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Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices

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Paddleocr Package

1 Get started quickly

1.1 install package

install by pypi

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

build own whl package and install

python3 setup.py bdist_wheel
pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version of paddleocr

2 Use

2.1 Use by code

The paddleocr whl package will automatically download the ppocr lightweight model as the default model, which can be customized and replaced according to the section 3 Custom Model.

  • detection angle classification and recognition
from paddleocr import PaddleOCR,draw_ocr
# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
# You can set the parameter `lang` as `ch`, `en`, `french`, `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 = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

# draw result
from PIL import Image
result = result[0]
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/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

[[[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

  • detection and recognition
from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR(lang='en') # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=False)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

# draw result
from PIL import Image
result = result[0]
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/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

[[[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

  • classification and recognition
from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang='en') # 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, cls=True)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

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

['PAIN', 0.990372]
  • only detection
from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path,rec=False)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

# draw result
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

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

[[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]]
......

Visualization of results

  • only recognition
from paddleocr import PaddleOCR
ocr = PaddleOCR(lang='en') # 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, cls=False)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

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

['PAIN', 0.990372]
  • only classification
from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True) # 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, rec=False, cls=True)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

Output will be a list, each item contains classification result and confidence

['0', 0.99999964]

2.2 Use by command line

show help information

paddleocr -h
  • detection classification and recognition
paddleocr --image_dir PaddleOCR/doc/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

[[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
[[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
[[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
......

pdf file is also supported, you can infer the first few pages by using the page_num parameter, the default is 0, which means infer all pages

paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
  • detection and recognition
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --lang en

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

[[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
[[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
[[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
......
  • classification and recognition
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --lang en

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

['PAIN', 0.9934559464454651]
  • only detection
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --rec false

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

[[397.0, 802.0], [1092.0, 802.0], [1092.0, 841.0], [397.0, 841.0]]
[[397.0, 750.0], [1211.0, 750.0], [1211.0, 789.0], [397.0, 789.0]]
[[397.0, 702.0], [1209.0, 698.0], [1209.0, 734.0], [397.0, 738.0]]
......
  • only recognition
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --lang en

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

['PAIN', 0.9934559464454651]
  • only classification
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --rec false

Output will be a list, each item contains classification result and confidence

['0', 0.99999964]

3 Use custom model

When the built-in model cannot meet the needs, you need to use your own trained model. First, refer to the first section of inference_en.md to convert your det and rec model to inference model, and then use it as follows

3.1 Use by code

from paddleocr import PaddleOCR,draw_ocr
# The path of detection and recognition model must contain model and params files
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}', rec_char_dict_path='{your_rec_char_dict_path}', cls_model_dir='{your_cls_model_dir}', use_angle_cls=True)
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

# draw result
from PIL import Image
result = result[0]
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/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

3.2 Use by command line

paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true

4 Use web images or numpy array as input

4.1 Web image

  • Use by code
from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

# show result
from PIL import Image
result = result[0]
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/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
  • Use by command line
paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true

4.2 Numpy array

Support numpy array as input only when used by code

import cv2
from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs/11.jpg'
img = cv2.imread(img_path)
# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), If your own training model supports grayscale images, you can uncomment this line
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

# show result
from PIL import Image
result = result[0]
download_with_progressbar(img_path, 'tmp.jpg')
image = Image.open('tmp.jpg').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/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

5 PDF file

  • Use by command line

you can infer the first few pages by using the page_num parameter, the default is 0, which means infer all pages

paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
  • Use by code
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="ch" page_num=2)  # need to run only once to download and load model into memory
img_path = './xxx.pdf'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
    res = result[idx]
    for line in res:
        print(line)

# draw result
import fitz
from PIL import Image
import cv2
import numpy as np
imgs = []
with fitz.open(img_path) as pdf:
    for pg in range(0, pdf.pageCount):
        page = pdf[pg]
        mat = fitz.Matrix(2, 2)
        pm = page.getPixmap(matrix=mat, alpha=False)
        # if width or height > 2000 pixels, don't enlarge the image
        if pm.width > 2000 or pm.height > 2000:
            pm = page.getPixmap(matrix=fitz.Matrix(1, 1), alpha=False)

        img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
        imgs.append(img)
for idx in range(len(result)):
    res = result[idx]
    image = imgs[idx]
    boxes = [line[0] for line in res]
    txts = [line[1][0] for line in res]
    scores = [line[1][1] for line in res]
    im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/simfang.ttf')
    im_show = Image.fromarray(im_show)
    im_show.save('result_page_{}.jpg'.format(idx))

6 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
page_num Valid when the input type is pdf file, specify to predict the previous page_num pages, all pages are predicted by default 0
det_algorithm Type of detection algorithm selected DB
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
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_db_score_mode The parameter that control how the score of the detection frame is calculated. There are 'fast' and 'slow' options. If the text to be detected is curved, it is recommended to use 'slow' 'fast'
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_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
rec_image_shape image shape of recognition algorithm "3,32,320"
rec_batch_num When performing recognition, the batchsize of forward images 30
max_text_length The maximum text length that the recognition algorithm can recognize 25
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
use_space_char Whether to recognize spaces TRUE
drop_score Filter the output by score (from the recognition model), and those below this score will not be returned 0.5
use_angle_cls Whether to load classification model FALSE
cls_model_dir the classification inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to ~/.paddleocr/cls; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path None
cls_image_shape image shape of classification algorithm "3,48,192"
label_list label list of classification algorithm ['0','180']
cls_batch_num When performing classification, the batchsize of forward images 30
enable_mkldnn Whether to enable mkldnn FALSE
use_zero_copy_run Whether to forward by zero_copy_run FALSE
lang The support language, now only Chinese(ch)、English(en)、French(french)、German(german)、Korean(korean)、Japanese(japan) are supported ch
det Enable detction when ppocr.ocr func exec TRUE
rec Enable recognition when ppocr.ocr func exec TRUE
cls Enable classification when ppocr.ocr func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) FALSE
show_log Whether to print log FALSE
type Perform ocr or table structuring, the value is selected in ['ocr','structure'] ocr
ocr_version OCR Model version number, the current model support list is as follows: PP-OCRv3 supports Chinese and English detection, recognition, multilingual recognition, direction classifier models, PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model PP-OCRv3

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