In recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise contamination. This study introduces a comprehensive deep learning methodology that is tailored for precise identification and extraction of rows and columns from document images containing tables. The proposed model employs table detection and structure recognition to delineate table and column areas, followed by semantic rule- based approaches for row extraction within tabular sub-regions. The evaluation was performed on the publicly available Marmot data Table datasets demonstrates state-of-the-art performance. Additionally, transfer learning using VGG 19 is employed for fine-tuning the model, enhancing its capability further. Furthermore, this project fills a void in the Marmot dataset by providing it with extra annotations for table structure, expanding its scope to encompass column detection in addition to table identification.