diff --git a/doc/doc_ch/angle_class.md b/doc/doc_ch/angle_class.md index 6e68134a4d1b8d9b8927d67c9724ba88563383a4..ad25a6661817623419af0c0c7a139dd4bfaeb08c 100644 --- a/doc/doc_ch/angle_class.md +++ b/doc/doc_ch/angle_class.md @@ -1,4 +1,12 @@ ## 文字角度分类 +### 方法介绍 +文字角度分类主要用于图片非0度的场景下，在这种场景下需要对图片里检测到的文本行进行一个转正的操作。在PaddleOCR系统内， +文字检测之后得到的文本行图片经过仿射变换之后送入识别模型，此时只需要对文字进行一个0和180度的角度分类，因此PaddleOCR内置的 +文字角度分类器**只支持了0和180度的分类**。如果想支持更多角度，可以自己修改算法进行支持。 + +0和180度数据样本例子： + +![](../imgs_results/angle_class_example.jpg) ### 数据准备 @@ -13,7 +21,7 @@ ln -sf /train_data/cls/dataset 请参考下文组织您的数据。 - 训练集 -首先请将训练图片放入同一个文件夹（train_images），并用一个txt文件（cls_gt_train.txt）记录图片路径和标签。 +首先建议将训练图片放入同一个文件夹，并用一个txt文件（cls_gt_train.txt）记录图片路径和标签。 **注意：** 默认请将图片路径和图片标签用 `\t` 分割，如用其他方式分割将造成训练报错 @@ -21,8 +29,8 @@ ln -sf /train_data/cls/dataset ``` " 图像文件名 图像标注信息 " -train/word_001.jpg 0 -train/word_002.jpg 180 +train/cls/train/word_001.jpg 0 +train/cls/train/word_002.jpg 180 ``` 最终训练集应有如下文件结构： diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md index 91d649078b3bccbf4e7a0858e90e422290e35cff..907cf24e1a31104096ab6c0cf0819457852d1490 100644 --- a/doc/doc_ch/recognition.md +++ b/doc/doc_ch/recognition.md @@ -1,61 +1,94 @@ ## 文字识别 -- [一、数据准备](#数据准备) - - [数据下载](#数据下载) - - [自定义数据集](#自定义数据集) - - [字典](#字典) - - [支持空格](#支持空格) +- [1 数据准备](#数据准备) + - [1.1 自定义数据集](#自定义数据集) + - [1.2 数据下载](#数据下载) + - [1.3 字典](#字典) + - [1.4 支持空格](#支持空格) -- [二、启动训练](#启动训练) - - [1. 数据增强](#数据增强) - - [2. 训练](#训练) - - [3. 小语种](#小语种) +- [2 启动训练](#启动训练) + - [2.1 数据增强](#数据增强) + - [2.2 训练](#训练) + - [2.3 小语种](#小语种) -- [三、评估](#评估) +- [3 评估](#评估) -- [四、预测](#预测) - - [1. 训练引擎预测](#训练引擎预测) +- [4 预测](#预测) + - [4.1 训练引擎预测](#训练引擎预测) -### 数据准备 +### 1. 数据准备 -PaddleOCR 支持两种数据格式: `lmdb` 用于训练公开数据，调试算法; `通用数据` 训练自己的数据: - -请按如下步骤设置数据集： +PaddleOCR 支持两种数据格式: + - `lmdb` 用于训练以lmdb格式存储的数据集; + - `通用数据` 用于训练以文本文件存储的数据集: 训练数据的默认存储路径是 `PaddleOCR/train_data`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录： ``` +# linux and mac os ln -sf /train_data/dataset +# windows +mklink /d /train_data/dataset ``` - -* 数据下载 - -若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。 -如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。 + +#### 1.1 自定义数据集 +下面以通用数据集为例， 介绍如何准备数据集： - -* 使用自己数据集 +* 训练集 -若您希望使用自己的数据进行训练，请参考下文组织您的数据。 +建议将训练图片放入同一个文件夹，并用一个txt文件（rec_gt_train.txt）记录图片路径和标签，txt文件里的内容如下: -- 训练集 +**注意：** txt文件中默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错。 -首先请将训练图片放入同一个文件夹（train_images），并用一个txt文件（rec_gt_train.txt）记录图片路径和标签。 +``` +" 图像文件名 图像标注信息 " -**注意：** 默认请将图片路径和图片标签用 \t 分割，如用其他方式分割将造成训练报错 +train_data/rec/train/word_001.jpg 简单可依赖 +train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单 +... +``` +最终训练集应有如下文件结构： +``` +|-train_data + |-rec + |- rec_gt_train.txt + |- train + |- word_001.png + |- word_002.jpg + |- word_003.jpg + | ... ``` -" 图像文件名 图像标注信息 " -train_data/train_0001.jpg 简单可依赖 -train_data/train_0002.jpg 用科技让复杂的世界更简单 +- 测试集 + +同训练集类似，测试集也需要提供一个包含所有图片的文件夹（test）和一个rec_gt_test.txt，测试集的结构如下所示： + +``` +|-train_data + |-rec + |- rec_gt_test.txt + |- test + |- word_001.jpg + |- word_002.jpg + |- word_003.jpg + | ... ``` -PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通过以下方式下载： + + + +1.2 数据下载 + +若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ，下载 benchmark 所需的lmdb格式数据集。 + +如果你使用的是icdar2015的公开数据集，PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通过以下方式下载： + +如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。 ``` # 训练集标签 @@ -71,34 +104,8 @@ PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支 python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt" ``` -最终训练集应有如下文件结构： -``` -|-train_data - |-ic15_data - |- rec_gt_train.txt - |- train - |- word_001.png - |- word_002.jpg - |- word_003.jpg - | ... -``` - -- 测试集 - -同训练集类似，测试集也需要提供一个包含所有图片的文件夹（test）和一个rec_gt_test.txt，测试集的结构如下所示： - -``` -|-train_data - |-ic15_data - |- rec_gt_test.txt - |- test - |- word_001.jpg - |- word_002.jpg - |- word_003.jpg - | ... -``` -- 字典 +1.3 字典 最后需要提供一个字典（{word_dict_name}.txt），使模型在训练时，可以将所有出现的字符映射为字典的索引。 @@ -115,6 +122,10 @@ n word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，“and” 将被映射成 [2 5 1] +* 内置字典 + +PaddleOCR内置了一部分字典，可以按需使用。 + `ppocr/utils/ppocr_keys_v1.txt` 是一个包含6623个字符的中文字典 `ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典 @@ -130,7 +141,7 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起， `ppocr/utils/dict/en_dict.txt` 是一个包含63个字符的英文字典 -您可以按需使用。 + 目前的多语言模型仍处在demo阶段，会持续优化模型并补充语种，**非常欢迎您为我们提供其他语言的字典和字体**， 如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict)，我们会在Repo中感谢您。 @@ -141,13 +152,13 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起， 并将 `character_type` 设置为 `ch`。 -- 添加空格类别 +1.4 添加空格类别 如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `True`。 -### 启动训练 +### 2. 启动训练 PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 CRNN 识别模型为例： @@ -172,7 +183,7 @@ tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml ``` -- 数据增强 +#### 2.1 数据增强 PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。 @@ -183,7 +194,7 @@ PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入 *由于OpenCV的兼容性问题，扰动操作暂时只支持Linux* -- 训练 +#### 2.2 训练 PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_train.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/rec_CRNN/best_accuracy` 。 @@ -272,7 +283,7 @@ Eval: **注意，预测/评估时的配置文件请务必与训练一致。** -- 小语种 +#### 2.3 小语种 PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。 @@ -415,7 +426,7 @@ Eval: ... ``` -### 评估 +### 3 评估 评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。 @@ -425,10 +436,10 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec ``` -### 预测 +### 4 预测 -* 训练引擎的预测 +#### 4.1 训练引擎的预测 使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。 diff --git a/doc/doc_ch/whl.md b/doc/doc_ch/whl.md index 6b218e31ecdc3d07132a2a88ad22528ed6ef23b4..032d7ae642ad7b2a10253dbfd6310bd5299fb5a0 100644 --- a/doc/doc_ch/whl.md +++ b/doc/doc_ch/whl.md @@ -1,8 +1,8 @@ # paddleocr package使用说明 -## 快速上手 +## 1 快速上手 -### 安装whl包 +### 1.1 安装whl包 pip安装 ```bash @@ -14,9 +14,12 @@ pip install "paddleocr>=2.0.1" # 推荐使用2.0.1+版本 python3 setup.py bdist_wheel pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本号 ``` -### 1. 代码使用 -* 检测+分类+识别全流程 +## 2 使用 +### 2.1 代码使用 +paddleocr whl包会自动下载ppocr轻量级模型作为默认模型，可以根据第3节**自定义模型**进行自定义更换。 + +* 检测+方向分类器+识别全流程 ```python from paddleocr import PaddleOCR, draw_ocr # Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换 @@ -84,7 +87,7 @@ im_show.save('result.jpg') -* 分类+识别 +* 方向分类器+识别 ```python from paddleocr import PaddleOCR ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory @@ -143,7 +146,7 @@ for line in result: ['韩国小馆', 0.9907421] ``` -* 单独执行分类 +* 单独执行方向分类器 ```python from paddleocr import PaddleOCR ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory @@ -157,14 +160,14 @@ for line in result: ['0', 0.9999924] ``` -### 通过命令行使用 +### 2.2 通过命令行使用 查看帮助信息 ```bash paddleocr -h ``` -* 检测+分类+识别全流程 +* 检测+方向分类器+识别全流程 ```bash paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true ``` @@ -188,7 +191,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg ...... ``` -* 分类+识别 +* 方向分类器+识别 ```bash paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false ``` @@ -220,7 +223,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false ['韩国小馆', 0.9907421] ``` -* 单独执行分类 +* 单独执行方向分类器 ```bash paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false --rec false ``` @@ -230,11 +233,11 @@ paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls tru ['0', 0.9999924] ``` -## 自定义模型 +## 3 自定义模型 当内置模型无法满足需求时，需要使用到自己训练的模型。 首先，参照[inference.md](./inference.md) 第一节转换将检测、分类和识别模型转换为inference模型，然后按照如下方式使用 -### 代码使用 +### 3.1 代码使用 ```python from paddleocr import PaddleOCR, draw_ocr # 模型路径下必须含有model和params文件 @@ -255,17 +258,17 @@ im_show = Image.fromarray(im_show) im_show.save('result.jpg') ``` -### 通过命令行使用 +### 3.2 通过命令行使用 ```bash paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true ``` -### 使用网络图片或者numpy数组作为输入 +## 4 使用网络图片或者numpy数组作为输入 -1. 网络图片 +### 4.1 网络图片 -代码使用 +- 代码使用 ```python from paddleocr import PaddleOCR, draw_ocr # Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换 @@ -286,12 +289,12 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc im_show = Image.fromarray(im_show) im_show.save('result.jpg') ``` -命令行模式 +- 命令行模式 ```bash paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true ``` -2. numpy数组 +### 4.2 numpy数组 仅通过代码使用时支持numpy数组作为输入 ```python from paddleocr import PaddleOCR, draw_ocr @@ -301,7 +304,7 @@ ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to downlo img_path = 'PaddleOCR/doc/imgs/11.jpg' img = cv2.imread(img_path) # img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), 如果你自己训练的模型支持灰度图，可以将这句话的注释取消 -result = ocr.ocr(img_path, cls=True) +result = ocr.ocr(img, cls=True) for line in result: print(line) @@ -316,7 +319,7 @@ im_show = Image.fromarray(im_show) im_show.save('result.jpg') ``` -## 参数说明 +## 5 参数说明 | 字段 | 说明 | 默认值 | |-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------| diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md index d1cc712f312bf8e70c0b399422519217df323129..0044d85ac0a43529c67746d25118bd80ee52be9a 100644 --- a/doc/doc_en/angle_class_en.md +++ b/doc/doc_en/angle_class_en.md @@ -1,5 +1,12 @@ ## TEXT ANGLE CLASSIFICATION +### Method introduction +The angle classification is used in the scene where the image is not 0 degrees. In this scene, it is necessary to perform a correction operation on the text line detected in the picture. In the PaddleOCR system, +The text line image obtained after text detection is sent to the recognition model after affine transformation. At this time, only a 0 and 180 degree angle classification of the text is required, so the built-in PaddleOCR text angle classifier **only supports 0 and 180 degree classification**. If you want to support more angles, you can modify the algorithm yourself to support. + +Example of 0 and 180 degree data samples： + +![](../imgs_results/angle_class_example.jpg) ### DATA PREPARATION Please organize the dataset as follows: diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md index 14ddcc755071924a91b9bf654fb2f0b314e00790..aeb9aa0d43e400c4d6e733b2c9f4a74559dccecb 100644 --- a/doc/doc_en/recognition_en.md +++ b/doc/doc_en/recognition_en.md @@ -1,79 +1,70 @@ ## TEXT RECOGNITION -- [DATA PREPARATION](#DATA_PREPARATION) - - [Dataset Download](#Dataset_download) - - [Costom Dataset](#Costom_Dataset) - - [Dictionary](#Dictionary) - - [Add Space Category](#Add_space_category) +- [1 DATA PREPARATION](#DATA_PREPARATION) + - [1.1 Costom Dataset](#Costom_Dataset) + - [1.2 Dataset Download](#Dataset_download) + - [1.3 Dictionary](#Dictionary) + - [1.4 Add Space Category](#Add_space_category) -- [TRAINING](#TRAINING) - - [Data Augmentation](#Data_Augmentation) - - [Training](#Training) - - [Multi-language](#Multi_language) +- [2 TRAINING](#TRAINING) + - [2.1 Data Augmentation](#Data_Augmentation) + - [2.2 Training](#Training) + - [2.3 Multi-language](#Multi_language) -- [EVALUATION](#EVALUATION) +- [3 EVALUATION](#EVALUATION) -- [PREDICTION](#PREDICTION) - - [Training engine prediction](#Training_engine_prediction) +- [4 PREDICTION](#PREDICTION) + - [4.1 Training engine prediction](#Training_engine_prediction) ### DATA PREPARATION -PaddleOCR supports two data formats: `LMDB` is used to train public data and evaluation algorithms; `general data` is used to train your own data: +PaddleOCR supports two data formats: +- `LMDB` is used to train data sets stored in lmdb format; +- `general data` is used to train data sets stored in text files: Please organize the dataset as follows: The default storage path for training data is `PaddleOCR/train_data`, if you already have a dataset on your disk, just create a soft link to the dataset directory: ``` +# linux and mac os ln -sf /train_data/dataset +# windows +mklink /d /train_data/dataset ``` - -* Dataset download - -If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，download the lmdb format dataset required for benchmark - -If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path. - -* Use your own dataset: +#### 1.1 Costom dataset If you want to use your own data for training, please refer to the following to organize your data. - Training set -First put the training images in the same folder (train_images), and use a txt file (rec_gt_train.txt) to store the image path and label. +It is recommended to put the training images in the same folder, and use a txt file (rec_gt_train.txt) to store the image path and label. The contents of the txt file are as follows: * Note: by default, the image path and image label are split with \t, if you use other methods to split, it will cause training error ``` " Image file name Image annotation " -train_data/train_0001.jpg 简单可依赖 -train_data/train_0002.jpg 用科技让复杂的世界更简单 -``` -PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways: - -``` -# Training set label -wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt -# Test Set Label -wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt +train_data/rec/train/word_001.jpg 简单可依赖 +train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单 +... ``` The final training set should have the following file structure: ``` |-train_data - |-ic15_data - |- rec_gt_train.txt - |- train - |- word_001.png - |- word_002.jpg - |- word_003.jpg - | ... + |-rec + |- rec_gt_train.txt + |- train + |- word_001.png + |- word_002.jpg + |- word_003.jpg + | ... ``` - Test set @@ -82,6 +73,7 @@ Similar to the training set, the test set also needs to be provided a folder con ``` |-train_data + |-rec |-ic15_data |- rec_gt_test.txt |- test @@ -90,8 +82,25 @@ Similar to the training set, the test set also needs to be provided a folder con |- word_003.jpg | ... ``` + + +#### 1.2 Dataset download + +If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ，download the lmdb format dataset required for benchmark + +If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path. + +PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways: + +``` +# Training set label +wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt +# Test Set Label +wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt +``` + -- Dictionary +#### 1.3 Dictionary Finally, a dictionary ({word_dict_name}.txt) needs to be provided so that when the model is trained, all the characters that appear can be mapped to the dictionary index. @@ -108,6 +117,8 @@ n In `word_dict.txt`, there is a single word in each line, which maps characters and numeric indexes together, e.g "and" will be mapped to [2 5 1] +PaddleOCR has built-in dictionaries, which can be used on demand. + `ppocr/utils/ppocr_keys_v1.txt` is a Chinese dictionary with 6623 characters. `ppocr/utils/ic15_dict.txt` is an English dictionary with 63 characters @@ -123,8 +134,6 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a `ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters -You can use it on demand. - The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**, If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) and we will thank you in the Repo. @@ -136,14 +145,14 @@ To customize the dict file, please modify the `character_dict_path` field in `co If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch. -- Add space category +#### 1.4 Add space category If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`. **Note: use_space_char only takes effect when character_type=ch** -### TRAINING +### 2 TRAINING PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example: @@ -166,7 +175,7 @@ Start training: python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml ``` -- Data Augmentation +#### 2.1 Data Augmentation PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file. @@ -175,7 +184,7 @@ The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, rand Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) -- Training +#### 2.2 Training PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/rec/rec_icdar15_train.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/rec_CRNN/best_accuracy` during the evaluation process. @@ -268,7 +277,7 @@ Eval: **Note that the configuration file for prediction/evaluation must be consistent with the training.** -- Multi-language +#### 2.3 Multi-language PaddleOCR currently supports 26 (except Chinese) language recognition. A multi-language configuration file template is provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。 @@ -420,7 +429,7 @@ Eval: ``` -### EVALUATION +### 3 EVALUATION The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file. @@ -430,10 +439,10 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec ``` -### PREDICTION +### 4 PREDICTION -* Training engine prediction +#### 4.1 Training engine prediction Using the model trained by paddleocr, you can quickly get prediction through the following script. diff --git a/doc/doc_en/whl_en.md b/doc/doc_en/whl_en.md index 1ef14f1427eb4c1a2a504f4a420cd43c8444aeac..ae4d34923535779d89594ee7f6fa4259f38ba497 100644 --- a/doc/doc_en/whl_en.md +++ b/doc/doc_en/whl_en.md @@ -1,7 +1,7 @@ # paddleocr package -## Get started quickly -### install package +## 1 Get started quickly +### 1.1 install package install by pypi ```bash pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+ @@ -12,9 +12,11 @@ build own whl package and install python3 setup.py bdist_wheel pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version of paddleocr ``` -### 1. Use by code +## 2 Use +### 2.1 Use by code +The paddleocr whl package will automatically download the ppocr lightweight model as the default model, which can be customized and replaced according to the section 3 **Custom Model**. -* detection classification and recognition +* detection angle classification and recognition ```python from paddleocr import PaddleOCR,draw_ocr # Paddleocr supports Chinese, English, French, German, Korean and Japanese. @@ -163,7 +165,7 @@ Output will be a list, each item contains classification result and confidence ['0', 0.99999964] ``` -### Use by command line +### 2.2 Use by command line show help information ```bash @@ -239,11 +241,11 @@ Output will be a list, each item contains classification result and confidence ['0', 0.99999964] ``` -## Use custom model +## 3 Use custom model When the built-in model cannot meet the needs, you need to use your own trained model. First, refer to the first section of [inference_en.md](./inference_en.md) to convert your det and rec model to inference model, and then use it as follows -### 1. Use by code +### 3.1 Use by code ```python from paddleocr import PaddleOCR,draw_ocr @@ -265,17 +267,17 @@ im_show = Image.fromarray(im_show) im_show.save('result.jpg') ``` -### Use by command line +### 3.2 Use by command line ```bash paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true ``` -### Use web images or numpy array as input +## 4 Use web images or numpy array as input -1. Web image +### 4.1 Web image -Use by code +- Use by code ```python from paddleocr import PaddleOCR, draw_ocr ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory @@ -294,12 +296,12 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc im_show = Image.fromarray(im_show) im_show.save('result.jpg') ``` -Use by command line +- Use by command line ```bash paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true ``` -2. Numpy array +### 4.2 Numpy array Support numpy array as input only when used by code ```python @@ -324,7 +326,7 @@ im_show.save('result.jpg') ``` -## Parameter Description +## 5 Parameter Description | Parameter | Description | Default value | |-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------| diff --git a/doc/imgs_results/angle_class_example.jpg b/doc/imgs_results/angle_class_example.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8e683be32cdb20e964a7154980d5b1d33d6a8eca Binary files /dev/null and b/doc/imgs_results/angle_class_example.jpg differ