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

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)
@@ -76,9 +77,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
     - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
     - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
     - [服务化部署](./deploy/hubserving/readme.md)
-    - [端侧部署](./deploy/lite/readme.md)
-    - [模型量化](./deploy/slim/quantization/README.md)
-    - [模型裁剪](./deploy/slim/prune/README.md)
+    - [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
+    - [模型量化](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README.md)
+    - [模型裁剪](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/README.md)
     - [Benchmark](./doc/doc_ch/benchmark.md)
 - 数据集
     - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
@@ -96,6 +97,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - [许可证书](#许可证书)
 - [贡献代码](#贡献代码)
 
+***注意：动态图端侧部署仍在开发中，目前仅支持动态图训练、python端预测，C++预测，
+如果您有需要移动端部署案例或者量化裁剪，请切换到静态图分支；***
+
 <a name="PP-OCR"></a>
 ## PP-OCR Pipline
 <div align="center">
@@ -141,6 +145,7 @@ 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 +153,6 @@ 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/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/android_demo/.gitignore

-*.iml
-.gradle
-/local.properties
-/.idea/*
-.DS_Store
-/build
-/captures
-.externalNativeBuild
-

deploy/android_demo/README.md

-# 如何快速测试
-### 1. 安装最新版本的Android Studio
-可以从https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
-
-### 2. 按照NDK 20 以上版本 
-Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。
-
-如果您是初学者，可以用以下方式安装和测试NDK编译环境。
-点击 File -> New ->New Project，  新建  "Native C++" project
-
-### 3. 导入项目
-点击 File->New->Import Project...， 然后跟着Android Studio的引导导入
-
-
-# 获得更多支持
-前往[端计算模型生成平台EasyEdge](https://ai.baidu.com/easyedge/app/open_source_demo?referrerUrl=paddlelite)，获得更多开发支持：
-
-- Demo APP：可使用手机扫码安装，方便手机端快速体验文字识别
-- SDK：模型被封装为适配不同芯片硬件和操作系统SDK，包括完善的接口，方便进行二次开发

deploy/android_demo/app/.gitignore

-/build

deploy/android_demo/app/build.gradle

-import java.security.MessageDigest
-
-apply plugin: 'com.android.application'
-
-android {
-    compileSdkVersion 29
-    defaultConfig {
-        applicationId "com.baidu.paddle.lite.demo.ocr"
-        minSdkVersion 23
-        targetSdkVersion 29
-        versionCode 1
-        versionName "1.0"
-        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
-        externalNativeBuild {
-            cmake {
-                cppFlags "-std=c++11 -frtti -fexceptions -Wno-format"
-                arguments '-DANDROID_PLATFORM=android-23', '-DANDROID_STL=c++_shared' ,"-DANDROID_ARM_NEON=TRUE"
-            }
-        }
-        ndk {
-            // abiFilters "arm64-v8a", "armeabi-v7a"
-            abiFilters   "arm64-v8a", "armeabi-v7a"
-            ldLibs "jnigraphics"
-        }
-    }
-    buildTypes {
-        release {
-            minifyEnabled false
-            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
-        }
-    }
-    externalNativeBuild {
-        cmake {
-            path "src/main/cpp/CMakeLists.txt"
-            version "3.10.2"
-        }
-    }
-}
-
-dependencies {
-    implementation fileTree(include: ['*.jar'], dir: 'libs')
-    implementation 'androidx.appcompat:appcompat:1.1.0'
-    implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
-    testImplementation 'junit:junit:4.12'
-    androidTestImplementation 'com.android.support.test:runner:1.0.2'
-    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
-}
-
-def archives = [
-        [
-                'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/paddle_lite_libs_v2_6_1.tar.gz',
-                'dest': 'PaddleLite'
-        ],
-        [
-                'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/opencv-4.2.0-android-sdk.tar.gz',
-                'dest': 'OpenCV'
-        ],
-        [
-                'src' : 'https://paddleocr.bj.bcebos.com/deploy/lite/ocr_v1_for_cpu.tar.gz',
-                'dest' : 'src/main/assets/models/ocr_v1_for_cpu'
-        ]
-]
-
-task downloadAndExtractArchives(type: DefaultTask) {
-    doFirst {
-        println "Downloading and extracting archives including libs and models"
-    }
-    doLast {
-        // Prepare cache folder for archives
-        String cachePath = "cache"
-        if (!file("${cachePath}").exists()) {
-            mkdir "${cachePath}"
-        }
-        archives.eachWithIndex { archive, index ->
-            MessageDigest messageDigest = MessageDigest.getInstance('MD5')
-            messageDigest.update(archive.src.bytes)
-            String cacheName = new BigInteger(1, messageDigest.digest()).toString(32)
-            // Download the target archive if not exists
-            boolean copyFiles = !file("${archive.dest}").exists()
-            if (!file("${cachePath}/${cacheName}.tar.gz").exists()) {
-                ant.get(src: archive.src, dest: file("${cachePath}/${cacheName}.tar.gz"))
-                copyFiles = true; // force to copy files from the latest archive files
-            }
-            // Extract the target archive if its dest path does not exists
-            if (copyFiles) {
-                copy {
-                    from tarTree("${cachePath}/${cacheName}.tar.gz")
-                    into "${archive.dest}"
-                }
-            }
-        }
-    }
-}
-preBuild.dependsOn downloadAndExtractArchives
\ No newline at end of file

deploy/android_demo/app/proguard-rules.pro

-# Add project specific ProGuard rules here.
-# You can control the set of applied configuration files using the
-# proguardFiles setting in build.gradle.
-#
-# For more details, see
-#   http://developer.android.com/guide/developing/tools/proguard.html
-
-# If your project uses WebView with JS, uncomment the following
-# and specify the fully qualified class name to the JavaScript interface
-# class:
-#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
-#   public *;
-#}
-
-# Uncomment this to preserve the line number information for
-# debugging stack traces.
-#-keepattributes SourceFile,LineNumberTable
-
-# If you keep the line number information, uncomment this to
-# hide the original source file name.
-#-renamesourcefileattribute SourceFile

deploy/android_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ocr/ExampleInstrumentedTest.java

-package com.baidu.paddle.lite.demo.ocr;
-
-import android.content.Context;
-import android.support.test.InstrumentationRegistry;
-import android.support.test.runner.AndroidJUnit4;
-
-import org.junit.Test;
-import org.junit.runner.RunWith;
-
-import static org.junit.Assert.*;
-
-/**
- * Instrumented test, which will execute on an Android device.
- *
- * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
- */
-@RunWith(AndroidJUnit4.class)
-public class ExampleInstrumentedTest {
-    @Test
-    public void useAppContext() {
-        // Context of the app under test.
-        Context appContext = InstrumentationRegistry.getTargetContext();
-
-        assertEquals("com.baidu.paddle.lite.demo", appContext.getPackageName());
-    }
-}

deploy/android_demo/app/src/main/AndroidManifest.xml

-<?xml version="1.0" encoding="utf-8"?>
-<manifest xmlns:android="http://schemas.android.com/apk/res/android"
-          package="com.baidu.paddle.lite.demo.ocr">
-
-    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
-    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
-    <uses-permission android:name="android.permission.CAMERA"/>
-
-
-    <application
-            android:allowBackup="true"
-            android:icon="@mipmap/ic_launcher"
-            android:label="@string/app_name"
-            android:roundIcon="@mipmap/ic_launcher_round"
-            android:supportsRtl="true"
-            android:theme="@style/AppTheme">
-        <!-- to test MiniActivity, change this to com.baidu.paddle.lite.demo.ocr.MiniActivity -->
-        <activity android:name="com.baidu.paddle.lite.demo.ocr.MainActivity">
-            <intent-filter>
-                <action android:name="android.intent.action.MAIN"/>
-                <category android:name="android.intent.category.LAUNCHER"/>
-            </intent-filter>
-        </activity>
-        <activity
-                android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
-                android:label="Settings">
-        </activity>
-        <provider
-            android:name="androidx.core.content.FileProvider"
-            android:authorities="com.baidu.paddle.lite.demo.ocr.fileprovider"
-            android:exported="false"
-            android:grantUriPermissions="true">
-            <meta-data
-                android:name="android.support.FILE_PROVIDER_PATHS"
-                android:resource="@xml/file_paths"></meta-data>
-        </provider>
-    </application>
-
-</manifest>
\ No newline at end of file