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PaddleOCR Quick Start

PaddleOCR Quick Start

1. Light Installation

1.1 Install PaddlePaddle2.0

# If you have cuda9 or cuda10 installed on your machine, please run the following command to install
python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple

# If you only have cpu on your machine, please run the following command to install
python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple

For more software version requirements, please refer to the instructions in Installation Document for operation.

1.2 Install PaddleOCR Whl Package

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.

    Reference: Solve shapely installation on windows

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

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

2. Easy-to-Use

2.1 Use by command line

PaddleOCR provides a series of test images, click xx to download, and then switch to the corresponding directory in the terminal

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

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.

    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

    [[[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

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

    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]]
......

  • Only recognition: set --det to false

    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

    ['PAIN', 0.990372]

More whl package usage can be found in whl package

2.1.2 Multi-language Model

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

paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
The result is a list, each item contains a text box, text and recognition confidence 
[('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

2.1.3 LayoutParser

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

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

    [
  {   '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。
    Table: HTML string of the table;
    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

2.2 Use by Code

2.2.1 Chinese & English Model and Multilingual 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`, `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

[[[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

2.2.2 LayoutParser

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')