Smart Mark Entry System Using Image Processing

Automated mark entry systems in education, utilizing technologies such as Optical Mark Recognition, barcode scanning, and Convolutional Neural Network models trained on handwritten digits, have transformed grading processes. Integration of machine learning ensures accuracy and adaptability to various handwriting styles. Digital image processing, contour detection, and edge reduction techniques contribute to precise digit recognition, allowing efficient data transfer to Excel for streamlined management and analysis. This approach not only saves time but also enhances the efficiency and accuracy of student performance assessments.