Design and practice of intelligent answer card recognition system based on OpenCV

Introduction

This article introduces the design and implementation of an answer card recognition system based on OpenCV and PyQt5. The system uses OpenCV to recognize the answers in the answer sheet pictures, calculate and output the scores. By applying OpenCV, we can easily obtain relevant data from digital images, and then perform various detections on the images, including preprocessing detection, Hough line detection, image smoothing, morphological filtering, area segmentation and labeling, and a series of operations.

Technical points

OpenCV

OpenCV is an open source computer vision and machine learning library that provides a rich set of image processing tools and algorithms. In this project, we use the powerful functions of OpenCV to process the answer sheet images and extract key information from them.

PyQt5

PyQt5 is a set of tools for Python bindings to the Qt library for creating powerful graphical user interfaces. We use PyQt5 to build a user interface that allows users to easily upload answer sheet images, view recognition results, and output scores.

Function realization

1. Answer identification

   Use OpenCV to preprocess the answer sheet image, including grayscale, binarization and other operations, and then find the edges and boxes of the answer sheet through Hough line detection and other technologies. Then, we can extract the answer area based on the position of the box, and use methods such as morphological filtering to reduce noise, and finally obtain a clear answer image.

2. Score calculation

   By processing the answer image, we can compare each answer area with the preset standard answers to obtain the score of each question. Add up the scores for all questions to get the final total exam score.

3. Result output

   The identified answers and scores are displayed to the user through the PyQt5 interface, allowing them to intuitively understand the test results.

Advantages and Applications

1. Efficient and accurate

   OpenCV provides a wealth of image processing tools to make the recognition of answer sheets more efficient and accurate.

2. friendly user

   The user interface built using PyQt5 is simple and intuitive. Users can easily upload answer sheets and view recognition results, which improves the usability of the system.

3. widely used

   This system can not only be used to identify school examination answer sheets, but can also be used in various fields such as questionnaires, and has broad application prospects.

Conclusion

Through this article, we have an in-depth understanding of the design and implementation of the answer sheet recognition system based on OpenCV and PyQt5. This system not only has efficient and accurate recognition capabilities, but also makes it easy for users to operate through a friendly user interface. I hope this article can provide some inspiration and help to developers who are interested in developing similar systems.