Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dyg_db

README_ch.md

@@ -4,16 +4,18 @@
 PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
 
 **近期更新**
+- 2020.12.07 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数124个，并且计划以后每周一都会更新，欢迎大家持续关注。
+- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
 - 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
-- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
+- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipeline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
 - 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型，媲美商业效果。[模型下载](#模型下载)
 - 2020.9.17 更新[英文识别模型](./doc/doc_ch/models_list.md#英文识别模型)和[多语言识别模型](doc/doc_ch/models_list.md#多语言识别模型)，已支持`德语、法语、日语、韩语`，更多语种识别模型将持续更新。
-- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./doc/doc_ch/FAQ.md)
 - 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./doc/doc_ch/whl.md)
 - 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
 - [More](./doc/doc_ch/update.md)
 
 
+
 ## 特性
 
 - PPOCR系列高质量预训练模型，准确的识别效果
@@ -48,15 +50,14 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - 代码体验：从[快速安装](./doc/doc_ch/installation.md) 开始
 
 <a name="模型下载"></a>
-## PP-OCR 1.1系列模型列表（9月17日更新）
+## PP-OCR 2.0系列模型列表（更新中）
 
 | 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](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_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](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar)          |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_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](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb)| [推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|  
+| 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](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[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（143M）   |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](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[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)  |  
 
-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
 - [快速安装](./doc/doc_ch/installation.md)
@@ -141,6 +142,7 @@ PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框
 ## 贡献代码
 我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
 
+
 - 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档
 - 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
 - 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
@@ -148,3 +150,6 @@ PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框
 - 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
 - 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格。
 - 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务。
+- 非常感谢 [lijinhan](https://github.com/lijinhan) 给PaddleOCR增加java SpringBoot 调用OCR Hubserving接口完成对OCR服务化部署的使用。
+- 非常感谢 [Mejans](https://github.com/Mejans) 给PaddleOCR增加新语言奥克西坦语Occitan的字典和语料。
+- 非常感谢 [Evezerest](https://github.com/Evezerest)， [ninetailskim](https://github.com/ninetailskim)， [edencfc](https://github.com/edencfc)， [BeyondYourself](https://github.com/BeyondYourself)， [1084667371](https://github.com/1084667371) 贡献了PPOCRLabel的完整代码。

configs/cls/cls_mv3.yml

@@ -8,7 +8,6 @@ Global:
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [0, 1000]
   # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
   cal_metric_during_train: True
   pretrained_model:
   checkpoints:

configs/det/bak/det_r50_vd_db.yml

-Global:
-  use_gpu: true
-  epoch_num: 1200
-  log_smooth_window: 20
-  print_batch_step: 2
-  save_model_dir: ./output/det_r50_vd/
-  save_epoch_step: 1200
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 8
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
-  cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: True
-  infer_img: doc/imgs_en/img_10.jpg
-  save_res_path: ./output/det_db/predicts_db.txt
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  learning_rate:
-    lr: 0.001
-  regularizer:
-    name: 'L2'
-    factor: 0
-
-Architecture:
-  type: det
-  algorithm: DB
-  Transform:
-  Backbone:
-    name: ResNet
-    layers: 50
-  Neck:
-    name: FPN
-    out_channels: 256
-  Head:
-    name: DBHead
-    k: 50
-
-Loss:
-  name: DBLoss
-  balance_loss: true
-  main_loss_type: DiceLoss
-  alpha: 5
-  beta: 10
-  ohem_ratio: 3
-
-PostProcess:
-  name: DBPostProcess
-  thresh: 0.3
-  box_thresh: 0.6
-  max_candidates: 1000
-  unclip_ratio: 1.5
-
-Metric:
-  name: DetMetric
-  main_indicator: hmean
-
-TRAIN:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./detection/
-    file_list:
-      - ./detection/train_icdar2015_label.txt # dataset1
-    ratio_list: [1.0]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - DetLabelEncode: # Class handling label
-      - IaaAugment:
-          augmenter_args:
-            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
-            - { 'type': Affine, 'args': { 'rotate': [ -10,10 ] } }
-            - { 'type': Resize,'args': { 'size': [ 0.5,3 ] } }
-      - EastRandomCropData:
-          size: [ 640,640 ]
-          max_tries: 50
-          keep_ratio: true
-      - MakeBorderMap:
-          shrink_ratio: 0.4
-          thresh_min: 0.3
-          thresh_max: 0.7
-      - MakeShrinkMap:
-          shrink_ratio: 0.4
-          min_text_size: 8
-      - NormalizeImage:
-          scale: 1./255.
-          mean: [ 0.485, 0.456, 0.406 ]
-          std: [ 0.229, 0.224, 0.225 ]
-          order: 'hwc'
-      - ToCHWImage:
-      - keepKeys:
-          keep_keys: ['image','threshold_map','threshold_mask','shrink_map','shrink_mask'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    drop_last: False
-    batch_size: 16
-    num_workers: 8
-
-EVAL:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./detection/
-    file_list:
-      - ./detection/test_icdar2015_label.txt
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - DetLabelEncode: # Class handling label
-      - DetResizeForTest:
-          image_shape: [736,1280]
-      - NormalizeImage:
-          scale: 1./255.
-          mean: [ 0.485, 0.456, 0.406 ]
-          std: [ 0.229, 0.224, 0.225 ]
-          order: 'hwc'
-      - ToCHWImage:
-      - keepKeys:
-          keep_keys: ['image','shape','polys','ignore_tags']
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size: 1 # must be 1
-    num_workers: 8
\ No newline at end of file

configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml

@@ -11,7 +11,7 @@ Global:
   load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
-  checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
+  checkpoints:
   save_inference_dir:
   use_visualdl: False
   infer_img: doc/imgs_en/img_10.jpg

configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml

@@ -11,7 +11,7 @@ Global:
   load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/ResNet18_vd_pretrained
-  checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
+  checkpoints:
   save_inference_dir:
   use_visualdl: False
   infer_img: doc/imgs_en/img_10.jpg

configs/det/det_mv3_db.yml

@@ -11,7 +11,7 @@ Global:
   load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
-  checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
+  checkpoints:
   save_inference_dir:
   use_visualdl: False
   infer_img: doc/imgs_en/img_10.jpg

configs/det/det_r50_vd_db.yml

@@ -3,7 +3,7 @@ Global:
   epoch_num: 1200
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: ./output/det_rc/det_r50_vd/
+  save_model_dir: ./output/det_r50_vd/
   save_epoch_step: 1200
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [5000,4000]

configs/rec/bak/rec_mv3_none_bilstm_ctc_simple.yml

-Global:
-  use_gpu: false
-  epoch_num: 500
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
-  save_epoch_step: 500
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 127
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
-  # for data or label process
-  max_text_length: 80
-  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: 'ch'
-  use_space_char: False
-  infer_mode: False
-  use_tps: False
-
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  learning_rate:
-    lr: 0.001
-  regularizer:
-    name: 'L2'
-    factor: 0.00001
-
-Architecture:
-  type: rec
-  algorithm: CRNN
-  Transform:
-  Backbone:
-    name: MobileNetV3
-    scale: 0.5
-    model_name: small
-    small_stride: [ 1, 2, 2, 2 ]
-  Neck:
-    name: SequenceEncoder
-    encoder_type: fc
-    hidden_size: 96
-  Head:
-    name: CTC
-    fc_decay: 0.00001
-
-Loss:
-  name: CTCLoss
-
-PostProcess:
-  name: CTCLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-TRAIN:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/train.txt # dataset1
-    ratio_list: [ 0.4,0.6 ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecAug:
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    batch_size: 256
-    shuffle: True
-    drop_last: True
-    num_workers: 8
-
-EVAL:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/val.txt
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size: 256
-    num_workers: 8

configs/rec/bak/rec_r34_vd_none_bilstm_ctc.yml

-Global:
-  use_gpu: false
-  epoch_num: 500
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output/rec/res34_none_bilstm_ctc/
-  save_epoch_step: 500
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 127
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
-  # for data or label process
-  max_text_length: 80
-  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: 'ch'
-  use_space_char: False
-  infer_mode: False
-  use_tps: False
-
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  learning_rate:
-    lr: 0.001
-  regularizer:
-    name: 'L2'
-    factor: 0.00001
-
-Architecture:
-  type: rec
-  algorithm: CRNN
-  Transform:
-  Backbone:
-    name: ResNet
-    layers: 34
-  Neck:
-    name: SequenceEncoder
-    encoder_type: fc
-    hidden_size: 96
-  Head:
-    name: CTC
-    fc_decay: 0.00001
-
-Loss:
-  name: CTCLoss
-
-PostProcess:
-  name: CTCLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-TRAIN:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/train.txt # dataset1
-    ratio_list: [ 0.4,0.6 ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecAug:
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    batch_size: 256
-    shuffle: True
-    drop_last: True
-    num_workers: 8
-
-EVAL:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/val.txt
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size: 256
-    num_workers: 8

configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yaml → configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml

@@ -3,7 +3,7 @@ Global:
   epoch_num: 500
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: ./output/rec_chinese_common_v1.1
+  save_model_dir: ./output/rec_chinese_common_v2.0
   save_epoch_step: 3
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [0, 2000]

configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yaml → configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml

@@ -3,7 +3,7 @@ Global:
   epoch_num: 500
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: ./output/rec_chinese_lite_v1.1
+  save_model_dir: ./output/rec_chinese_lite_v2.0
   save_epoch_step: 3
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [0, 2000]
@@ -19,7 +19,7 @@ Global:
   character_type: ch
   max_text_length: 25
   infer_mode: False
-  use_space_char: False
+  use_space_char: True
 
 
 Optimizer:

configs/rec/multi_language/rec_en_number_lite_train.yml

 Global:
-  use_gpu: true
+  use_gpu: True
   epoch_num: 500
   log_smooth_window: 20
   print_batch_step: 10
@@ -15,7 +15,7 @@ Global:
   use_visualdl: False
   infer_img:
   # for data or label process
-  character_dict_path: ppocr/utils/dict/ic15_dict.txt
+  character_dict_path: ppocr/utils/dict/en_dict.txt
   character_type: ch
   max_text_length: 25
   infer_mode: False

configs/rec/multi_language/rec_french_lite_train.yml

 Global:
-  use_gpu: true
+  use_gpu: True
   epoch_num: 500
   log_smooth_window: 20
   print_batch_step: 10
@@ -9,9 +9,9 @@ Global:
   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:
+  pretrained_model: 
   checkpoints: 
-  save_inference_dir:
+  save_inference_dir: 
   use_visualdl: False
   infer_img:
   # for data or label process
@@ -19,7 +19,7 @@ Global:
   character_type: french
   max_text_length: 25
   infer_mode: False
-  use_space_char: True
+  use_space_char: False
 
 
 Optimizer:

configs/rec/multi_language/rec_german_lite_train.yml

 Global:
-  use_gpu: true
+  use_gpu: True
   epoch_num: 500
   log_smooth_window: 20
   print_batch_step: 10
@@ -19,7 +19,7 @@ Global:
   character_type: german
   max_text_length: 25
   infer_mode: False
-  use_space_char: True
+  use_space_char: False
 
 
 Optimizer:

configs/rec/multi_language/rec_japan_lite_train.yml

 Global:
-  use_gpu: true
+  use_gpu: True
   epoch_num: 500
   log_smooth_window: 20
   print_batch_step: 10
@@ -19,7 +19,7 @@ Global:
   character_type: japan
   max_text_length: 25
   infer_mode: False
-  use_space_char: True
+  use_space_char: False
 
 
 Optimizer:

configs/rec/multi_language/rec_korean_lite_train.yml

 Global:
-  use_gpu: true
+  use_gpu: True
   epoch_num: 500
   log_smooth_window: 20
   print_batch_step: 10
@@ -19,7 +19,7 @@ Global:
   character_type: korean
   max_text_length: 25
   infer_mode: False
-  use_space_char: True
+  use_space_char: False
 
 
 Optimizer:

configs/rec/bak/rec_r34_vd_none_none_ctc.yml → configs/rec/rec_icdar15_train.yml

 Global:
-  use_gpu: false
-  epoch_num: 500
+  use_gpu: true
+  epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: ./output/rec/res34_none_none_ctc/
-  save_epoch_step: 500
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 127
+  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
-  load_static_weights: True
   cal_metric_during_train: True
   pretrained_model:
   checkpoints:
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
-  max_text_length: 80
-  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: 'ch'
-  use_space_char: False
+  character_dict_path: ppocr/utils/ic15_dict.txt
+  character_type: ch
+  max_text_length: 25
   infer_mode: False
-  use_tps: False
-
+  use_space_char: False
 
 Optimizer:
   name: Adam
   beta1: 0.9
   beta2: 0.999
-  learning_rate:
-    lr: 0.001
+  lr:
+    learning_rate: 0.0005
   regularizer:
     name: 'L2'
-    factor: 0.00001
+    factor: 0
 
 Architecture:
-  type: rec
+  model_type: rec
   algorithm: CRNN
   Transform:
   Backbone:
@@ -43,10 +40,11 @@ Architecture:
     layers: 34
   Neck:
     name: SequenceEncoder
-    encoder_type: reshape
+    encoder_type: rnn
+    hidden_size: 256
   Head:
-    name: CTC
-    fc_decay: 0.00001
+    name: CTCHead
+    fc_decay: 0
 
 Loss:
   name: CTCLoss
@@ -58,46 +56,42 @@ Metric:
   name: RecMetric
   main_indicator: acc
 
-TRAIN:
+Train:
   dataset:
     name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/train.txt # dataset1
-    ratio_list: [ 0.4,0.6 ]
+    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
-      - RecAug:
       - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
   loader:
-    batch_size: 256
     shuffle: True
+    batch_size_per_card: 256
     drop_last: True
     num_workers: 8
 
-EVAL:
+Eval:
   dataset:
     name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/val.txt
+    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: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
+          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: 256
-    num_workers: 8
+    batch_size_per_card: 256
+    num_workers: 4

deploy/cpp_infer/src/ocr_cls.cpp

@@ -81,7 +81,8 @@ cv::Mat Classifier::Run(cv::Mat &img) {
 
 void Classifier::LoadModel(const std::string &model_dir) {
   AnalysisConfig config;
-  config.SetModel(model_dir + "/model", model_dir + "/params");
+  config.SetModel(model_dir + "/inference.pdmodel",
+                  model_dir + "/inference.pdiparams");
 
   if (this->use_gpu_) {
     config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);

deploy/cpp_infer/src/ocr_det.cpp

@@ -18,7 +18,8 @@ namespace PaddleOCR {
 
 void DBDetector::LoadModel(const std::string &model_dir) {
   AnalysisConfig config;
-  config.SetModel(model_dir + "/model", model_dir + "/params");
+  config.SetModel(model_dir + "/inference.pdmodel",
+                  model_dir + "/inference.pdiparams");
 
   if (this->use_gpu_) {
     config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);