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StyleTextRec/README.md

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+### Quick Start
+
+`Style-Text` is an improvement of the SRNet network proposed in Baidu's self-developed text editing algorithm "Editing Text in the Wild". It is different from the commonly used GAN methods. This tool decomposes the text synthesis task into three sub-modules to improve the effect of synthetic data: text style transfer module, background extraction module and fusion module. 
+
+The following figure shows some example results. In addition, the actual `nameplate text recognition` scene and `the Korean text recognition` scene verify the effectiveness of the synthesis tool, as follows.
+
+
+#### Preparation
+
+1. Please refer the [QUICK INSTALLATION](../doc/doc_en/installation_en.md) to install PaddlePaddle. Python3 environment is strongly recommended.
+2. Download the pretrained models and unzip:
+
+```bash
+cd tools/style_text_rec
+wget /path/to/style_text_models.zip
+unzip style_text_models.zip
+```
+
+You can dowload models [here](https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/style_text_models.zip). If you save the model files in other folders, please edit the three model paths in `configs/config.yml`:
+
+```
+bg_generator:
+ pretrain: style_text_rec/bg_generator
+...
+text_generator:
+ pretrain: style_text_models/text_generator
+...
+fusion_generator:
+ pretrain: style_text_models/fusion_generator
+```
+
+
+
+#### Demo
+
+1. You can use the following commands to run a demo：
+
+```bash
+python -m tools.synth_image -c configs/config.yml
+```
+
+2. The results are `fake_bg.jpg`, `fake_text.jpg` and `fake_fusion.jpg` as shown in the figure above. Above them:
+ * `fake_text.jpg` is the generated image with the same font style as `Style Input`;
+ * `fake_bg.jpg` is the generated image of `Style Input` after removing foreground. 
+ * `fake_fusion.jpg` is the final result, that is synthesised by `fake_text.jpg` and `fake_bg.jpg`. 
+
+3. If want to generate image by other `Style Input` or `Text Input`, you can modify the `tools/synth_image.py`:
+ * `img = cv2.imread("examples/style_images/1.jpg")`: the path of `Style Input`;
+ * `corpus = "PaddleOCR"`: the `Text Input`;
+ * Notice：modify the language option(`language = "en"`) to adapt `Text Input`, that support `en`, `ch`, `ko`.
+
+4. We also provide `batch_synth_images` mothod, that can combine corpus and pictures in pairs to generate a batch of data.
+
+### Advanced Usage
+
+#### Components
+
+`Style Text Rec` mainly contains the following components：
+
+* `style_samplers`: It can sample `Style Input` from a dataset. Now, We only provide `DatasetSampler`.
+
+* `corpus_generators`: It can generate corpus. Now, wo only provide two `corpus_generators`:
+ * `EnNumCorpus`: It can generate a random string according to a given length, including uppercase and lowercase English letters, numbers and spaces.
+ * `FileCorpus`: It can read a text file and randomly return the words in it.
+
+* `text_drawers`: It can generate `Text Input`(text picture in standard font according to the input corpus). Note that when using, you have to modify the language information according to the corpus.
+
+* `predictors`: It can call the deep learning model to generate new data based on the `Style Input` and `Text Input`.
+
+* `writers`: It can write the generated pictures(`fake_bg.jpg`, `fake_text.jpg` and `fake_fusion.jpg`) and label information to the disk.
+
+* `synthesisers`: It can call the all modules to complete the work.
+
+### Generate Dataset
+
+Before the start, you need to prepare some data as material.
+First, you should have the style reference data for synthesis tasks, which are generally used as datasets for OCR recognition tasks.
+
+1. The referenced dataset can be specifed in `configs/dataset_config.yml`:
+ * `StyleSampler`: 
+ * `method`: The method of `StyleSampler`.
+ * `image_home`: The directory of pictures.
+ * `label_file`: The list of pictures path if `with_label` is `false`, otherwise, the label file path.
+ * `with_label`: The `label_file` is label file or not.
+
+ * `CorpusGenerator`: 
+ * `method`: The mothod of `CorpusGenerator`. If `FileCorpus` used, you need modify `corpus_file` and `language` accordingly, if `EnNumCorpus`, other configurations is not needed.
+ * `language`: The language of the corpus. Needed if method is not `EnNumCorpus`.
+ * `corpus_file`: The corpus file path. Needed if method is not `EnNumCorpus`.
+
+2. You can run the following command to start synthesis task:
+
+ ``` bash
+ python -m tools.synth_dataset.py -c configs/dataset_config.yml
+ ```
+
+3. You can using the following command to start multiple synthesis tasks in a multi-threaded manner, which needed to specifying tags by `-t`:
+
+ ```bash
+ python -m tools.synth_dataset.py -t 0 -c configs/dataset_config.yml
+ python -m tools.synth_dataset.py -t 1 -c configs/dataset_config.yml
+ ```
+
+### OCR Recognition Training
+
+After completing the above operations, you can get the synthetic data set for OCR recognition. Next, please complete the training by refering to [OCR Recognition Document](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/recognition. md#%E5%90%AF%E5%8A%A8%E8%AE%AD%E7%BB%83).

StyleTextRec/README_ch.md

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+## Style Text Rec
+
+### 目录
+- [工具简介](#工具简介)
+- [环境配置](#环境配置)
+- [快速上手](#快速上手)
+- [高级使用](#高级使用)
+- [应用示例](#应用示例)
+
+### 工具简介
+<div align="center">
+ <img src="doc/images/3.png" width="800">
+</div>
+
+<div align="center">
+ <img src="doc/images/1.png" width="600">
+</div>
+
+Style-Text数据合成工具是基于百度自研的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047
+不同于常用的基于GAN的数据合成工具，Style-Text主要框架包括：1.文本前景风格迁移模块 2.背景抽取模块 3.融合模块。经过这样三步，就可以迅速实现图片文字风格迁移。下图是一些该数据合成工具效果图。
+
+<div align="center">
+ <img src="doc/images/2.png" width="1000">
+</div>
+
+### 环境配置
+
+1. 参考[快速安装](../doc/doc_ch/installation.md)，安装PaddleOCR。
+2. 进入`style_text_rec`目录，下载模型，并解压：
+
+```bash
+cd style_text_rec
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/style_text_models.zip
+unzip style_text_models.zip
+```
+
+如果您将模型保存再其他位置，请在`configs/config.yml`中修改模型文件的地址，修改时需要同时修改这三个配置：
+
+```
+bg_generator:
+ pretrain: style_text_models/bg_generator
+...
+text_generator:
+ pretrain: style_text_models/text_generator
+...
+fusion_generator:
+ pretrain: style_text_models/fusion_generator
+```
+
+### 快速上手
+
+1. 运行tools/synth_image，生成示例图片：
+
+```python
+python3 -m tools.synth_image -c configs/config.yml
+```
+
+1. 运行后，会生成`fake_busion.jpg`，即为最终结果。
+<div align="center">
+ <img src="doc/images/4.jpg" width="300">
+</div>
+除此之外，程序还会生成并保存中间结果：
+ * `fake_bg.jpg`：为风格参考图去掉文字后的背景；
+ * `fake_text.jpg`：是用提供的字符串，仿照风格参考图中文字的风格，生成在灰色背景上的文字图片。
+
+2. 如果您想尝试其他风格图像和文字的效果，可以添加style_image,text_corpus和language参数：
+```python
+python3 -m tools.synth_image -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
+```
+ * 注意：语言选项和语料相对应，目前我们支持英文、简体中文和韩语。
+
+3. 在`tools/synth_image.py`中，我们还提供了一个`batch_synth_images`方法，可以两两组合语料和图片，批量生成一批数据。
+
+### 高级使用
+
+在开始合成数据集前，需要准备一些素材。
+
+首先，需要风格图片作为合成图片的参考依据，这些数据可以是用作训练OCR识别模型的数据集。本例中使用带有标注文件的数据集作为风格图片.
+
+1. 在`configs/dataset_config.yml`中配置输入数据路径。
+ * `StyleSampler`：
+ * `method`：使用的风格图片采样方法；
+ * `image_home`：风格图片目录；
+ * `label_file`：风格图片路径列表文件，如果所用数据集有label，则label_file为label文件路径；
+ * `with_label`：标志`label_file`是否为label文件。
+ * `CorpusGenerator`：
+ * `method`：语料生成方法，目前有`FileCorpus`和`EnNumCorpus`可选。如果使用`EnNumCorpus`，则不需要填写其他配置，否则需要修改`corpus_file`和`language`；
+ * `language`：语料的语种；
+ * `corpus_file`: 语料文件路径。
+
+ 我们提供了一批[样例图](https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/chkoen_5w.tar)供您试用，下面给出了一些示例：
+<div align="center">
+ <img src="doc/images/5.png" width="800">
+</div>
+2. 运行`tools/synth_dataset`合成数据：
+
+ ``` bash
+ python -m tools.synth_dataset -c configs/dataset_config.yml
+ ```
+
+3. 如果您想使用并行方式来快速合成数据，可以通过启动多个进程，在启动时需要指定不同的`tag`（`-t`），如下所示:
+
+ ```bash
+ python3 -m tools.synth_dataset -t 0 -c configs/dataset_config.yml
+ python3 -m tools.synth_dataset -t 1 -c configs/dataset_config.yml
+ ```
+
+
+### 应用示例
+
+在完成上述操作后，即可得到用于OCR识别的合成数据集，下面给出了一些数据集生成的示例：
+<div align="center">
+ <img src="doc/images/6.png" width="800">
+</div>
+请您参考[OCR识别文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/recognition.md#%E5%90%AF%E5%8A%A8%E8%AE%AD%E7%BB%83)，完成训练。
+
+下面展示了一些使用合成数据训练的效果：
+
+| 场景 | 字符 | 原始数据 | 测试数据 | 只使用原始数据的识别准确率 | 新增合成数据 | 使用合成数据识别准确率 | 指标提升 |
+| -------- | ---------- | -------- | -------- | -------------------------- | ------------ | ---------------------- | -------- |
+| 金属表面 | 英文和数字 | 2203 | 650 | 0.5938 | 20000 | 0.7546 | 16% |
+| 随机背景 | 韩语 | 5631 | 1230 | 0.3012 | 100000 | 0.5057 | 20% |
+
+### 项目结构
+```
+style_text_rec
+|-- arch
+| |-- base_module.py
+| |-- decoder.py
+| |-- encoder.py
+| |-- spectral_norm.py
+| `-- style_text_rec.py
+|-- configs
+| |-- config.yml
+| `-- dataset_config.yml
+|-- engine
+| |-- corpus_generators.py
+| |-- predictors.py
+| |-- style_samplers.py
+| |-- synthesisers.py
+| |-- text_drawers.py
+| `-- writers.py
+|-- examples
+| |-- corpus
+| | `-- example.txt
+| |-- image_list.txt
+| `-- style_images
+| |-- 1.jpg
+| `-- 2.jpg
+|-- fonts
+| |-- ch_standard.ttf
+| |-- en_standard.ttf
+| `-- ko_standard.ttf
+|-- tools
+| |-- __init__.py
+| |-- synth_dataset.py
+| `-- synth_image.py
+`-- utils
+ |-- config.py
+ |-- load_params.py
+ |-- logging.py
+ |-- math_functions.py
+ `-- sys_funcs.py
+```