fix bug and update doc

README_cn.md

@@ -122,7 +122,10 @@ PaddleOCR开源的文本识别算法列表：
 - [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
 - [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
 - [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
-- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研, coming soon)
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研)
+
+*备注：* SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广,增广后的数据可以在[百度网盘](todo)上下载。
+原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
 
 参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
 
@@ -136,6 +139,7 @@ PaddleOCR开源的文本识别算法列表：
 |STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
 |RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
 |RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
+|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
 
 使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：  
 

ppocr/utils/character.py

@@ -25,7 +25,7 @@ class CharacterOps(object):
     def __init__(self, config):
         self.character_type = config['character_type']
         self.loss_type = config['loss_type']
-        self.max_text_len = config['max_text_length']
+        self.max_text_len = 25
         if self.character_type == "en":
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)

tools/infer/predict_rec.py

@@ -40,7 +40,8 @@ class TextRecognizer(object):
         char_ops_params = {
             "character_type": args.rec_char_type,
             "character_dict_path": args.rec_char_dict_path,
-            "use_space_char": args.use_space_char
+            "use_space_char": args.use_space_char,
+            "max_text_length": args.max_text_length
         }
         if self.rec_algorithm != "RARE":
             char_ops_params['loss_type'] = 'ctc'

tools/infer/utility.py

@@ -56,8 +56,8 @@ def parse_args():
     #params for text recognizer
     parser.add_argument("--rec_algorithm", type=str, default='CRNN')
     parser.add_argument("--rec_model_dir", type=str)
-    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
-    parser.add_argument("--rec_char_type", type=str, default='ch')
+    parser.add_argument("--rec_image_shape", type=str, default="1, 64, 320")
+    parser.add_argument("--rec_char_type", type=str, default='en')
     parser.add_argument("--rec_batch_num", type=int, default=30)
     parser.add_argument(
         "--rec_char_dict_path",

tools/program.py

@@ -194,13 +194,14 @@ def build(config, main_prog, startup_prog, mode):
                 global_lr = optimizer._global_learning_rate()
                 fetch_name_list.insert(0, "lr")
                 fetch_varname_list.insert(0, global_lr.name)
-                if config['Global']["loss_type"] == 'srn':
-                    model_average = fluid.optimizer.ModelAverage(
-                        config['Global']['average_window'],
-                        min_average_window=config['Global'][
-                            'min_average_window'],
-                        max_average_window=config['Global'][
-                            'max_average_window'])
+                if "loss_type" in config["Global"]:
+                    if config['Global']["loss_type"] == 'srn':
+                        model_average = fluid.optimizer.ModelAverage(
+                            config['Global']['average_window'],
+                            min_average_window=config['Global'][
+                                'min_average_window'],
+                            max_average_window=config['Global'][
+                                'max_average_window'])
 
     return (dataloader, fetch_name_list, fetch_varname_list, opt_loss_name,
             model_average)