update multi dic and export

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
@@ -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:

deploy/cpp_infer/src/ocr_cls.cpp

@@ -81,7 +81,7 @@ 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 + ".pdmodel", model_dir + ".pdiparams");
 
   if (this->use_gpu_) {
     config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);

deploy/cpp_infer/src/ocr_det.cpp

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

deploy/cpp_infer/src/ocr_rec.cpp

@@ -103,7 +103,7 @@ void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
 
 void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   AnalysisConfig config;
-  config.SetModel(model_dir + "/model", model_dir + "/params");
+  config.SetModel(model_dir + ".pdmodel", model_dir + ".pdiparams");
 
   if (this->use_gpu_) {
     config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);

ppocr/utils/dict/en_dict.txt

+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+ 

ppocr/utils/dict/french_dict.txt

@@ -133,3 +133,4 @@ j
 Å
 $
 #
+ 

ppocr/utils/dict/german_dict.txt

@@ -124,3 +124,4 @@ z
 å
 æ
 é
+ 

ppocr/utils/dict/japan_dict.txt

@@ -4396,3 +4396,4 @@ z
 ｚ
 ～
 ･
+ 

ppocr/utils/dict/korean_dict.txt

@@ -179,7 +179,7 @@ z
 с
 т
 я
-
+​
 ’
 “
 ”
@@ -3685,3 +3685,4 @@ z
 茶
 切
 宅
+ 

tools/export_model.py

@@ -39,26 +39,12 @@ def parse_args():
     return parser.parse_args()
 
 
-class Model(paddle.nn.Layer):
-    def __init__(self, model):
-        super(Model, self).__init__()
-        self.pre_model = model
-
-    # Please modify the 'shape' according to actual needs
-    @to_static(input_spec=[
-        paddle.static.InputSpec(
-            shape=[None, 3, 640, 640], dtype='float32')
-    ])
-    def forward(self, inputs):
-        x = self.pre_model(inputs)
-        return x
-
-
 def main():
     FLAGS = parse_args()
     config = load_config(FLAGS.config)
     logger = get_logger()
     # build post process
+
     post_process_class = build_post_process(config['PostProcess'],
                                             config['Global'])
 
@@ -71,9 +57,16 @@ def main():
     init_model(config, model, logger)
     model.eval()
 
-    model = Model(model)
-    save_path = '{}/{}'.format(FLAGS.output_path,
+    save_path = '{}/{}/inference'.format(FLAGS.output_path,
                                          config['Architecture']['model_type'])
+    infer_shape = [3, 32, 100] if config['Architecture'][
+        'model_type'] != "det" else [3, 640, 640]
+    model = to_static(
+        model,
+        input_spec=[
+            paddle.static.InputSpec(
+                shape=[None] + infer_shape, dtype='float32')
+        ])
     paddle.jit.save(model, save_path)
     logger.info('inference model is saved to {}'.format(save_path))
 

tools/infer/utility.py

@@ -100,8 +100,8 @@ def create_predictor(args, mode, logger):
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
         sys.exit(0)
-    model_file_path = model_dir + "/model"
-    params_file_path = model_dir + "/params"
+    model_file_path = model_dir + ".pdmodel"
+    params_file_path = model_dir + ".pdiparams"
     if not os.path.exists(model_file_path):
         logger.info("not find model file path {}".format(model_file_path))
         sys.exit(0)
@@ -230,10 +230,10 @@ def draw_ocr_box_txt(image,
                 box[2][1], box[3][0], box[3][1]
             ],
             outline=color)
-        box_height = math.sqrt((box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][
-            1]) ** 2)
-        box_width = math.sqrt((box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][
-            1]) ** 2)
+        box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][
+            1])**2)
+        box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][
+            1])**2)
         if box_height > 2 * box_width:
             font_size = max(int(box_width * 0.9), 10)
             font = ImageFont.truetype(font_path, font_size, encoding="utf-8")