Update README.md (#2)

- Update README.md (75580bbc48efe57eb080b76e27431d343b0f3031)


Co-authored-by: Mélodie Boillet <mboillet@users.noreply.huggingface.co>

README.md

@@ -4,7 +4,7 @@ language:
 - zh
 task_categories:
 - image-to-text
-pretty_name: CASIA
+pretty_name: CASIA-HWDB2-line
 dataset_info:
   features:
   - name: image
@@ -19,12 +19,18 @@ dataset_info:
   - name: test
     num_examples: 10441
   dataset_size: 52160
+tags:
+  - atr
+  - htr
+  - ocr
+  - modern
+  - handwritten
 ---
 
-# CASIA Dataset
+# CASIA-HWDB2 - line level
 
 ## Table of Contents
-- [CASIA Dataset](#casia-dataset)
+- [CASIA-HWDB2 - line level](#casia-hwdb2-line-level)
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
@@ -35,15 +41,17 @@ dataset_info:
 
 ## Dataset Description
 
-- **Homepage:** [CASIA](http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html)
+- **Homepage:** [CASIA-HWDB2](http://www.nlpr.ia.ac.cn/databases/handwriting/Download.html)
 - **Paper:** [Online and offline handwritten Chinese character recognition: Benchmarking on new databases](https://www.sciencedirect.com/science/article/abs/pii/S0031320312002919)
 - **Point of Contact:** [TEKLIA](https://teklia.com)
 
 ## Dataset Summary
 
-The offline Chinese handwriting database (CASIA-HWDB) was built by the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences (CASIA). 
+The offline Chinese handwriting database (CASIA-HWDB2) was built by the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences (CASIA). 
 The handwritten samples were produced by 1,020 writers using Anoto pen on papers, such that both online and offline data were obtained.
 
+Note that all images are resized to a fixed height of 128 pixels.
+
 ### Languages
 
 All the documents in the dataset are written in Chinese.
@@ -62,5 +70,5 @@ All the documents in the dataset are written in Chinese.
 ### Data Fields
 
 
-- `image`: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
+- `image`: a PIL.Image.Image object containing the image. Note that when accessing the image column (using dataset[0]["image"]), the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
 - `text`: the label transcription of the image.