Merge pull request #1905 from littletomatodonkey/dyg/fix_typo

fix doc

doc/doc_ch/recognition.md

@@ -133,7 +133,7 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，
 您可以按需使用。
 
 目前的多语言模型仍处在demo阶段，会持续优化模型并补充语种，**非常欢迎您为我们提供其他语言的字典和字体**，
-如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict) 将语料文件提交至[corpus](../../ppocr/utils/corpus)，我们会在Repo中感谢您。
+如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict)，我们会在Repo中感谢您。
 
 - 自定义字典
 

doc/doc_en/recognition_en.md

@@ -126,7 +126,7 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a
 You can use it on demand.
 
 The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
-If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) or corpus file to [corpus](../../ppocr/utils/corpus) and we will thank you in the Repo.
+If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) and we will thank you in the Repo.
 
 
 To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.

tools/program.py

@@ -222,8 +222,8 @@ def train(config,
                 batch = [item.numpy() for item in batch]
                 post_result = post_process_class(preds, batch[1])
                 eval_class(post_result, batch)
-                metirc = eval_class.get_metric()
-                train_stats.update(metirc)
+                metric = eval_class.get_metric()
+                train_stats.update(metric)
 
             if vdl_writer is not None and dist.get_rank() == 0:
                 for k, v in train_stats.get().items():
@@ -251,7 +251,7 @@ def train(config,
                         min_average_window=10000,
                         max_average_window=15625)
                     Model_Average.apply()
-                cur_metric = eval(model, valid_dataloader, post_process_class, 
+                cur_metric = eval(model, valid_dataloader, post_process_class,
                                   eval_class)
                 cur_metric_str = 'cur metric, {}'.format(', '.join(
                     ['{}: {}'.format(k, v) for k, v in cur_metric.items()]))