Optical Mark Recognition (OMR) is an automated process of capturing the information, usually in the form of bubbles or squares and analysing the same using complex computations in order to generate specific required knowledge. Experimental results depict the efficiency of the proposed method with the error rate less than 2% by processing 150 sheets per minute. The algorithm is evaluated on various OMR templates obtained from different scanners and smartphones. Marked regions are accurately detected by computing local threshold instead of global classification margin. Accuracy is improved by implementing some of the heuristics approaches which identifies marking regions that are missed during preprocessing. The main contributions of this paper are dynamic localization of optical mark region that makes the algorithm generic and independent of templates. To address these problems, robust and computationally efficient parameter-less OMR method is proposed.

The need for parameter less and computationally efficient method that works in real time adds on to the existing challenges. Developing robust and low-cost solutions for optical mark recognition (OMR) is still a challenging problem. Most of the existing methods are template specific and requires parameter tuning based on the given template. Innovation anddevelopment of computer vision algorithms finds real-time application inautomated evaluation of optical mark sheets. We also call the community’s attention to the lack of a standard dataset that could be used to compare OMR solutions. We map and categorize the restrictions to help the reader improve the current software OMR technology state. The present work reviews 35 papers around OMR subject and lists the reviewed methods’ main characteristics, datasets, restrictions, technological challenges, techniques used, processing time, and accuracy. The literature proposes several methods, often highlighting the issue of cost and accessibility. However, most solutions lack flexibility, mainly for the end-users. OMR initially appeared as a dedicated hardware solution, but software solutions have emerged with the evolution of technology, gradually replacing dedicated equipment. Such task can be facilitated and accelerated by Optical Mark Recognition (OMR) technology, bringing educational institutions to look for this solution. Performing mass assessment corrections is a tedious and costly task, especially when allocating teachers or instructors to do these corrections.

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