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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