Exam Marks Summation App Using Tesseract OCR in Python - Saravanan, Kalaimathi; Chew, Chang Choon; Tay, Kim Gaik; Kek, Sie Long; Huong, Audrey (International Journal of Integrated Engineering, 2022)
There are no tools to auto grade subjective questions and no auto summations app to date. As a result, examiners need to mark subjective answer scripts manually and they might overlook some marks while summing up subjective answer scripts scores obtained by students. Therefore, this study aims to develop an exam mark summation app to ease miscalculation problems. Tesseract OCR was implemented in this Marks Summation App as a platform to scan the multi-page handwritten marks using pen tip above a 0.5mm size in red, blue, and black to recognise the marks wrote by the examiners. The captured marks images will be uploaded to server for image processing by promptly executing the python script in the server. The extraction of numbers from the processed image will sum up the scores to determine each candidate’s final score. It is recommendable to scan the original materials to produce more accurate accuracy. Out of 118 numbers from 21 testing samples, 88 numbers are correctly recognised, whereas 30 are not recognised correctly. Thus, the accuracy of the developed exam mark summation app is 74.58%. The app’s performance is more superior than car plate recognition result in the field. In the future, we hope to increase the appâ€™s recognition accuracy by exploring deep learning in recognizing handwritten numbers.