MCQ checker is an automated system for evaluating and marking different types of MCQs by using machine learning, contour detection and filtering, template matching and various other IP functions. In order to process the images, various preprocessing techniques such as resizing, thresholding, grayscale conversion, dilution, and erosion were used along with contour detection, which segmented the image both vertically as well as horizontally. For the first type of MCQ, referred to as ‘Bubble Sheet’, two approaches, template matching and contour detection and filtering, were used. In template matching, two preprocessed images, correct answer and answer sheet, were fed to the system which resulted in common features of both the images which were then counted to extract the total number of correct answers. In contour detection and filtering, the marked bubbles were compared with the result set to get the correct number of answers. For the second type of MCQ, referred to as ‘Written Answers’, machine learning was used. The preprocessed answer sheet was fed to the neural network which read the characters in the image. These characters were compared to the correct answer and total number of answers were calculated. The accuracy of the neural network was 91.66% for 10 digits (0-9) and 5 letters (AE). So, by using the system, we can correctly and efficiently evaluate the answers of the MCQ answer sheets.