If you do not have a Python environment, please refer to Environment Preparation.
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If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install
python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
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If you have no available GPU on your machine, please run the following command to install the CPU version
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
For more software version requirements, please refer to the instructions in Installation Document for operation.
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 foundwhen you install shapely on windows. Please try to download Shapely whl file here.Reference: Solve shapely installation on windows
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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
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_imgIf you do not use the provided test image, you can replace the following --image_dir parameter with the corresponding test image path
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Detection, direction classification and recognition: set the parameter
--use_gpu falseto disable the gpu devicepaddleocr --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
[[[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]] ......
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Only detection: set
--rectofalsepaddleocr --image_dir ./imgs_en/img_12.jpg --rec falseOutput 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]] ......
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Only recognition: set
--dettofalsepaddleocr --image_dir ./imgs_words_en/word_10.png --det false --lang enOutput will be a list, each item contains text and recognition confidence
['PAIN', 0.990372]
If you need to use the 2.0 model, please specify the parameter --version PP-OCR, paddleocr uses the 2.1 model by default(--versioin PP-OCRv2). More whl package usage can be found in whl package
Paddleocr currently supports 80 languages, which can be switched by modifying the --lang parameter.
paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
[('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
Layout analysis refers to the division of 5 types of areas of the document, including text, title, list, picture and table. For the first three types of regions, directly use the OCR model to complete the text detection and recognition of the corresponding regions, and save the results in txt. For the table area, after the table structuring process, the table picture is converted into an Excel file of the same table style. The picture area will be individually cropped into an image.
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
- 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
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')In this section, you have mastered the use of PaddleOCR whl packages and obtained results.
PaddleOCR is a rich and practical OCR tool library that opens up the whole process of data, model training, compression and inference deployment, so in the next section we will first introduce you to the overview of PaddleOCR, and then clone the PaddleOCR project to start the application journey of PaddleOCR.