OpenCV implementation and design of automatic bank card number identification system

Introduction

This article introduces the design and implementation of a bank card number identification system based on OpenCV and PyQt5. The system can import bank card images, undergo binarization, grayscale, corrosion, expansion, edge detection and other processes to finally extract the number information on the bank card. By matching with the template numbers, the value with the highest matching degree is taken as the result and displayed on the terminal. This program has the advantage of quickly and accurately identifying bank card number information.

Technical points

OpenCV

OpenCV is a powerful computer vision library that provides a wealth of image processing and computer vision algorithms. In this project, we use OpenCV to perform various processing on bank card images to improve the accuracy of card number recognition.

PyQt5

PyQt5 is a set of tools for Python bindings to the Qt library for creating graphical user interfaces. We use PyQt5 to build the user interface so that users can easily import bank card images and view the recognition results.

Function realization

1. Image Processing

   • Binarization: Convert bank card images into black and white to better extract card number information.
   • Grayscale: Reduce the complexity of the image and help extract digital information.
   • Erosion and expansion: Optimize the edge information of numbers and improve the clarity of numbers through erosion and expansion operations.
   • Edge detection: Use edge detection algorithms, such as Canny algorithm, to find the edge contour of the card number.

2. Card number extraction and matching

   • Extract card number information: After image processing, extract the digital information on the bank card.
   • Template matching: Match the extracted numbers with the pre-prepared template numbers and calculate the matching degree.
   • Select the best match: Select the number with the highest matching degree as the final card number result.

3. The results show

   • Terminal display: The final recognition result is displayed through the terminal, providing clear feedback to the user.

Advantages and Applications

1. Fast and accurate

   Through OpenCV's image processing technology and template matching algorithm, the system can quickly and accurately identify bank card number information.

2. friendly user

   The user interface built using PyQt5 allows users to easily import bank card images and view recognition results, improving the ease of use of the system.

3. widely used

   This system can not only be applied to the identification of bank card numbers, but can also be extended to other areas that require digital identification, such as bills, ID cards, etc.

Conclusion

Through this article, we have an in-depth understanding of the design and implementation of a bank card number identification system based on OpenCV and PyQt5. The system has achieved good results in image processing and matching algorithms, and has the ability to quickly and accurately identify bank card numbers. I hope this article can provide some useful guidance and inspiration to developers developing similar systems.