Bayesian-Optimization-based auto Optical Mark array recognition for flexible paper answer sheet

Abstract

Flexible paper answer sheets are widely employed in various examinations due to cost-effectiveness. However, optical marks on flexible paper often encounter challenges such as irregular shapes, non-uniform arrangement, deformation, and scanning noise, rendering automatic optical mark recognition (OMR) a formidable task. This paper introduces a multi-layer feature energy model based on the Bayesian global optimization method. The model seamlessly integrates the localization of individual marks and a division model to effectively address the problem of locating and segmenting optical marks with varying shapes and arrangements, even in the presence of deformation and diverse noise disturbances. Furthermore, the model incorporates the pixel occupancy ratio to achieve optical mark recognition. A comprehensive dataset comprising 31,940 instances of optical marks with diverse shapes and arrangements was meticulously created. This dataset achieved an impressive single-mark localization accuracy of 97.07% and an outstanding recognition accuracy of 97.80%. These results underscore the proposed method's remarkable flexibility and noise resilience in solving multiple-choice recognition problems.