Designed key components of an automated examination marking system

The current process of marking examination papers often relies on manual correction and scoring. This mechanical fixed-answer correction and scoring process not only consumes a lot of time and energy of the markers but is also prone to errors. To address this problem, this paper proposes a fully automated framework for scanning, correcting, and marking question papers. There are three main parts in this framework. The first part is a hardware system device that can scan the paper and turn pages automatically. The second part is a handwritten character recognition algorithm, based on the Pytorch framework, which can segment and identify the different categories of characters in the test paper image. The third part is the answer checking and marking system, which calculates the total marks of the question paper through the score summation model. The proposed automatic marking model can significantly improve the efficiency of test paper correction. The experimental results demonstrate that the proposed framework achieves high character recognition accuracy and can quickly and efficiently count the test paper marks automatically.