Automated Marks Entry Processing in Handwritten Answer Scripts using Character Recognition Techniques

The educational system went online during the outbreak, which resulted in computerized test submissions. Because of the Covid-19 outbreak, the paper assessment process in schools and universities has moved online. Hand-counting the marks on a large number of documents is a time-consuming and laborious operation. Evaluators digitally correct the script before manually counting the marks as they return to previous pages, a task that has become one of the most time-consuming. The length of time it takes to count the marks increases as the number of papers increases. This programme captures images with a scanner device, such as a smartphone, and converts them to a portable document format. Using an optical character recognition (OCR) tool, the recommendation system converts an image of a handwritten response sheet into a text document. The algorithm will look for red-colored digits on the answer sheet, extract the numbers from the response sheet, and then add the digits to get the total marks for the final content. The accuracy obtained by comparing the three different OCR in the proposed system are as follows Intelligent character recognition (ICR) accuracy – 89%, Convolutional neural network (CNN) accuracy – 86% and Intelligent word recognition (IWR) accuracy – 84%.