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.
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
python -m pip install paddlepaddle-gpu -i https://pypi.tuna.tsinghua.edu.cn/simple
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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.
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
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
[[[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_numparameter, the default is 0, which means infer all pagespaddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
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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
[[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]] ......
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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', 0.9934559464454651]
Version
paddleocr uses the PP-OCRv4 model by default(--ocr_version PP-OCRv4). If you want to use other versions, you can set the parameter --ocr_version, the specific version description is as follows:
| version name | description |
|---|---|
| PP-OCRv4 | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
| PP-OCRv3 | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
| PP-OCRv2 | only supports Chinese and English detection and recognition, direction classifier, multilingual model is not updated |
| PP-OCR | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
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
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
| 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
- 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.
PAGE_NUM = 10 # Set the recognition page number
pdf_path = 'default.pdf'
ocr = PaddleOCR(use_angle_cls=True, lang="ch", page_num=PAGE_NUM) # need to run only once to download and load model into memory
# ocr = PaddleOCR(use_angle_cls=True, lang="ch", page_num=PAGE_NUM,use_gpu=0) # To Use GPU,uncomment this line and comment the above one.
result = ocr.ocr(pdf_path, cls=True)
for idx in range(len(result)):
res = result[idx]
if res == None: # Skip when empty result detected to avoid TypeError:NoneType
print(f"[DEBUG] Empty page {idx+1} detected, skip it.")
continue
for line in res:
print(line)
# draw the result
import fitz
from PIL import Image
import cv2
import numpy as np
imgs = []
with fitz.open(pdf_path) as pdf:
for pg in range(0, PAGE_NUM):
page = pdf[pg]
mat = fitz.Matrix(2, 2)
pm = page.get_pixmap(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.get_pixmap(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]
if res == None:
continue
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))- Detection and Recognition Using Sliding Windows
To perform OCR using sliding windows, the following code snippet can be employed:
from paddleocr import PaddleOCR
from PIL import Image, ImageDraw, ImageFont
# Initialize OCR engine
ocr = PaddleOCR(use_angle_cls=True, lang="en")
img_path = "./very_large_image.jpg"
slice = {'horizontal_stride': 300, 'vertical_stride': 500, 'merge_x_thres': 50, 'merge_y_thres': 35}
results = ocr.ocr(img_path, cls=True, slice=slice)
# Load image
image = Image.open(img_path).convert("RGB")
draw = ImageDraw.Draw(image)
font = ImageFont.truetype("./doc/fonts/simfang.ttf", size=20) # Adjust size as needed
# Process and draw results
for res in results:
for line in res:
box = [tuple(point) for point in line[0]] # Convert list of lists to list of tuples
# Convert four corners to two corners
box = [(min(point[0] for point in box), min(point[1] for point in box)),
(max(point[0] for point in box), max(point[1] for point in box))]
txt = line[1][0]
draw.rectangle(box, outline="red", width=2) # Draw rectangle
draw.text((box[0][0], box[0][1] - 25), txt, fill="blue", font=font) # Draw text above the box
# Save result
image.save("result.jpg")This example initializes the PaddleOCR instance with angle classification enabled and sets the language to English. The ocr method is then called with several parameters to customize the detection and recognition process, including the slice parameter for handling image slices.
For a more comprehensive understanding of the slicing operation, please refer to the slice operation documentation.
In this section, you have mastered the use of PaddleOCR whl package.
PaddleX provides a high-quality ecological model of the paddle. It is a one-stop full-process high-efficiency development platform for training, pressing and pushing. Its mission is to help AI technology to be implemented quickly. The vision is to make everyone an AI Developer! Currently PP-OCRv4 has been launched on PaddleX, you can enter General OCR to experience the whole process of model training, compression and inference deployment.