The explosion of digital documents—PDFs, scans, and web pages—has created a massive reservoir of untapped data, especially in finance. Long-form reports, such as financial statements and contracts, contain valuable insights hidden within their text and tables. However, extracting these insights in a contextual form to address critical enterprise tasks, including compliance, risk analysis, and decision-making, is a significant challenge. One particular challenge within this highly unstructured content is the detection and extraction of multi-dimensional tabular data.
Despite advancements in large language models (LLMs) and vision-language models (VLMs), extracting tabular data with high accuracy remains a complex and largely unresolved challenge. Compounding this issue, data privacy concerns prevent many financial institutions from transmitting sensitive documents to cloud-based APIs.
The result is a pressing need for secure, high-performance solutions that can be deployed on-premise or in private cloud environments without sacrificing flexibility or accuracy.
This white paper presents nRoad’s innovative approach to table detection and extraction, engineered to address the variability of real-world documents, ensure auditability, and operate securely within client infrastructure. Through a detailed comparative analysis, we demonstrate how nRoad’s platform consistently surpasses existing solutions in accuracy, robustness, and deployability, setting a new standard for document data extraction.
Authors:
Hrishekesh Rajpatak, Co-Founder and CTO, nRoad, a Linedata Company
Prabhod Sunkara, COO, nRoad, a Linedata Company