diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,3 +1,5 @@
+English [README](./README_en.md).
+
 # Release Notes
 
 ### Update 2020.04.21: 发布 cnocr V1.1.0
@@ -168,7 +170,7 @@ class CnOcr(object):
 
 * `model_name`: 模型名称，即上面表格第一列中的值。默认为 `conv-lite-fc`。
 * `model_epoch`: 模型迭代次数。默认为 `None`，表示使用默认的迭代次数值。对于模型名称 `conv-lite-fc`就是 `27`。
-* `cand_alphabet`: 待识别字符所在的候选集合。默认为 `None`，表示不限定识别字符范围。`cnocr.consts`中内置了两个候选集合：(1)数字和标点 `NUMBERS`；(1)英文字母、数字和标点 `ENG_LETTERS`。
+* `cand_alphabet`: 待识别字符所在的候选集合。默认为 `None`，表示不限定识别字符范围。`cnocr.consts`中内置了两个候选集合：(1) 数字和标点 `NUMBERS`；(2) 英文字母、数字和标点 `ENG_LETTERS`。
    * 例如对于图片 ![examples/hybrid.png](./examples/hybrid.png) ，不做约束时识别结果为 `o12345678`；如果加入数字约束时（`ocr = CnOcr(cand_alphabet=NUMBERS)`），识别结果为 `012345678`。
 * `root`: 模型文件所在的根目录。
    * Linux/Mac下默认值为 `~/.cnocr`，表示模型文件所处文件夹类似 `~/.cnocr/1.1.0/conv-lite-fc`。
@@ -281,7 +283,7 @@ print("Predicted Chars:", res)
 的预测结果如下：
 
 ```bash
-Predicted Chars: ['笠', '淡', '嘿', '骅', '谧', '鼎', '臭', '姚', '歼', '蠢', '驼', '耳', '裔', '挝', '涯', '狗', '蒽', '子', '犷'] 
+Predicted Chars: ['笠', '淡', '嘿', '骅', '谧', '鼎', '皋', '姚', '歼', '蠢', '驼', '耳', '胬', '挝', '涯', '狗', '蒽', '子', '犷'] 
 ```
 
 
diff --git a/README_en.md b/README_en.md
new file mode 100644
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+++ b/README_en.md
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+中文版说明请见[中文README](./README.md)。
+
+
+
+# Update 2019.07.25: release cnocr V1.0.0
+
+`cnocr` `v1.0.0` is released, which is more efficient for prediction. **The new version of the model is not compatible with the previous version.** So if upgrading, please download the latest model file again. See below for the details (same as before).
+
+
+
+Main changes are：
+
+-  **The new crnn model supports prediction for variable-width image files, so is more efficient for prediction.**
+-  Support fine-tuning the existing model with specific data.
+-  Fix bugs，such as `train accuracy` always `0`.
+-  Depended package `mxnet` is upgraded from `1.3.1`  to `1.4.1`.
+
+
+
+# cnocr
+
+A python package for Chinese OCR with available trained models.
+So it can be used directly after installed.
+
+The accuracy of the current crnn model is about `98.8%`.
+
+The project originates from our own ([爱因互动 Ein+](https://einplus.cn)) internal needs.
+Thanks for the internal supports.
+
+## Changes
+
+Most of the codes are adapted from [crnn-mxnet-chinese-text-recognition](https://github.com/diaomin/crnn-mxnet-chinese-text-recognition).
+Much thanks to the author.
+
+Some changes are:
+
+* use raw MXNet CTC Loss instead of WarpCTC Loss. No more complicated installation.
+* public pre-trained model for anyone. No more a-few-days training.
+* add online `predict` function and script. Easy to use.
+
+## Installation
+
+```bash
+pip install cnocr
+```
+
+> Please use Python3 (3.4, 3.5, 3.6 should work). Python2 is not tested.
+
+## Usage
+
+The first time cnocr is used, the model files will be downloaded automatically from 
+[Dropbox](https://www.dropbox.com/s/7w8l3mk4pvkt34w/cnocr-models-v1.0.0.zip?dl=0) to `~/.cnocr`. 
+
+The zip file will be extracted and you can find the resulting model files in `~/.cnocr/models` by default.
+In case the automatic download can't perform well, you can download the zip file manually 
+from [Baidu NetDisk](https://pan.baidu.com/s/1DWV3H2UWmzOU6d48UbTYVw) with extraction code `ss81`, and put the zip file to `~/.cnocr`. The code will do else.
+
+
+
+### Predict
+
+Three functions are provided for prediction.
+
+
+
+#### 1. `CnOcr.ocr(img_fp)`
+
+The function `cnOcr.ocr (img_fp)` can recognize texts in an image containing multiple lines of text (or single lines).
+
+
+
+**Function Description**
+
+- input parameter `img_fp`: image file path; or color image `mx.nd.NDArray` or `np.ndarray`, with shape `(height, width, 3)`, and the channels should be RGB formatted.
+- return: `List(List(Char))`,  such as:  `[['第', '一', '行'], ['第', '二', '行'], ['第', '三', '行']]`.
+  
+
+
+
+**Use Case**
+
+
+```python
+from cnocr import CnOcr
+ocr = CnOcr()
+res = ocr.ocr('examples/multi-line_cn1.png')
+print("Predicted Chars:", res)
+```
+
+or:
+
+```python
+import mxnet as mx
+from cnocr import CnOcr
+ocr = CnOcr()
+img_fp = 'examples/multi-line_cn1.png'
+img = mx.image.imread(img_fp, 1)
+res = ocr.ocr(img)
+print("Predicted Chars:", res)
+```
+
+The previous codes can recognize texts in the image file [examples/multi-line_cn1.png](./examples/multi-line_cn1.png):
+
+![examples/multi-line_cn1.png](./examples/multi-line_cn1.png)
+
+The OCR results shoule be:
+
+```bash
+Predicted Chars: [['网', '络', '支', '付', '并', '无', '本', '质', '的', '区', '别', '，', '因', '为'],
+                  ['每', '一', '个', '手', '机', '号', '码', '和', '邮', '件', '地', '址', '背', '后'],
+                  ['都', '会', '对', '应', '着', '一', '个', '账', '户', '一', '―', '这', '个', '账'],
+                  ['户', '可', '以', '是', '信', '用', '卡', '账', '户', '、', '借', '记', '卡', '账'],
+                  ['户', '，', '也', '包', '括', '邮', '局', '汇', '款', '、', '手', '机', '代'],
+                  ['收', '、', '电', '话', '代', '收', '、', '预', '付', '费', '卡', '和', '点', '卡'],
+                  ['等', '多', '种', '形', '式', '。']]
+```
+
+#### 2. `CnOcr.ocr_for_single_line(img_fp)`
+
+If you know that the image you're predicting contains only one line of text, function `CnOcr.ocr_for_single_line(img_fp)` can be used instead。Compared with `CnOcr.ocr()`, the result of `CnOcr.ocr_for_single_line()` is more reliable because the process of splitting lines is not required. 
+
+
+
+**Function Description**
+
+- input parameter `img_fp`: image file path; or color image `mx.nd.NDArray` or `np.ndarray`, with shape `[height, width]` or `[height, width, channel]`.  The optional channel should be `1` (gray image) or `3` (color image).
+- return: `List(Char)`,  such as:  `['你', '好']`.
+
+
+
+**Use Case**：
+
+```python
+from cnocr import CnOcr
+ocr = CnOcr()
+res = ocr.ocr_for_single_line('examples/rand_cn1.png')
+print("Predicted Chars:", res)
+```
+
+or:
+
+```python
+import mxnet as mx
+from cnocr import CnOcr
+ocr = CnOcr()
+img_fp = 'examples/rand_cn1.png'
+img = mx.image.imread(img_fp, 1)
+res = ocr.ocr_for_single_line(img)
+print("Predicted Chars:", res)
+```
+
+
+The previous codes can recognize texts in the image file  [examples/rand_cn1.png](./examples/rand_cn1.png)：
+
+![examples/rand_cn1.png](./examples/rand_cn1.png)
+
+The OCR results shoule be:
+
+```bash
+Predicted Chars: ['笠', '淡', '嘿', '骅', '谧', '鼎', '臭', '姚', '歼', '蠢', '驼', '耳', '裔', '挝', '涯', '狗', '蒽', '子', '犷'] 
+```
+
+#### 3. `CnOcr.ocr_for_single_lines(img_list)`
+
+Function `CnOcr.ocr_for_single_lines(img_list)` can predict a number of single-line-text image arrays batchly. Actually `CnOcr.ocr(img_fp)` and `CnOcr.ocr_for_single_line(img_fp)` both invoke `CnOcr.ocr_for_single_lines(img_list)` internally.
+
+
+
+**Function Description**
+
+- input parameter `img_list`: list of images, in which each element should be a line image array,  with type `mx.nd.NDArray` or `np.ndarray`.  Each element should be a tensor with values ranging from `0` to` 255`, and with shape `[height, width]` or `[height, width, channel]`.  The optional channel should be `1` (gray image) or `3` (color image).
+- return: `List(List(Char))`,  such as:  `[['第', '一', '行'], ['第', '二', '行'], ['第', '三', '行']]`.
+
+
+
+**Use Case**：
+
+```python
+import mxnet as mx
+from cnocr import CnOcr
+ocr = CnOcr()
+img_fp = 'examples/multi-line_cn1.png'
+img = mx.image.imread(img_fp, 1).asnumpy()
+line_imgs = line_split(img, blank=True)
+line_img_list = [line_img for line_img, _ in line_imgs]
+res = ocr.ocr_for_single_lines(line_img_list)
+print("Predicted Chars:", res)
+```
+
+More use cases can be found at [tests/test_cnocr.py](./tests/test_cnocr.py).
+
+
+### Using  the Script
+
+```bash
+python scripts/cnocr_predict.py --file examples/multi-line_cn1.png
+```
+
+
+
+### (No NECESSARY) Train
+
+You can use the package without any train. But if you really really want to train your own models, follow this:
+
+```bash
+python scripts/cnocr_train.py --cpu 2 --num_proc 4 --loss ctc --dataset cn_ocr
+```
+
+
+
+Fine-tuning the model with specific data from existing models is also supported. Please refer to the following command:
+
+```bash
+python scripts/cnocr_train.py --cpu 2 --num_proc 4 --loss ctc --dataset cn_ocr --load_epoch 20
+```
+
+
+
+More references can be found at  [scripts/run_cnocr_train.sh](./scripts/run_cnocr_train.sh).
+
+
+
+## Future Work
+
+* [x] support multi-line-characters recognition (`Done`)
+* [x] crnn model supports prediction for variable-width image files (`Done`)
+* [x] Add Unit Tests  (`Doing`)
+* [x]  Bugfixes  (`Doing`)
+* [ ] Support space recognition (Tried, but not successful for now )
+* [ ] Try other models such as DenseNet, ResNet