SoFunction
Updated on 2025-05-06

Code and steps for using Python and PaddleOCR to implement graphics and text recognition

1. Introduction

In today's digital age, the application of graphic and text recognition technology is becoming more and more widely, such as document digitization and information extraction. PaddleOCR is a powerful OCR toolkit from Baidu open source. It integrates a variety of advanced algorithms and models to efficiently and accurately identify graphics and texts. This article will introduce in detail how to use PaddleOCR and Python to achieve graphic and text recognition, and give specific code and steps.

2. Environmental preparation

2.1 Install Python

Make sure your system has Python 3.7 and above installed. You canPython official websiteDownload and install. After the installation is complete, enter the following command on the command line to verify that the installation is successful:

python --version

2.2 Install PaddlePaddle

Choose the appropriate installation method based on your hardware environment (CPU or GPU) and system type. The following are the CPU version installation commands:

pip install paddlepaddle -i /pypi/simple

If you use GPU, you need to install the corresponding CUDA versionpaddlepaddle-gpu, please refer to the specific installation commandPaddlePaddle Official Installation Documentation。

2.3 Install PaddleOCR

pip install "paddleocr>=2.0.1"

3. Simple graphic and text recognition examples

3.1 Code implementation

Here is a simple Python script for text recognition of a single image:

from paddleocr import PaddleOCR
 
# Create PaddleOCR instance, using default configurationocr = PaddleOCR(use_angle_cls=True, lang="ch")
 
# The image path to identifyimg_path = ''
 
# Conduct text recognitionresult = (img_path, cls=True)
 
# Process the identification resultsfor line in result[0]:
    print(line[1][0])

3.2 Code explanation

  • ImportPaddleOCRkind:frompaddleocrImporting in modulePaddleOCRkind.
  • CreatePaddleOCRExample:usePaddleOCRClass creates an OCR instance,use_angle_cls=TrueIndicates that the direction classification function is enabled,lang="ch"Indicates the use of Chinese recognition model.
  • Specify the image path:Willimg_pathReplace with the actual path of the picture you want to identify.
  • Perform text recognition: CallocrMethods use text recognition of the specified image and return the recognition result.

Processing identification results: traversal the recognition results and print each line of text.

4. Examples of batch graphics and text recognition

4.1 Code implementation

If you need to text recognition for multiple images, you can use the following code:

from paddleocr import PaddleOCR
import os
 
# Create a PaddleOCR instanceocr = PaddleOCR(use_angle_cls=True, lang="ch")
 
# Picture folder pathimage_folder = 'path/to/your/image/folder'
 
# Get all picture files in the folderimage_files = [(image_folder, f) for f in (image_folder) if (('.png', '.jpg', '.jpeg'))]
 
# traverse image files for text recognitionfor img_path in image_files:
    print(f"Recognizing pictures: {img_path}")
    result = (img_path, cls=True)
    print("Identification results:")
    for line in result[0]:
        print(line[1][0])
    print("-" * 50)

4.2 Code explanation

  • Import the necessary libraries:Apart fromPaddleOCRClass, also importedosModules are used to process files and folders.
  • Specify the image folder path:Willimage_folderReplace with the actual path to the folder that contains the picture to be identified.
  • Get all picture files:useFunctions get all files in the folder and filter out to.png、.jpgor.jpegThe ending picture file.
  • Traversing the image files for identification: Called on each image fileocrMethods to identify text and print the recognition results.

5. Custom configuration

5.1 Code implementation

PaddleOCR provides a wealth of configuration options that you can customize as you want. For example, if you want to use English recognition model, you canlangThe parameter is set to"en"：

from paddleocr import PaddleOCR
 
# Create PaddleOCR instance and use English to identify the modelocr = PaddleOCR(use_angle_cls=True, lang="en")
 
# The image path to identifyimg_path = 'path/to/your/'
 
# Conduct text recognitionresult = (img_path, cls=True)
 
# Process the identification resultsfor line in result[0]:
    print(line[1][0])

5.2 Code explanation

CreatingPaddleOCRWhen an instance, thelangThe parameter is set to"en", means the use of English recognition model. The other steps are the same as the previous example.

6. Summary

Through this article, you have learned how to use PaddleOCR and Python to achieve graphics and text recognition. You can perform simple graphics and text recognition according to your needs, or batch recognition and custom configuration. I hope this article will be helpful to you and I wish you better results on the road of graphic and text recognition!

7. Things to note

  • Ensure the clarity and quality of the image, blurry or low-quality images may affect the recognition results.
  • If you need to process a large number of images, it is recommended to use multi-threaded or asynchronous programming to improve processing efficiency.
  • When using the GPU for identification, make sure CUDA and cuDNN are installed and configured correctly.

The above is a detailed tutorial on using PaddleOCR+Python to achieve graphic and text recognition. You can expand and optimize according to your needs.

This is the article about the code and steps for using Python and PaddleOCR to achieve graphic and text recognition. For more related Python PaddleOCR graphic and text recognition content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!