In a document, Different Graphical elements can be recognized by technolo-gies like optical character recognition (OCR) and optical mark recognition (OMR). While OCR is meant to detect and identify text regions, OMR is mainly used to detect and identify selection markings like ticks, bubbles, or checkmarks. Though OCR and OMR technologies have advanced signifi-cantly in recent years, further research is required to determine how selec-tion marks relate to text in order to improve OCR's capabilities. In this study, we present a novel method to associate the relevant text de-tected by OCR with the selection marks identified by OMR. To identify text components and selection marks from a document image, we employ an OCR pipeline (OCR with OMR capability). Next, using the structure of the graph network as a guide, we construct the selection mark to text associa-tion as an optimization problem and use Bipartite network algorithms to match the selection marks with their associated text. Our proposed method correctly connects selection marks with their corre-sponding text in a variety of document types across different domains, ac-cording to experimental results. Our analysis of ten different document types from various industry domains yielded an average F1-score of 95.95%. Our suggested method can be combined with any OCR pipeline to improve its performance without adding to its complexity. This could lead to a rise in office automation and Robotic Process Automation (RPA) for tasks involving document processing.