quickstart_en.md

# PaddleOCR Quick Start

[PaddleOCR Quick Start](#paddleocr-quick-start)

+ [1. Install PaddleOCR Whl Package](#1-install-paddleocr-whl-package)
* [2. Easy-to-Use](#2-easy-to-use)
  + [2.1 Use by command line](#21-use-by-command-line)
    - [2.1.1 English and Chinese Model](#211-english-and-chinese-model)
    - [2.1.2 Multi-language Model](#212-multi-language-model)
    - [2.1.3 LayoutParser](#213-layoutparser)
  + [2.2 Use by Code](#22-use-by-code)
    - [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese---english-model-and-multilingual-model)
    - [2.2.2 LayoutParser](#222-layoutparser)



<a name="1-install-paddleocr-whl-package"></a>

## 1. Install PaddleOCR Whl Package

```bash
pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
```

- **For windows users:** If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows. Please try to download Shapely whl file [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).

  Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)

- **For layout analysis users**, run the following command to install **Layout-Parser**

  ```bash
  pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
  ```

<a name="2-easy-to-use"></a>

## 2. Easy-to-Use

<a name="21-use-by-command-line"></a>

### 2.1 Use by command line

PaddleOCR provides a series of test images, click [here](https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip) to download, and then switch to the corresponding directory in the terminal

```bash
cd /path/to/ppocr_img
```

If you do not use the provided test image, you can replace the following `--image_dir` parameter with the corresponding test image path

<a name="211-english-and-chinese-model"></a>

#### 2.1.1 Chinese and English Model

* Detection, direction classification and recognition: set the direction classifier parameter`--use_angle_cls true` to recognize vertical text.

  ```bash
  paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en
  ```

  Output will be a list, each item contains bounding box, text and recognition confidence

  ```bash
  [[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
  [[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
  [[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
  ......
  ```

* Only detection: set `--rec` to `false`

  ```bash
  paddleocr --image_dir ./imgs_en/img_12.jpg --rec false
  ```

  Output will be a list, each item only contains bounding box

  ```bash
  [[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
  [[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
  [[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
  ......
  ```

* Only recognition: set `--det` to `false`

  ```bash
  paddleocr --image_dir ./imgs_words_en/word_10.png --det false --lang en
  ```

  Output will be a list, each item contains text and recognition confidence

  ```bash
  ['PAIN', 0.990372]
  ```

More whl package usage can be found in [whl package](./whl_en.md)
<a name="212-multi-language-model"></a>

#### 2.1.2 Multi-language Model

Paddleocr currently supports 80 languages, which can be switched by modifying the `--lang` parameter.

``` bash
paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
```

<div align="center">
    <img src="../imgs_en/254.jpg" width="300" height="600">
    <img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
</div>
The result is a list, each item contains a text box, text and recognition confidence

```text
[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
......
```

Commonly used multilingual abbreviations include

| Language            | Abbreviation |      | Language | Abbreviation |      | Language | Abbreviation |
| ------------------- | ------------ | ---- | -------- | ------------ | ---- | -------- | ------------ |
| Chinese & English   | ch           |      | French   | fr           |      | Japanese | japan        |
| English             | en           |      | German   | german       |      | Korean   | korean       |
| Chinese Traditional | chinese_cht  |      | Italian  | it           |      | Russian  | ru           |

A list of all languages and their corresponding abbreviations can be found in [Multi-Language Model Tutorial](./multi_languages_en.md)
<a name="213-layoutparser"></a>

#### 2.1.3 LayoutParser

To use the layout analysis function of PaddleOCR, you need to specify `--type=structure`

```bash
paddleocr --image_dir=../doc/table/1.png --type=structure
```

- **Results Format**

  The returned results of PP-Structure is a list composed of a dict, an example is as follows

  ```shell
  [
    {   'type': 'Text',
        'bbox': [34, 432, 345, 462],
        'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
                  [('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent  ', 0.465441)])
    }
  ]
  ```

  The description of each field in dict is as follows

  | Parameter | Description                                                  |
  | --------- | ------------------------------------------------------------ |
  | type      | Type of image area                                           |
  | bbox      | The coordinates of the image area in the original image, respectively [left upper x, left upper y, right bottom x, right bottom y] |
  | res       | OCR or table recognition result of image area。<br> Table: HTML string of the table; <br> OCR: A tuple containing the detection coordinates and recognition results of each single line of text |

- **Parameter Description：**

  | Parameter       | Description                                                  | Default value                                |
  | --------------- | ------------------------------------------------------------ | -------------------------------------------- |
  | output          | The path where excel and recognition results are saved       | ./output/table                               |
  | table_max_len   | The long side of the image is resized in table structure model | 488                                          |
  | table_model_dir | inference model path of table structure model                | None                                         |
  | table_char_type | dict path of table structure model                           | ../ppocr/utils/dict/table_structure_dict.txt |

<a name="22-use-by-code"></a>

### 2.2 Use by Code
<a name="221-chinese---english-model-and-multilingual-model"></a>

#### 2.2.1 Chinese & English Model and Multilingual Model

* detection, angle classification and recognition:

```python
from paddleocr import PaddleOCR,draw_ocr
# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
# You can set the parameter `lang` as `ch`, `en`, `fr`, `german`, `korean`, `japan`
# to switch the language model in order.
ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
img_path = './imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
    print(line)


# draw result
from PIL import Image
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='./fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```

Output will be a list, each item contains bounding box, text and recognition confidence

```bash
[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
......
```

Visualization of results

<div align="center">
    <img src="../imgs_results/whl/12_det_rec.jpg" width="800">
</div>
<a name="222-layoutparser"></a>

#### 2.2.2 LayoutParser

```python
import os
import cv2
from paddleocr import PPStructure,draw_structure_result,save_structure_res

table_engine = PPStructure(show_log=True)

save_folder = './output/table'
img_path = './table/1.png'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])

for line in result:
    line.pop('img')
    print(line)

from PIL import Image

font_path = './fonts/simfang.ttf'
image = Image.open(img_path).convert('RGB')
im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```