PaddleOCR Quick Start

Note: This tutorial mainly introduces the usage of PP-OCR series models, please refer to PP-Structure Quick Start for the quick use of document analysis related functions.

• 1. Installation
  • 1.1 Install PaddlePaddle
  • 1.2 Install PaddleOCR Whl Package
• 2. Easy-to-Use
  • 2.1 Use by Command Line
    • 2.1.1 Chinese and English Model
    • 2.1.2 Multi-language Model
  • 2.2 Use by Code
    • 2.2.1 Chinese & English Model and Multilingual Model
• 3. Summary

1. Installation

1.1 Install PaddlePaddle

If you do not have a Python environment, please refer to Environment Preparation.

• If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install

  python -m pip install paddlepaddle-gpu -i https://pypi.tuna.tsinghua.edu.cn/simple

• If you have no available GPU on your machine, please run the following command to install the CPU version

  python -m pip install paddlepaddle -i https://pypi.tuna.tsinghua.edu.cn/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

2. Easy-to-Use

2.1 Use by Command Line

PaddleOCR provides a series of test images, click here 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 parameter--use_gpu false to disable the gpu device

  paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en --use_gpu false

  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

• 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

  [[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: 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', 0.9934559464454651]

Version paddleocr uses the PP-OCRv3 model by default(--ocr_version PP-OCRv3). If you want to use other versions, you can set the parameter --ocr_version, the specific version description is as follows:

If you want to add your own trained model, you can add model links and keys in paddleocr and recompile.

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

[[[67.0, 51.0], [327.0, 46.0], [327.0, 74.0], [68.0, 80.0]], ('PHOCAPITAL', 0.9944712519645691)]
[[[72.0, 92.0], [453.0, 84.0], [454.0, 114.0], [73.0, 122.0]], ('107 State Street', 0.9744491577148438)]
[[[69.0, 135.0], [501.0, 125.0], [501.0, 156.0], [70.0, 165.0]], ('Montpelier Vermont', 0.9357033967971802)]
......

Commonly used multilingual abbreviations include

A list of all languages and their corresponding abbreviations can be found in Multi-Language Model Tutorial

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 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='./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

[[[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)]
  ......

Visualization of results

If the input is a PDF file, you can refer to the following code for visualization

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))

3. Summary

In this section, you have mastered the use of PaddleOCR whl package.

PaddleOCR is a rich and practical OCR tool library that get through the whole process of data production, model training, compression, inference and deployment, please refer to the tutorials to start the journey of PaddleOCR.