200 lines
12 KiB
Markdown
200 lines
12 KiB
Markdown
# paddleocr package
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## Get started quickly
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### install package
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install by pypi
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```bash
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pip install paddleocr
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```
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build own whl package and install
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```bash
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python setup.py bdist_wheel
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pip install dist/paddleocr-0.0.3-py3-none-any.whl
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```
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### 1. Use by code
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* detection and recognition
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```python
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from paddleocr import PaddleOCR,draw_ocr
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
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result = ocr.ocr(img_path)
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for line in result:
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print(line)
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# draw result
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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Output will be a list, each item contains bounding box, text and recognition confidence
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```bash
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[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
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[[[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]]
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[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
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......
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```
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Visualization of results
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<div align="center">
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<img src="../imgs_results/whl/12_det_rec.jpg" width="800">
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</div>
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* only detection
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```python
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from paddleocr import PaddleOCR,draw_ocr
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ocr = PaddleOCR() # need to run only once to download and load model into memory
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img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
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result = ocr.ocr(img_path,rec=False)
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for line in result:
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print(line)
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# draw result
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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Output will be a list, each item only contains bounding box
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```bash
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[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
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[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
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[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
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......
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```
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Visualization of results
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<div align="center">
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<img src="../imgs_results/whl/12_det.jpg" width="800">
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</div>
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* only recognition
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR() # need to run only once to load model into memory
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img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
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result = ocr.ocr(img_path,det=False)
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for line in result:
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print(line)
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```
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Output will be a list, each item contains text and recognition confidence
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```bash
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['PAIN', 0.990372]
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```
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### Use by command line
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show help information
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```bash
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paddleocr -h
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```
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* detection and recognition
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg
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```
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Output will be a list, each item contains bounding box, text and recognition confidence
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```bash
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[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
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[[[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]]
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[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
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......
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```
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* only detection
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --rec false
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```
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Output will be a list, each item only contains bounding box
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```bash
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[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
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[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
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[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
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......
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```
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* only recognition
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false
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```
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Output will be a list, each item contains text and recognition confidence
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```bash
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['PAIN', 0.990372]
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```
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## Use custom model
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When the built-in model cannot meet the needs, you need to use your own trained model.
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First, refer to the first section of [inference_en.md](./inference_en.md) to convert your det and rec model to inference model, and then use it as follows
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### 1. Use by code
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```python
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from paddleocr import PaddleOCR,draw_ocr
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# The path of detection and recognition model must contain model and params files
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ocr = PaddleOCR(det_model_dir='{your_det_model_dir}',rec_model_dir='{your_rec_model_dir}å')
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img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
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result = ocr.ocr(img_path)
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for line in result:
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print(line)
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# draw result
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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### Use by command line
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir}
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```
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## Parameter Description
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| Parameter | Description | Default value |
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|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
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| use_gpu | use GPU or not | TRUE |
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| gpu_mem | GPU memory size used for initialization | 8000M |
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| image_dir | The images path or folder path for predicting when used by the command line | |
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| det_algorithm | Type of detection algorithm selected | DB |
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| det_model_dir | the text detection inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/det`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
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| 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 |
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| det_db_thresh | Binarization threshold value of DB output map | 0.3 |
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| det_db_box_thresh | The threshold value of the DB output box. Boxes score lower than this value will be discarded | 0.5 |
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| det_db_unclip_ratio | The expanded ratio of DB output box | 2 |
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| det_east_score_thresh | Binarization threshold value of EAST output map | 0.8 |
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| det_east_cover_thresh | The threshold value of the EAST output box. Boxes score lower than this value will be discarded | 0.1 |
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| det_east_nms_thresh | The NMS threshold value of EAST model output box | 0.2 |
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| rec_algorithm | Type of recognition algorithm selected | CRNN |
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| rec_model_dir | the text recognition inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/rec`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
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| rec_image_shape | image shape of recognition algorithm | "3,32,320" |
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| rec_char_type | Character type of recognition algorithm, Chinese (ch) or English (en) | ch |
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| rec_batch_num | When performing recognition, the batchsize of forward images | 30 |
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| max_text_length | The maximum text length that the recognition algorithm can recognize | 25 |
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| rec_char_dict_path | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2 | ./ppocr/utils/ppocr_keys_v1.txt |
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| use_space_char | Whether to recognize spaces | TRUE |
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| enable_mkldnn | Whether to enable mkldnn | FALSE |
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| det | Enable detction when `ppocr.ocr` func exec | TRUE |
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| rec | Enable detction when `ppocr.ocr` func exec | TRUE |
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