Merge pull request #265 from xxxpsyduck/develop

Update English docs version 24.06.20

README_en.md

@@ -1,18 +1,18 @@
 English | [简体中文](README.md)
 
-## Introduction
+## INTRODUCTION
 PaddleOCR aims to create a rich, leading, and practical OCR tools that help users train better models and apply them into practice.
 
 **Recent updates**
 - 2020.6.8 Add [dataset](./doc/doc_en/datasets_en.md) and keep updating
 - 2020.6.5 Support exporting `attention` model to `inference_model`
 - 2020.6.5 Support separate prediction and recognition, output result score
-- 2020.5.30 Provide ultra-lightweight Chinese OCR online experience
+- 2020.5.30 Provide lightweight Chinese OCR online experience
 - 2020.5.30 Model prediction and training supported on Windows system
 - [more](./doc/doc_en/update_en.md)
 
-## Features
-- Ultra-lightweight Chinese OCR model, total model size is only 8.6M
+## FEATURES
+- Lightweight Chinese OCR model, total model size is only 8.6M
  - Single model supports Chinese and English numbers combination recognition, vertical text recognition, long text recognition
  - Detection model DB (4.1M) + recognition model CRNN (4.5M)
 - Various text detection algorithms: EAST, DB
@@ -22,34 +22,34 @@ PaddleOCR aims to create a rich, leading, and practical OCR tools that help user
 
 |Model Name|Description |Detection Model link|Recognition Model link|
 |-|-|-|-|
-|chinese_db_crnn_mobile|Ultra-lightweight Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
+|chinese_db_crnn_mobile|lightweight Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
 |chinese_db_crnn_server|General Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
 
 
 For testing our Chinese OCR online：https://www.paddlepaddle.org.cn/hub/scene/ocr
 
-**You can also quickly experience the Ultra-lightweight Chinese OCR and General Chinese OCR models as follows:**
+**You can also quickly experience the lightweight Chinese OCR and General Chinese OCR models as follows:**
 
-## **Ultra-lightweight Chinese OCR and General Chinese OCR inference**
+## **LIGHTWEIGHT CHINESE OCR AND GENERAL CHINESE OCR INFERENCE**
 
 ![](doc/imgs_results/11.jpg)
 
-The picture above is the result of our Ultra-lightweight Chinese OCR model. For more testing results, please see the end of the article [Ultra-lightweight Chinese OCR results](#Ultra-lightweight-Chinese-OCR-results) and [General Chinese OCR results](#General-Chinese-OCR-results).
+The picture above is the result of our lightweight Chinese OCR model. For more testing results, please see the end of the article [lightweight Chinese OCR results](#lightweight-Chinese-OCR-results) and [General Chinese OCR results](#General-Chinese-OCR-results).
 
-#### 1. Environment configuration
+#### 1. ENVIRONMENT CONFIGURATION
 
 Please see [Quick installation](./doc/doc_en/installation_en.md)
 
-#### 2. Download inference models
+#### 2. DOWNLOAD INFERENCE MODELS
 
-#### (1) Download Ultra-lightweight Chinese OCR models
+#### (1) Download lightweight Chinese OCR models
 *If wget is not installed in the windows system, you can copy the link to the browser to download the model. After model downloaded, unzip it and place it in the corresponding directory*
 
 ```
 mkdir inference && cd inference
-# Download the detection part of the Ultra-lightweight Chinese OCR and decompress it
+# Download the detection part of the lightweight Chinese OCR and decompress it
 wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
-# Download the recognition part of the Ultra-lightweight Chinese OCR and decompress it
+# Download the recognition part of the lightweight Chinese OCR and decompress it
 wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
 cd ..
 ```
@@ -63,7 +63,7 @@ wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar && t
 cd ..
 ```
 
-#### 3. Single image and batch image prediction
+#### 3. SINGLE IMAGE AND BATCH PREDICTION
 
 The following code implements text detection and recognition inference tandemly. When performing prediction, you need to specify the path of a single image or image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detection model, and the parameter `rec_model_dir` specifies the path to the recognition model. The visual prediction results are saved to the `./inference_results` folder by default.
 
@@ -87,14 +87,14 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_mode
 
 For more text detection and recognition models, please refer to the document [Inference](./doc/doc_en/inference_en.md)
 
-## Documentation
+## DOCUMENTATION
 - [Quick installation](./doc/doc_en/installation_en.md)
 - [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
 - [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
 - [Inference](./doc/doc_en/inference_en.md)
 - [Dataset](./doc/doc_en/datasets_en.md)
 
-## Text detection algorithm
+## TEXT DETECTION ALGORITHM
 
 PaddleOCR open source text detection algorithms list:
 - [x] EAST([paper](https://arxiv.org/abs/1704.03155))
@@ -113,14 +113,14 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for Chinese detection task are as follows:
 |Model|Backbone|Configuration file|Pre-trained model|
 |-|-|-|-|
-|Ultra-lightweight Chinese model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
+|lightweight Chinese model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
 |General Chinese OCR model|ResNet50_vd|det_r50_vd_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
 
 * Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result.
 
 For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
 
-## Text recognition algorithm
+## TEXT RECOGNITION ALGORITHM
 
 PaddleOCR open-source text recognition algorithms list:
 - [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
@@ -145,16 +145,16 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
 We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) dataset and cropout 30w traning data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the Chinese model. The related configuration and pre-trained models are as follows:
 |Model|Backbone|Configuration file|Pre-trained model|
 |-|-|-|-|
-|Ultra-lightweight Chinese model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
+|lightweight Chinese model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
 |General Chinese OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
 
 Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
 
-## End-to-end OCR algorithm
+## END-TO-END OCR ALGORITHM
 - [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, comming soon)
 
-<a name="Ultra-lightweight Chinese OCR results"></a>
-## Ultra-lightweight Chinese OCR results
+<a name="lightweight Chinese OCR results"></a>
+## LIGHTWEIGHT CHINESE OCR RESULTS
 ![](doc/imgs_results/1.jpg)
 ![](doc/imgs_results/7.jpg)
 ![](doc/imgs_results/12.jpg)
@@ -189,11 +189,12 @@ Please refer to the document for training guide and use of PaddleOCR text recogn
 
 [more](./doc/doc_en/FAQ_en.md)
 
-## Welcome to the PaddleOCR technical exchange group
-WeChat: paddlehelp . remarks OCR, the assistant will invite you to join the group~
+## WELCOME TO THE PaddleOCR TECHNICAL EXCHANGE GROUP
+WeChat: paddlehelp, note OCR, our assistant will get you into the group~
 
+<img src="./doc/paddlehelp.jpg" width = "200" height = "200" />
 
-## References
+## REFERENCES
 ```
 1. EAST:
 @inproceedings{zhou2017east,
@@ -248,10 +249,10 @@ WeChat: paddlehelp . remarks OCR, the assistant will invite you to join the grou
 }
 ```
 
-## License
+## LICENSE
 This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
 
-## Contribution
+## CONTRIBUTION
 We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
 
 - Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) for contributing the English documentation.

doc/doc_en/FAQ_en.md

@@ -47,3 +47,7 @@ At present, the open source model, dataset and magnitude are as follows:
 10. **Error in using the model with TPS module for prediction** 
 Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3](108) != Grid dimension[2](100) 
 Solution：TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
+
+11. **Custom dictionary used during training, the recognition results show that words do not appear in the dictionary**
+
+The used custom dictionary path is not set when making prediction. The solution is setting parameter `rec_char_dict_path` to the corresponding dictionary file.

doc/doc_en/config_en.md

@@ -1,4 +1,4 @@
-# Optional parameters list
+# OPTIONAL PARAMETERS LIST
 
 The following list can be viewed via `--help`
 
@@ -8,7 +8,7 @@ The following list can be viewed via `--help`
 | -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: `-o Global.use_gpu=false` | 
 
 
-## Introduction to Global Parameters of Configuration File
+## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE
 
 Take `rec_chinese_lite_train.yml` as an example
 
@@ -35,7 +35,7 @@ Take `rec_chinese_lite_train.yml` as an example
 | checkpoints | Load saved model path | None | Used to load saved parameters to continue training after interruption |
 | save_inference_dir | path to save model for inference | None | Use to save inference model |
 
-## Introduction to Reader parameters of Configuration file
+## INTRODUCTION TO READER PARAMETERS OF CONFIGURATION FILE
 
 Take `rec_chinese_reader.yml` as an example:
 
@@ -47,7 +47,7 @@ Take `rec_chinese_reader.yml` as an example:
 | label_file_path | Groundtruth file path | ./train_data/rec_gt_train.txt| \ |
 | infer_img | Result folder path | ./infer_img | \|
 
-## Introduction to Optimizer parameters of Configuration file
+## INTRODUCTION TO OPTIMIZER PARAMETERS OF CONFIGURATION FILE
 
 Take `rec_icdar15_train.yml` as an example:
 

doc/doc_en/customize_en.md

@@ -1,24 +1,24 @@
-# How to make your own ultra-lightweight OCR models?
+# HOW TO MAKE YOUR OWN LIGHTWEIGHT OCR MODEL?
 
 The process of making a customized ultra-lightweight OCR models can be divided into three steps: training text detection model, training text recognition model, and concatenate the predictions from previous steps.
 
-## step1: Train text detection model
+## STEP1: TRAIN TEXT DETECTION MODEL
 
 PaddleOCR provides two text detection algorithms: EAST and DB. Both support MobileNetV3 and ResNet50_vd backbone networks, select the corresponding configuration file as needed and start training. For example, to train with MobileNetV3 as the backbone network for DB detection model :
 ```
 python3 tools/train.py -c configs/det/det_mv3_db.yml
 ```
 For more details about data preparation and training tutorials, refer to the documentation [Text detection model training/evaluation/prediction](./detection_en.md)
 
-## step2: Train text recognition model
+## STEP2: TRAIN TEXT RECOGNITION MODEL
 
 PaddleOCR provides four text recognition algorithms: CRNN, Rosetta, STAR-Net, and RARE. They all support two backbone networks: MobileNetV3 and ResNet34_vd, select the corresponding configuration files as needed to start training. For example, to train a CRNN recognition model that uses MobileNetV3 as the backbone network:
 ```
 python3 tools/train.py -c configs/rec/rec_chinese_lite_train.yml
 ```
 For more details about data preparation and training tutorials, refer to the documentation [Text recognition model training/evaluation/prediction](./recognition_en.md)
 
-## step3: Concatenate predictions
+## STEP3: CONCATENATE PREDICTIONS
 
 PaddleOCR provides a concatenation tool for detection and recognition models, which can connect any trained detection model and any recognition model into a two-stage text recognition system. The input image goes through four main stages: text detection, text rectification, text recognition, and score filtering to output the text position and recognition results, and at the same time, you can choose to visualize the results.
 

doc/doc_en/datasets_en.md

@@ -1,4 +1,4 @@
-## Dataset
+## DATASET
 This is a collection of commonly used Chinese datasets, which is being updated continuously. You are welcome to contribute to this list～
 - [ICDAR2019-LSVT](#ICDAR2019-LSVT)
 - [ICDAR2017-RCTW-17](#ICDAR2017-RCTW-17)
@@ -13,8 +13,11 @@ In addition to opensource data, users can also use synthesis tools to synthesize
 - **Data sources**：https://ai.baidu.com/broad/introduction?dataset=lsvt
 - **Introduction**： A total of 45w Chinese street view images, including 5w (2w test + 3w training) fully labeled data (text coordinates + text content), 40w weakly labeled data (text content only), as shown in the following figure:
  ![](../datasets/LSVT_1.jpg)
+
  (a) Fully labeled data
+
  ![](../datasets/LSVT_2.jpg)
+
  (b) Weakly labeled data
 - **Download link**：https://ai.baidu.com/broad/download?dataset=lsvt
 

doc/doc_en/detection_en.md

@@ -1,8 +1,8 @@
-# Text detection
+# TEXT DETECTION
 
 This section uses the icdar15 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
 
-## Data preparation
+## DATA PREPARATION
 The icdar2015 dataset can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading.
 
 Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. In addition, PaddleOCR organizes many scattered annotation files into two separate annotation files for train and test respectively, which can be downloaded by wget:
@@ -27,13 +27,13 @@ The provided annotation file format is as follow:
 " Image file name Image annotation information encoded by json.dumps"
 ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
 ```
-The image annotation information before json.dumps encoding is a list containing multiple dictionaries. The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
+The image annotation after json.dumps() encoding is a list containing multiple dictionaries. The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
 
 `transcription` represents the text of the current text box, and this information is not needed in the text detection task.
 If you want to train PaddleOCR on other datasets, you can build the annotation file according to the above format.
 
 
-## Quickstart training
+## TRAINING
 
 First download the pretrained model. The detection model of PaddleOCR currently supports two backbones, namely MobileNetV3 and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
 ```
@@ -56,7 +56,7 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models
 
 ```
 
-**Start training**
+**START TRAINING**
 ```
 python3 tools/train.py -c configs/det/det_mv3_db.yml
 ```
@@ -80,7 +80,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
 **Note**:The priority of Global.checkpoints is higher than the priority of Global.pretrain_weights, that is, when two parameters are specified at the same time, the model specified by Global.checkpoints will be loaded first. If the model path specified by Global.checkpoints is wrong, the one specified by Global.pretrain_weights will be loaded.
 
 
-## Evaluation Indicator
+## EVALUATION
 
 PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean.
 
@@ -100,7 +100,7 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./ou
 
 * Note: box_thresh and unclip_ratio are parameters required for DB post-processing, and not need to be set when evaluating the EAST model.
 
-## Test detection result
+## TEST DETECTION RESULT
 
 Test the detection result on a single image:
 ```