
+- [Paddle Deployment Introduction](#1)
+- [PP-OCR Deployment](#2)
+
+<a name="1"></a>
+## Paddle Deployment Introduction
+
+Paddle provides a variety of deployment schemes to meet the deployment requirements of different scenarios. Please choose according to the actual situation: 
+
+<div align="center">
+    <img src="../doc/deployment_en.png" width="800">
+</div>
+
+
+<a name="2"></a>
+## PP-OCR Deployment
+
+PP-OCR has supported muti deployment schemes. Click the link to get the specific tutorial.
+
+- [Python Inference](../doc/doc_en/inference_ppocr_en.md)
+- [C++ Inference](./cpp_infer/readme.md)
+- [Serving (Python/C++)](./pdserving/README.md)
+- [Paddle-Lite (ARM CPU/OpenCL ARM GPU)](./lite/readme.md)
+- [Paddle.js](./paddlejs/README.md)
+- [Jetson Inference](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/Jetson/readme.md)
+- [Paddle2ONNX](./paddle2onnx/readme.md)
+
+If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。

deploy/README_ch.md

+[English](README.md) | 简体中文
+
+# PP-OCR 模型推理部署
+
+- [Paddle 推理部署方式简介](#1)
+- [PP-OCR 推理部署](#2)
+
+<a name="1"></a>
+## Paddle 推理部署方式简介
+
+飞桨提供多种部署方案，以满足不同场景的部署需求，请根据实际情况进行选择：
+
+<div align="center">
+    <img src="../doc/deployment.png" width="800">
+</div>
+
+
+<a name="2"></a>
+## PP-OCR 推理部署
+
+PP-OCR模型已打通多种场景部署方案，点击链接获取具体的使用教程。
+
+- [Python 推理](../doc/doc_ch/inference_ppocr.md)
+- [C++ 推理](./cpp_infer/readme_ch.md)
+- [Serving 服务化部署（Python/C++）](./pdserving/README_CN.md)
+- [Paddle-Lite 端侧部署（ARM CPU/OpenCL ARM GPU）](./lite/readme_ch.md)
+- [Paddle.js 部署](./paddlejs/README_ch.md)
+- [Jetson 推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/Jetson/readme_ch.md)
+- [Paddle2ONNX 推理](./paddle2onnx/readme_ch.md)
+
+需要PP-OCR以外的学术算法模型的推理部署，请直接进入相应算法主页面，[入口](../doc/doc_ch/algorithm_overview.md)。
\ No newline at end of file

deploy/android_demo/.gitignore

+*.iml
+.gradle
+/local.properties
+/.idea/*
+.DS_Store
+/build
+/captures
+.externalNativeBuild
+

deploy/android_demo/README.md

+- [Android Demo](#android-demo)
+  - [1. 简介](#1-简介)
+  - [2. 近期更新](#2-近期更新)
+  - [3. 快速使用](#3-快速使用)
+    - [3.1 环境准备](#31-环境准备)
+    - [3.2 导入项目](#32-导入项目)
+    - [3.3 运行demo](#33-运行demo)
+    - [3.4 运行模式](#34-运行模式)
+    - [3.5 设置](#35-设置)
+  - [4 更多支持](#4-更多支持)
+
+# Android Demo
+
+## 1. 简介
+此为PaddleOCR的Android Demo，目前支持文本检测，文本方向分类器和文本识别模型的使用。使用 [PaddleLite v2.10](https://github.com/PaddlePaddle/Paddle-Lite/tree/release/v2.10) 进行开发。
+
+## 2. 近期更新
+* 2022.02.27
+    * 预测库更新到PaddleLite v2.10
+    * 支持6种运行模式：
+      * 检测+分类+识别
+      * 检测+识别
+      * 分类+识别
+      * 检测
+      * 识别
+      * 分类
+
+## 3. 快速使用
+
+### 3.1 环境准备
+1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
+2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`
+
+**注意**：如果您的 Android Studio 尚未配置 NDK ，请根据 Android Studio 用户指南中的[安装及配置 NDK 和 CMake ](https://developer.android.com/studio/projects/install-ndk)内容，预先配置好 NDK 。您可以选择最新的 NDK 版本，或者使用 Paddle Lite 预测库版本一样的 NDK
+
+### 3.2 导入项目
+
+点击 File->New->Import Project...， 然后跟着Android Studio的引导导入
+导入完成后呈现如下界面
+![](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/import_demo.jpg)
+
+### 3.3 运行demo
+将手机连接上电脑后，点击Android Studio工具栏中的运行按钮即可运行demo。在此过程中，手机会弹出"允许从 USB 安装软件权限"的弹窗，点击允许即可。
+
+软件安转到手机上后会在手机主屏最后一页看到如下app
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/install_finish.jpeg" width="400">
+</div>
+
+点击app图标即可启动app，启动后app主页如下
+
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/main_page.jpg" width="400">
+</div>
+
+app主页中有四个按钮，一个下拉列表和一个菜单按钮，他们的功能分别为
+
+* 运行模型：按照已选择的模式，运行对应的模型组合
+* 拍照识别：唤起手机相机拍照并获取拍照的图像，拍照完成后需要点击运行模型进行识别
+* 选取图片：唤起手机相册拍照选择图像，选择完成后需要点击运行模型进行识别
+* 清空绘图：清空当前显示图像上绘制的文本框，以便进行下一次识别(每次识别使用的图像都是当前显示的图像)
+* 下拉列表：进行运行模式的选择，目前包含6种运行模式，默认模式为**检测+分类+识别**详细说明见下一节。
+* 菜单按钮：点击后会进入菜单界面，进行模型和内置图像有关设置
+
+点击运行模型后，会按照所选择的模式运行对应的模型，**检测+分类+识别**模式下运行的模型结果如下所示：
+
+<img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400">
+
+模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model successed`表明模型运行成功。
+
+模型的运行结果显示在运行结果显示区，显示格式为
+```text
+序号：Det：(x1,y1)(x2,y2)(x3,y3)(x4,y4) Rec: 识别文本,识别置信度 Cls：分类类别,分类分时
+```
+
+### 3.4 运行模式
+
+PaddleOCR demo共提供了6种运行模式，如下图
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/select_mode.jpg" width="400">
+</div>
+
+每种模式的运行结果如下表所示
+
+| 检测+分类+识别                                                                                       | 检测+识别                                                                                      | 分类+识别                                                                                      |
+|------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|
+| <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_cls_rec.jpg" width="400"> |
+
+
+| 检测                                                                                     | 识别                                                                                     | 分类                                                                                     |
+|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
+| <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_cls.jpg" width="400"> |
+
+### 3.5 设置
+
+设置界面如下
+
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/settings.jpg" width="400">
+</div>
+
+在设置界面可以进行如下几项设定：
+1. 普通设置
+   * Enable custom settings: 选中状态下才能更改设置
+   * Model Path: 所运行的模型地址，使用默认值就好
+   * Label Path: 识别模型的字典
+   * Image Path: 进行识别的内置图像名
+2. 模型运行态设置，此项设置更改后返回主界面时，会自动重新加载模型
+   * CPU Thread Num: 模型运行使用的CPU核心数量
+   * CPU Power Mode: 模型运行模式，大小核设定
+3. 输入设置
+   * det long size: DB模型预处理时图像的长边长度，超过此长度resize到该值，短边进行等比例缩放，小于此长度不进行处理。
+4. 输出设置
+   * Score Threshold: DB模型后处理box的阈值，低于此阈值的box进行过滤，不显示。
+
+## 4 更多支持
+1. 实时识别，更新预测库可参考 https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/ocr/android/app/cxx/ppocr_demo
+2. 更多Paddle-Lite相关问题可前往[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) ，获得更多开发支持

deploy/android_demo/app/.gitignore

+/build

deploy/android_demo/app/build.gradle

+import java.security.MessageDigest
+
+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 29
+    defaultConfig {
+        applicationId "com.baidu.paddle.lite.demo.ocr"
+        minSdkVersion 23
+        targetSdkVersion 29
+        versionCode 2
+        versionName "2.0"
+        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
+        externalNativeBuild {
+            cmake {
+                cppFlags "-std=c++11 -frtti -fexceptions -Wno-format"
+                arguments '-DANDROID_PLATFORM=android-23', '-DANDROID_STL=c++_shared' ,"-DANDROID_ARM_NEON=TRUE"
+            }
+        }
+    }
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+    externalNativeBuild {
+        cmake {
+            path "src/main/cpp/CMakeLists.txt"
+            version "3.10.2"
+        }
+    }
+}
+
+dependencies {
+    implementation fileTree(include: ['*.jar'], dir: 'libs')
+    implementation 'androidx.appcompat:appcompat:1.1.0'
+    implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
+    testImplementation 'junit:junit:4.12'
+    androidTestImplementation 'com.android.support.test:runner:1.0.2'
+    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
+}
+
+def archives = [
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/libs/paddle_lite_libs_v2_10.tar.gz',
+                'dest': 'PaddleLite'
+        ],
+        [
+                'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/opencv-4.2.0-android-sdk.tar.gz',
+                'dest': 'OpenCV'
+        ],
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2.tar.gz',
+                'dest' : 'src/main/assets/models'
+        ],
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_dict.tar.gz',
+                'dest' : 'src/main/assets/labels'
+        ]
+]
+
+task downloadAndExtractArchives(type: DefaultTask) {
+    doFirst {
+        println "Downloading and extracting archives including libs and models"
+    }
+    doLast {
+        // Prepare cache folder for archives
+        String cachePath = "cache"
+        if (!file("${cachePath}").exists()) {
+            mkdir "${cachePath}"
+        }
+        archives.eachWithIndex { archive, index ->
+            MessageDigest messageDigest = MessageDigest.getInstance('MD5')
+            messageDigest.update(archive.src.bytes)
+            String cacheName = new BigInteger(1, messageDigest.digest()).toString(32)
+            // Download the target archive if not exists
+            boolean copyFiles = !file("${archive.dest}").exists()
+            if (!file("${cachePath}/${cacheName}.tar.gz").exists()) {
+                ant.get(src: archive.src, dest: file("${cachePath}/${cacheName}.tar.gz"))
+                copyFiles = true; // force to copy files from the latest archive files
+            }
+            // Extract the target archive if its dest path does not exists
+            if (copyFiles) {
+                copy {
+                    from tarTree("${cachePath}/${cacheName}.tar.gz")
+                    into "${archive.dest}"
+                }
+            }
+        }
+    }
+}
+preBuild.dependsOn downloadAndExtractArchives
\ No newline at end of file

deploy/android_demo/app/proguard-rules.pro

+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

deploy/android_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ocr/ExampleInstrumentedTest.java

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.support.test.InstrumentationRegistry;
+import android.support.test.runner.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getTargetContext();
+
+        assertEquals("com.baidu.paddle.lite.demo", appContext.getPackageName());
+    }
+}

deploy/android_demo/app/src/main/AndroidManifest.xml

+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+          package="com.baidu.paddle.lite.demo.ocr">
+
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.CAMERA"/>
+
+
+    <application
+            android:allowBackup="true"
+            android:icon="@mipmap/ic_launcher"
+            android:label="@string/app_name"
+            android:roundIcon="@mipmap/ic_launcher_round"
+            android:supportsRtl="true"
+            android:theme="@style/AppTheme">
+        <activity android:name="com.baidu.paddle.lite.demo.ocr.MainActivity">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN"/>
+                <category android:name="android.intent.category.LAUNCHER"/>
+            </intent-filter>
+        </activity>
+        <activity
+                android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
+                android:label="Settings">
+        </activity>
+        <provider
+            android:name="androidx.core.content.FileProvider"
+            android:authorities="com.baidu.paddle.lite.demo.ocr.fileprovider"
+            android:exported="false"
+            android:grantUriPermissions="true">
+            <meta-data
+                android:name="android.support.FILE_PROVIDER_PATHS"
+                android:resource="@xml/file_paths"></meta-data>
+        </provider>
+    </application>
+
+</manifest>
\ No newline at end of file