Financial industries rely on a variety of data to understand an organization's financial health and performance, and analysis of financial statements is used for decision-making and understanding business activities. Financial statements often have complex, unstructured formats, making it difficult to extract useful information for decision-making. Organizing and refining these data is crucial for effective analysis. Previous research in the field of table detection has primarily centred around object detection methods, with limited exploration of methods for cell-wise information extraction by identifying row and column entities. In order to address this research gap, this paper proposes a novel dataset called TERED (Tabular Entity Relationship Establishment Dataset) to train a model to identify relationships among elements in large financial tables, such as statements and balance sheets, using computer vision techniques which have yet to be fully explored in financial analysis. The dataset contains more than 10,000 tabular data in scanned image and pdf formats and is divided into 12 classes. We trained CLF-RCNN, a Contrastive Learning based Faster RCNN model which in turn is a state-of-the-art object detection model on this dataset and achieved an F1 score of 93 % for table detection and 74% for identifying tabular entities and relationships. Additionally, we introduced a new loss term influenced by contrastive learning that improves prediction performances with our developed algorithm to return the sequential order of the unordered predictions.