fix typo

configs/rec/crnn/README.md

@@ -4,7 +4,7 @@ English | [中文](https://github.com/mindspore-lab/mindocr/blob/main/configs/re
 <!--- Guideline: use url linked to abstract in ArXiv instead of PDF for fast loading.  -->
 
 > [An End-to-End Trainable Neural Network for Image-based Sequence
-Recognition and Its Application to Scene Text Recognition](https://https://arxiv.org/abs/1507.05717)
+Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
 
 ## 1. Introduction
 <!--- Guideline: Introduce the model and architectures. Cite if you use/adopt paper explanation from others. -->

configs/rec/crnn/README_CN.md

@@ -4,7 +4,7 @@
 <!--- Guideline: use url linked to abstract in ArXiv instead of PDF for fast loading.  -->
 
 > [An End-to-End Trainable Neural Network for Image-based Sequence
-Recognition and Its Application to Scene Text Recognition](https://https://arxiv.org/abs/1507.05717)
+Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
 
 ## 1. 模型描述
 <!--- Guideline: Introduce the model and architectures. Cite if you use/adopt paper explanation from others. -->

mindocr/data/builder.py

@@ -49,7 +49,7 @@ def build_dataset(
 
     Notes:
         - The main data process pipeline in MindSpore contains 3 parts: 1) load data files and generate source dataset, 2) perform per-data-row mapping such as image augmentation, 3) generate batch and apply batch mapping.
-        - Each of the three steps supports multiprocess. Detailed machenism can be seen in https://www.mindspore.cn/docs/zh-CN/r2.0.0-alpha/api_python/mindspore.dataset.html
+        - Each of the three steps supports multiprocess. Detailed mechanism can be seen in https://www.mindspore.cn/docs/zh-CN/r2.0.0-alpha/api_python/mindspore.dataset.html
         - A data row is a data tuple item containing multiple elements such as (image_i, mask_i, label_i). A data column corresponds to an element in the tuple like 'image', 'label'.
         - The total number of `num_workers` used for data loading and processing should not be larger than the maximum threads of the CPU. Otherwise, it will lead to resource competing overhead. Especially for distributed training, `num_parallel_workers` should not be too large to avoid thread competition.