Agentic Table Parsing: A Composable Approach to Complex Documents

Complex table extraction in document AI has highlighted the challenges of requiring spatial understanding, semantic interpretation, and structural precision, prompting a shift from monolithic to composable, multi-model architectures. While traditional single-model approaches aimed to handle these complexities end-to-end, the composable approach involves specialized models focusing on their strengths and combining outputs for superior performance. This method has proven its efficacy with notable improvements in accuracy and structure preservation, as evidenced by the results from the SCORE-Bench evaluation. The agentic table parsing system, which employs this composable architecture, shows significant advancements in extracting complex tables by orchestrating specialized models to address distinct aspects of the task. This architecture not only enhances immediate performance but also offers extensibility as models evolve, making it adaptable to future capabilities in document understanding.