Table Extraction Benchmark 2025: Top AI Parsers and OCR Tools Compared

The 2025 Table Extraction Benchmark evaluates the capabilities of various AI parsers and OCR tools in extracting structured data from complex documents, which remains a significant challenge in document intelligence. This benchmark highlights the importance of accurately reconstructing document structure, such as tables with merged cells or nested formats, for use in downstream AI systems. The document compares five leading tools—LlamaParse, Docling, Amazon Textract, Azure Document Intelligence, and Google Cloud Document AI—each offering different strengths, such as layout-aware extraction, scalability, privacy-sensitive processing, or industry-specific parsing. While traditional OCR struggles with preserving structure, modern solutions are moving towards layout analysis and vision-language reasoning to improve extraction accuracy and reduce post-processing burdens. The choice between self-hosted and cloud-based solutions depends on factors like governance, deployment flexibility, and operational control. Ultimately, the benchmark underscores that the most valuable metric is how well these tools preserve table structure for practical use, beyond mere OCR accuracy.