diff --git a/deploy/lite/readme_en.md b/deploy/lite/readme_en.md
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--- a/deploy/lite/readme_en.md
+++ b/deploy/lite/readme_en.md
@@ -1,275 +1,130 @@
-# Tutorial of PaddleOCR Mobile deployment
-
-This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy paddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
-
-paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoTs, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
-
-## 1. Preparation
-
-### 运行准备
-
-- Computer (for Compiling Paddle Lite)
-- Mobile phone (arm7 or arm8)
-
-### 1.1 Prepare the cross-compilation environment
-The cross-compilation environment is used to compile C++ demos of Paddle Lite and PaddleOCR.
-Supports multiple development environments.
-
-For the compilation process of different development environments, please refer to the corresponding documents.
-
-1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
-2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
-3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
-
-### 1.2 Prepare Paddle-Lite library
-
-There are two ways to obtain the Paddle-Lite library：
-- 1. Download directly, the download link of the Paddle-Lite library is as follows：
-
- | Platform | Paddle-Lite library download link |
- |---|---|
- |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
- |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
-
- Note: 1. The above Paddle-Lite library is compiled from the Paddle-Lite 2.9 branch. For more information about Paddle-Lite 2.9, please refer to [link](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.9).
-
-- 2. [Recommended] Compile Paddle-Lite to get the prediction library. The compilation method of Paddle-Lite is as follows：
-```
-git clone https://github.com/PaddlePaddle/Paddle-Lite.git
-cd Paddle-Lite
-# Switch to Paddle-Lite release/v2.8 stable branch
-git checkout release/v2.8
-./lite/tools/build_android.sh --arch=armv8 --with_cv=ON --with_extra=ON
-```
-
-Note: When compiling Paddle-Lite to obtain the Paddle-Lite library, you need to turn on the two options `--with_cv=ON --with_extra=ON`, `--arch` means the `arm` version, here is designated as armv8,
-
-More compilation commands refer to the introduction [link](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html) 。
-
-After directly downloading the Paddle-Lite library and decompressing it, you can get the `inference_lite_lib.android.armv8/` folder, and the Paddle-Lite library obtained by compiling Paddle-Lite is located
-`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/` folder.
-
-The structure of the prediction library is as follows:
-```
-inference_lite_lib.android.armv8/
-|-- cxx C++ prebuild library
-| |-- include C++
-| | |-- paddle_api.h
-| | |-- paddle_image_preprocess.h
-| | |-- paddle_lite_factory_helper.h
-| | |-- paddle_place.h
-| | |-- paddle_use_kernels.h
-| | |-- paddle_use_ops.h
-| | `-- paddle_use_passes.h
-| `-- lib C++ library
-| |-- libpaddle_api_light_bundled.a C++ static library
-| `-- libpaddle_light_api_shared.so C++ dynamic library
-|-- java Java library
-| |-- jar
-| | `-- PaddlePredictor.jar
-| |-- so
-| | `-- libpaddle_lite_jni.so
-| `-- src
-|-- demo C++ and Java demo
-| |-- cxx C++ demo
-| `-- java Java demo
-```
-
-## 2 Run
-
-### 2.1 Inference Model Optimization
-
-Paddle Lite provides a variety of strategies to automatically optimize the original training model, including quantization, sub-graph fusion, hybrid scheduling, Kernel optimization and so on. In order to make the optimization process more convenient and easy to use, Paddle Lite provide opt tools to automatically complete the optimization steps and output a lightweight, optimal executable model.
-
-If you have prepared the model file ending in .nb, you can skip this step.
-
-The following table also provides a series of models that can be deployed on mobile phones to recognize Chinese. You can directly download the optimized model.
-
+## OCR model list（V2.0, updated on 2021.1.20）
+> **Note**
+> 1. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
+> 2. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
+
+- [1. Text Detection Model](#Detection)
+- [2. Text Recognition Model](#Recognition)
+ - [Chinese Recognition Model](#Chinese)
+ - [English Recognition Model](#English)
+ - [Multilingual Recognition Model](#Multilingual)
+- [3. Text Angle Classification Model](#Angle)
+- [4. Paddle-Lite Model](#Paddle-Lite Model)
+
+The downloadable models provided by PaddleOCR include `inference model`, `trained model`, `pre-trained model` and `slim model`. The differences between the models are as follows:
+
+|model type|model format|description|
+|--- | --- | --- |
+|inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine，[detail](./inference_en.md)|
+|trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
+|slim model|\*.nb| Model compressed by PaddleSim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
+
+Relationship of the above models is as follows.
+
+
+
+
+### 1. Text Detection Model
+
+|model name|description|config|model size|download|
+| --- | --- | --- | --- | --- |
+|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|2.6M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
+|ch_ppocr_mobile_v2.0_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
+|ch_ppocr_server_v2.0_det|General model, which is larger than the lightweight model, but achieved better performance|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
+
+
+### 2. Text Recognition Model
+
+
+#### Chinese Recognition Model
+
+|model name|description|config|model size|download|
+| --- | --- | --- | --- | --- |
+|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
+|ch_ppocr_mobile_v2.0_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
+|ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
+
+
+**Note:** The `trained model` is finetuned on the `pre-trained model` with real data and synthsized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.
+
+
+#### English Recognition Model
+
+|model name|description|config|model size|download|
+| --- | --- | --- | --- | --- |
+|en_number_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)| 2.7M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_train.tar) |
+|en_number_mobile_v2.0_rec|Original lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) |
+
+
+#### Multilingual Recognition Model（Updating...）
+
+**Note：** The configuration file of the new multi language model is generated by code. You can use the `--help` parameter to check which multi language are supported by current PaddleOCR.
+
+```bash
+# The code needs to run in the specified directory
+cd {your/path/}PaddleOCR/configs/rec/multi_language/
+python3 generate_multi_language_configs.py --help
+```
+
+Take the Italian configuration file as an example：
+##### 1.Generate Italian configuration file to test the model provided
+you can generate the default configuration file through the following command, and use the default language dictionary provided by paddleocr for prediction.
+```bash
+# The code needs to run in the specified directory
+cd {your/path/}PaddleOCR/configs/rec/multi_language/
+# Set the required language configuration file through -l or --language parameter
+# This command will write the default parameter to the configuration file.
+python3 generate_multi_language_configs.py -l it
+```
+##### 2. Generate Italian configuration file to train your own data
+If you want to train your own model, you can prepare the training set file, verification set file, dictionary file and training data path. Here we assume that the Italian training set, verification set, dictionary and training data path are:
+- Training set:{your/path/}PaddleOCR/train_data/train_list.txt
+- Validation set: {your/path/}PaddleOCR/train_data/val_list.txt
+- Use the default dictionary provided by paddleocr:{your/path/}PaddleOCR/ppocr/utils/dict/it_dict.txt
+- Training data path:{your/path/}PaddleOCR/train_data
+```bash
+# The code needs to run in the specified directory
+cd {your/path/}PaddleOCR/configs/rec/multi_language/
+# The -l or --language parameter is required
+# --train modify train_list path
+# --val modify eval_list path
+# --data_dir modify data dir
+# -o modify default parameters
+# --dict Change the dictionary path. The example uses the default dictionary path, so that this parameter can be empty.
+python3 generate_multi_language_configs.py -l it \
+--train {path/to/train_list} \
+--val {path/to/val_list} \
+--data_dir {path/to/data_dir} \
+-o Global.use_gpu=False
+```
+|model name| dict file | description|config|model size|download|
+| --- | --- | --- |--- | --- | --- |
+| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt | Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
+| german_mobile_v2.0_rec | ppocr/utils/dict/german_dict.txt | Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
+| korean_mobile_v2.0_rec | ppocr/utils/dict/korean_dict.txt | Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
+| japan_mobile_v2.0_rec | ppocr/utils/dict/japan_dict.txt | Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
+| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
+| te_mobile_v2.0_rec | ppocr/utils/dict/te_dict.txt | Lightweight model for Telugu recognition|rec_te_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
+| ka_mobile_v2.0_rec | ppocr/utils/dict/ka_dict.txt | Lightweight model for Kannada recognition|rec_ka_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
+| ta_mobile_v2.0_rec | ppocr/utils/dict/ta_dict.txt | Lightweight model for Tamil recognition|rec_ta_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
+| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | Lightweight model for latin recognition | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
+| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
+| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
+| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
+
+For more supported languages, please refer to : [Multi-language model](./multi_languages_en.md)
+
+
+
+### 3. Text Angle Classification Model
+
+|model name|description|config|model size|download|
+| --- | --- | --- | --- | --- |
+|ch_ppocr_mobile_slim_v2.0_cls|Slim quantized model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_train.tar) |
+|ch_ppocr_mobile_v2.0_cls|Original model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
+
+### 4. Paddle-Lite Model
|Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
|---|---|---|---|---|---|---|
|V2.0|extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download lin](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download lin](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
|V2.0(slim)|extra-lightweight chinese OCR optimized model|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
-
-If you directly use the model in the above table for deployment, you can skip the following steps and directly read [Section 2.2](#2.2 Run optimized model on Phone).
-
-If the model to be deployed is not in the above table, you need to follow the steps below to obtain the optimized model.
-
-The `opt` tool can be obtained by compiling Paddle Lite.
-```
-git clone https://github.com/PaddlePaddle/Paddle-Lite.git
-cd Paddle-Lite
-git checkout release/v2.9
-./lite/tools/build.sh build_optimize_tool
-```
-
-After the compilation is complete, the opt file is located under build.opt/lite/api/, You can view the operating options and usage of opt in the following ways:
-
-```
-cd build.opt/lite/api/
-./opt
-```
-
-|Options|Description|
-|---|---|
-|--model_dir|The path of the PaddlePaddle model to be optimized (non-combined form)|
-|--model_file|The network structure file path of the PaddlePaddle model (combined form) to be optimized|
-|--param_file|The weight file path of the PaddlePaddle model (combined form) to be optimized|
-|--optimize_out_type|Output model type, currently supports two types: protobuf and naive_buffer, among which naive_buffer is a more lightweight serialization/deserialization implementation. If you need to perform model prediction on the mobile side, please set this option to naive_buffer. The default is protobuf|
-|--optimize_out|The output path of the optimized model|
-|--valid_targets|The executable backend of the model, the default is arm. Currently it supports x86, arm, opencl, npu, xpu, multiple backends can be specified at the same time (separated by spaces), and Model Optimize Tool will automatically select the best method. If you need to support Huawei NPU (DaVinci architecture NPU equipped with Kirin 810/990 Soc), it should be set to npu, arm|
-|--record_tailoring_info|When using the function of cutting library files according to the model, set this option to true to record the kernel and OP information contained in the optimized model. The default is false|
-
-`--model_dir` is suitable for the non-combined mode of the model to be optimized, and the inference model of PaddleOCR is the combined mode, that is, the model structure and model parameters are stored in a single file.
-
-The following takes the ultra-lightweight Chinese model of PaddleOCR as an example to introduce the use of the compiled opt file to complete the conversion of the inference model to the Paddle-Lite optimized model
-
-```
-# [Recommendation] Download the Chinese and English inference model of PaddleOCR V2.0
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_slim_infer.tar
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_slim_infer.tar
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_slim_infer.tar
-# Convert V2.0 detection model
-./opt --model_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_det_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
-# Convert V2.0 recognition model
-./opt --model_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_rec_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
-# Convert V2.0 angle classifier model
-./opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm --optimize_out_type=naive_buffer
-
-```
-
-After the conversion is successful, there will be more files ending with `.nb` in the inference model directory, which is the successfully converted model file.
-
-
-### 2.2 Run optimized model on Phone
-
-Some preparatory work is required first.
- 1. Prepare an Android phone with arm8. If the compiled prediction library and opt file are armv7, you need an arm7 phone and modify ARM_ABI = arm7 in the Makefile.
- 2. Make sure the phone is connected to the computer, open the USB debugging option of the phone, and select the file transfer mode.
- 3. Install the adb tool on the computer.
-
- 3.1. Install ADB for MAC:
- ```
- brew cask install android-platform-tools
- ```
- 3.2. Install ADB for Linux
- ```
- sudo apt update
- sudo apt install -y wget adb
- ```
- 3.3. Install ADB for windows
-
- To install on win, you need to go to Google's Android platform to download the adb package for installation：[link](https://developer.android.com/studio)
-
- Verify whether adb is installed successfully
- ```
- adb devices
- ```
- If there is device output, it means the installation is successful。
- ```
- List of devices attached
- 744be294 device
- ```
-
- 4. Prepare optimized models, prediction library files, test images and dictionary files used.
- ```
- git clone https://github.com/PaddlePaddle/PaddleOCR.git
- cd PaddleOCR/deploy/lite/
- # run prepare.sh
- sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
-
- #
- cd /{lite prediction library path}/inference_lite_lib.android.armv8/
- cd demo/cxx/ocr/
- # copy paddle-lite C++ .so file to debug/ directory
- cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
-
- cd inference_lite_lib.android.armv8/demo/cxx/ocr/
- cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
- ```
-
-Prepare the test image, taking PaddleOCR/doc/imgs/11.jpg as an example, copy the image file to the demo/cxx/ocr/debug/ folder. Prepare the model files optimized by the lite opt tool, ch_det_mv3_db_opt.nb, ch_rec_mv3_crnn_opt.nb, and place them under the demo/cxx/ocr/debug/ folder.
-
-The structure of the OCR demo is as follows after the above command is executed:
-
-```
-demo/cxx/ocr/
-|-- debug/
-| |--ch_ppocr_mobile_v2.0_det_slim_opt.nb Detection model
-| |--ch_ppocr_mobile_v2.0_rec_slim_opt.nb Recognition model
-| |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb Text direction classification model
-| |--11.jpg Image for OCR
-| |--ppocr_keys_v1.txt Dictionary file
-| |--libpaddle_light_api_shared.so C++ .so file
-| |--config.txt Config file
-|-- config.txt Config file
-|-- cls_process.cc Pre-processing and post-processing files for the angle classifier
-|-- cls_process.h
-|-- crnn_process.cc Pre-processing and post-processing files for the CRNN model
-|-- crnn_process.h
-|-- db_post_process.cc Pre-processing and post-processing files for the DB model
-|-- db_post_process.h
-|-- Makefile
-|-- ocr_db_crnn.cc C++ main code
-```
-
-#### 注意：
-1. `ppocr_keys_v1.txt` is a Chinese dictionary file. If the nb model is used for English recognition or other language recognition, dictionary file should be replaced with a dictionary of the corresponding language. PaddleOCR provides a variety of dictionaries under ppocr/utils/, including:
-```
-dict/french_dict.txt # french
-dict/german_dict.txt # german
-ic15_dict.txt # english
-dict/japan_dict.txt # japan
-dict/korean_dict.txt # korean
-ppocr_keys_v1.txt # chinese
-```
-
-2. `config.txt` of the detector and classifier, as shown below:
-```
-max_side_len 960 # Limit the maximum image height and width to 960
-det_db_thresh 0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
-det_db_box_thresh 0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
-det_db_unclip_ratio 1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
-use_direction_classify 0 # Whether to use the direction classifier, 0 means not to use, 1 means to use
-```
-
- 5. Run Model on phone
-
-After the above steps are completed, you can use adb to push the file to the phone to run, the steps are as follows:
-
- ```
- # Execute the compilation and get the executable file ocr_db_crnn
- # The first execution of this command will download dependent libraries such as opencv. After the download is complete, you need to execute it again
- make -j
- # Move the compiled executable file to the debug folder
- mv ocr_db_crnn ./debug/
- # Push the debug folder to the phone
- adb push debug /data/local/tmp/
- adb shell
- cd /data/local/tmp/debug
- export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
- # The use of ocr_db_crnn is:
- # ./ocr_db_crnn Detection model file Orientation classifier model file Recognition model file Test image path Dictionary file path
- ./ocr_db_crnn ch_ppocr_mobile_v2.0_det_opt.nb ch_ppocr_mobile_v2.0_rec_opt.nb ch_ppocr_mobile_v2.0_cls_opt.nb ./11.jpg ppocr_keys_v1.txt
- ```
-
-If you modify the code, you need to recompile and push to the phone.
-
-The outputs are as follows:
-
-
-
-