whl.md

# paddleocr package

## Get started quickly
### install package
install by pypi
```bash
pip install paddleocr
```

build own whl package and install
```bash
python setup.py bdist_wheel
pip install dist/paddleocr-0.0.1-py3-none-any.whl
```
### 1. Use by code

* detection and recognition
```python
from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path)
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='/path/to/PaddleOCR/doc/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]]
[[[395.0, 443.0], [1211.0, 443.0], [1211.0, 489.0], [395.0, 489.0]], ['production of this book;their contributions', 0.9713175]]
[[[395.0, 497.0], [1209.0, 495.0], [1209.0, 531.0], [395.0, 533.0]], ['have been indispensable to its creation.We', 0.96009934]]
[[[393.0, 545.0], [1212.0, 545.0], [1212.0, 591.0], [393.0, 591.0]], ['would also like to express our gratitude to al', 0.9371007]]
[[[393.0, 595.0], [1212.0, 593.0], [1212.0, 635.0], [393.0, 637.0]], ['the producers for their invaluable opinions', 0.96872145]]
[[[393.0, 645.0], [1209.0, 645.0], [1209.0, 685.0], [393.0, 685.0]], ['and assistance throughout this proiect.Andto', 0.94448787]]
[[[392.0, 697.0], [1212.0, 693.0], [1212.0, 735.0], [392.0, 739.0]], ['the many others whose names are not credited', 0.93633145]]
[[[397.0, 753.0], [689.0, 755.0], [689.0, 786.0], [397.0, 784.0]], ['buthavemades', 0.99324507]]
[[[813.0, 749.0], [1212.0, 747.0], [1212.0, 784.0], [813.0, 786.0]], ['inputin this book, we', 0.9166398]]
[[[675.0, 760.0], [799.0, 755.0], [799.0, 778.0], [675.0, 784.0]], ['speciti', 0.9063535]]
[[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]], ['thankyouforyoul', 0.92475533]]
[[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]], ['P', 0.14887337]]
[[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]], ['nuoussupport', 0.9898951]]
```

Visualization of results

<div align="center">
    <img src="../imgs_results/whl/12_det_rec.jpg" width="800">
</div>

* only detection
```python
from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path,rec=False)
for line in result:
    print(line)

# draw result
from PIL import Image

image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```

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]]
[[675.0, 760.0], [799.0, 755.0], [799.0, 778.0], [675.0, 784.0]]
[[397.0, 753.0], [689.0, 755.0], [689.0, 786.0], [397.0, 784.0]]
[[813.0, 749.0], [1212.0, 747.0], [1212.0, 784.0], [813.0, 786.0]]
[[392.0, 697.0], [1212.0, 693.0], [1212.0, 735.0], [392.0, 739.0]]
[[393.0, 645.0], [1209.0, 645.0], [1209.0, 685.0], [393.0, 685.0]]
[[393.0, 595.0], [1212.0, 593.0], [1212.0, 635.0], [393.0, 637.0]]
[[393.0, 545.0], [1212.0, 545.0], [1212.0, 591.0], [393.0, 591.0]]
[[395.0, 497.0], [1209.0, 495.0], [1209.0, 531.0], [395.0, 533.0]]
[[395.0, 443.0], [1211.0, 443.0], [1211.0, 489.0], [395.0, 489.0]]
[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]]
[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]]
[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]]
```

Visualization of results

<div align="center">
    <img src="../imgs_results/whl/12_det.jpg" width="800">
</div>

* only recognition
```python
from paddleocr import PaddleOCR
ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
result = ocr.ocr(img_path,det=False)
for line in result:
    print(line)
```

Output will be a list, each item contains text and recognition confidence
```bash
['PAIN', 0.990372]
```

### Use by command line

show help information
```bash
paddleocr -h
```

* detection and recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.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]]
[[[395.0, 443.0], [1211.0, 443.0], [1211.0, 489.0], [395.0, 489.0]], ['production of this book;their contributions', 0.9713175]]
[[[395.0, 497.0], [1209.0, 495.0], [1209.0, 531.0], [395.0, 533.0]], ['have been indispensable to its creation.We', 0.96009934]]
[[[393.0, 545.0], [1212.0, 545.0], [1212.0, 591.0], [393.0, 591.0]], ['would also like to express our gratitude to al', 0.9371007]]
[[[393.0, 595.0], [1212.0, 593.0], [1212.0, 635.0], [393.0, 637.0]], ['the producers for their invaluable opinions', 0.96872145]]
[[[393.0, 645.0], [1209.0, 645.0], [1209.0, 685.0], [393.0, 685.0]], ['and assistance throughout this proiect.Andto', 0.94448787]]
[[[392.0, 697.0], [1212.0, 693.0], [1212.0, 735.0], [392.0, 739.0]], ['the many others whose names are not credited', 0.93633145]]
[[[397.0, 753.0], [689.0, 755.0], [689.0, 786.0], [397.0, 784.0]], ['buthavemades', 0.99324507]]
[[[813.0, 749.0], [1212.0, 747.0], [1212.0, 784.0], [813.0, 786.0]], ['inputin this book, we', 0.9166398]]
[[[675.0, 760.0], [799.0, 755.0], [799.0, 778.0], [675.0, 784.0]], ['speciti', 0.9063535]]
[[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]], ['thankyouforyoul', 0.92475533]]
[[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]], ['P', 0.14887337]]
[[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]], ['nuoussupport', 0.9898951]]
```

* only detection
```bash
paddleocr --image_dir PaddleOCR/doc/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]]
[[675.0, 760.0], [799.0, 755.0], [799.0, 778.0], [675.0, 784.0]]
[[397.0, 753.0], [689.0, 755.0], [689.0, 786.0], [397.0, 784.0]]
[[813.0, 749.0], [1212.0, 747.0], [1212.0, 784.0], [813.0, 786.0]]
[[392.0, 697.0], [1212.0, 693.0], [1212.0, 735.0], [392.0, 739.0]]
[[393.0, 645.0], [1209.0, 645.0], [1209.0, 685.0], [393.0, 685.0]]
[[393.0, 595.0], [1212.0, 593.0], [1212.0, 635.0], [393.0, 637.0]]
[[393.0, 545.0], [1212.0, 545.0], [1212.0, 591.0], [393.0, 591.0]]
[[395.0, 497.0], [1209.0, 495.0], [1209.0, 531.0], [395.0, 533.0]]
[[395.0, 443.0], [1211.0, 443.0], [1211.0, 489.0], [395.0, 489.0]]
[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]]
[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]]
[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]]
```

* only recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false
```

Output will be a list, each item contains text and recognition confidence
```bash
['PAIN', 0.990372]
```

## Parameter Description

| Parameter                    | Description                                                                                                                                                                                                                 | Default value                  |
|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
| use_gpu                 | use GPU or not                                                                                                                                                                                                          | TRUE                    |
| gpu_mem                 | GPU memory size used for initialization                                                                                                                                                                                              | 8000M                   |
| image_dir               | The images path or folder path for predicting when used by the command line                                                                                                                                                                           |                         |
| det_algorithm           | Type of detection algorithm selected                                                                                                                                                                                                   | DB                      |
| det_model_name          | There are two ways to use: 1. The name of the detection algorithm which must be in the support list(only ch_det_mv3_db is built in currently), and the supported list will be displayed when the wrong parameter is passed in. 2. The path of the inference model that has been converted by yourself. At this time, the model path must contains model and params files. When choosing this method, you need to give the name of det_algorithm | ch_det_mv3_db           |
| det_max_side_len        | The maximum size of the long side of the image. When the long side exceeds this value, the long side will be resized to this size, and the short side will be scaled proportionally                                                                                                                         | 960                     |
| det_db_thresh           | Binarization threshold value of DB output map                                                                                                                                                                                        | 0.3                     |
| det_db_box_thresh       | The threshold value of the DB output box. Boxes score lower than this value will be discarded                                                                                                                                                                         | 0.5                     |
| det_db_unclip_ratio     | The expanded ratio of DB output box                                                                                                                                                                                             | 2                       |
| det_east_score_thresh   | Binarization threshold value of EAST output map                                                                                                                                                                                       | 0.8                     |
| det_east_cover_thresh   | The threshold value of the EAST output box. Boxes score lower than this value will be discarded                                                                                                                                                                         | 0.1                     |
| det_east_nms_thresh     | The NMS threshold value of EAST model output box                                                                                                                                                                                              | 0.2                     |
| rec_algorithm           | Type of recognition algorithm selected                                                                                                                                                                                                | CRNN                    |
| rec_model_name          | There are two ways to use: 1. The name of the recognition algorithm which must be in the support list(only supports CRNN, Rosetta, STAR, RARE and other algorithms currently, but only ch_rec_mv3_crnn_enhance is built-in), and the supported list will be displayed when the wrong parameter is passed in. 2. The path of the inference model that has been converted by yourself. At this time, the model path must contains model and params files. When choosing this method, you need to give the name of rec_algorithm | ch_rec_mv3_crnn_enhance |
| rec_image_shape         | image shape of recognition algorithm                                                                                                                                                                                            | "3,32,320"              |
| rec_char_type           | Character type of recognition algorithm, Chinese (ch) or English (en)                                                                                                                                                                               | ch                      |
| rec_batch_num           | When performing recognition, the batchsize of forward images                                                                                                                                                                                         | 30                      |
| rec_char_dict_path      | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2                                                                                                                                              |                         |
| use_space_char          | Whether to recognize spaces                                                                                                                                                                                                         | TRUE                    |
| enable_mkldnn           | Whether to enable mkldnn                                                                                                                                                                                                       | FALSE                   |
| model_storage_directory | Download model save path when det_model_name or rec_model_name use mode 1                                                                                                                                                                                                     | ～/.paddleocr                |
| det                     | Enable detction when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |
| rec                     | Enable detction when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |