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7
README_ch.md
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@ -54,11 +54,10 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 | | 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
| ------------ | --------------- | ----------------|---- | ---------- | -------- | | ------------ | --------------- | ----------------|---- | ---------- | -------- |
| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](link) / [预训练模型](link)|[推理模型](link) / [预训练模型](link) |[推理模型](link) / [预训练模型](link) | | 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](link) / [预训练模型](link) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| 中英文通用OCR模型（155.1M） |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](link) / [预训练模型](link) |[推理模型](link) / [预训练模型](link) |[推理模型](link) / [预训练模型](link) | | 中英文通用OCR模型（155.1M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](link) / [预训练模型](link) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
| 中英文超轻量压缩OCR模型（3.5M） | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |[推理模型](link) / [slim模型](link) |[推理模型](link) / [slim模型](link)| [推理模型](link) / [slim模型](link)|
更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](./doc/doc_ch/models_list.md) 更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](./doc/doc_ch/models_list.md)
## 文档教程 ## 文档教程
- [快速安装](./doc/doc_ch/installation.md) - [快速安装](./doc/doc_ch/installation.md)

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README_en.md
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@ -62,15 +62,11 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model | | Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Chinese and English ultra-lightweight OCR model (8.1M) | ch_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | | Chinese and English ultra-lightweight OCR model (8.1M) | ch_ppocr_mobile_v2.0_xx | Mobile & server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](link) / [pre-trained model](link) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| Chinese and English general OCR model (155.1M) | ch_ppocr_server_v1.1_xx | Server | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | | Chinese and English general OCR model (155.1M) | ch_ppocr_server_v2.0_xx | Server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[inference model](link) / [pre-trained model](link) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
| Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile | [inference model](link) / [slim model](link) | [inference model](link) / [slim model](link) | [inference model](link) / [slim model](link) |
| French ultra-lightweight OCR model (4.6M) | french_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - | [inference model](link) / [pre-trained model](link) |
| German ultra-lightweight OCR model (4.6M) | german_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link) | For more model downloads (including multiple languages), please refer to [PP-OCR v2.0 series model downloads](./doc/doc_en/models_list_en.md).
| Korean ultra-lightweight OCR model (5.9M) | korean_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link)|
| Japan ultra-lightweight OCR model (6.2M) | japan_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link) |
For more model downloads (including multiple languages), please refer to [PP-OCR v1.1 series model downloads](./doc/doc_en/models_list_en.md).
For a new language request, please refer to [Guideline for new language_requests](#language_requests). For a new language request, please refer to [Guideline for new language_requests](#language_requests).

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configs/rec/rec_icdar15_train.yml
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@ -0,0 +1,97 @@
Global:
use_gpu: true
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/ic15/
save_epoch_step: 3
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/ic15_dict.txt
character_type: ch
max_text_length: 25
infer_mode: False
use_space_char: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.0005
regularizer:
name: 'L2'
factor: 0
Architecture:
model_type: rec
algorithm: CRNN
Transform:
Backbone:
name: ResNet
layers: 34
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 256
Head:
name: CTCHead
fc_decay: 0
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list: ["./train_data/train_list.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 100]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 256
drop_last: True
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list: ["./train_data/train_list.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 100]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 4

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doc/doc_ch/recognition.md
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@ -37,8 +37,6 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
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。 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。
<a name="自定义数据集"></a> <a name="自定义数据集"></a>
* 使用自己数据集 * 使用自己数据集
@ -65,7 +63,7 @@ wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_t
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
``` ```
PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `train_data/gen_label.py`, 这里以训练集为例： PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `ppocr/utils/gen_label.py`, 这里以训练集为例：
``` ```
# 将官网下载的标签文件转换为 rec_gt_label.txt # 将官网下载的标签文件转换为 rec_gt_label.txt
@ -116,9 +114,9 @@ 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] 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]
`ppocr/utils/ppocr_keys_v1.txt` 是一个包含6623个字符的中文字典， `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， `ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典
`ppocr/utils/dict/french_dict.txt` 是一个包含118个字符的法文字典 `ppocr/utils/dict/french_dict.txt` 是一个包含118个字符的法文字典
@ -128,6 +126,8 @@ 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/german_dict.txt` 是一个包含131个字符的法文字典 `ppocr/utils/dict/german_dict.txt` 是一个包含131个字符的法文字典
`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。 您可以按需使用。
@ -155,10 +155,10 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 CRNN
``` ```
cd PaddleOCR/ cd PaddleOCR/
# 下载MobileNetV3的预训练模型 # 下载MobileNetV3的预训练模型
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_infer.tar
# 解压模型参数 # 解压模型参数
cd pretrain_models cd pretrain_models
tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar tar -xf rec_mv3_none_bilstm_ctc_v2.0_infer.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_infer.tar
``` ```
开始训练: 开始训练:
@ -204,9 +204,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| rec_mv3_tps_bilstm_attn.yml | RARE | Mobilenet_v3 large 0.5 | tps | BiLSTM | attention | | rec_mv3_tps_bilstm_attn.yml | RARE | Mobilenet_v3 large 0.5 | tps | BiLSTM | attention |
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc | | rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc | | rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
| rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc | | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件： 训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：

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doc/doc_en/recognition_en.md
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@ -114,11 +114,13 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a
`ppocr/utils/dict/french_dict.txt` is a French dictionary with 118 characters `ppocr/utils/dict/french_dict.txt` is a French dictionary with 118 characters
`ppocr/utils/dict/japan_dict.txt` is a French dictionary with 4399 characters `ppocr/utils/dict/japan_dict.txt` is a Japan dictionary with 4399 characters
`ppocr/utils/dict/korean_dict.txt` is a French dictionary with 3636 characters `ppocr/utils/dict/korean_dict.txt` is a Korean dictionary with 3636 characters
`ppocr/utils/dict/german_dict.txt` is a French dictionary with 131 characters `ppocr/utils/dict/german_dict.txt` is a German dictionary with 131 characters
`ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters
You can use it on demand. You can use it on demand.
@ -194,7 +196,6 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| rec_mv3_tps_bilstm_attn.yml | RARE | Mobilenet_v3 large 0.5 | tps | BiLSTM | attention | | rec_mv3_tps_bilstm_attn.yml | RARE | Mobilenet_v3 large 0.5 | tps | BiLSTM | attention |
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc | | rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc | | rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
| rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
| rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc | | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
For training Chinese data, it is recommended to use For training Chinese data, it is recommended to use

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