AI Table Extraction Comparison: Claude vs Docling vs ScaleDP

A practical comparison of Claude, Docling, and ScaleDP for AI table extraction, with accuracy gaps that matter for document processing at scale.

I ran identical documents through Docling, ScaleDP, and Claude-based sub-agents. Title-detection accuracy ranged from 11% to 100%, with consequences for whether extraction saved work or created more cleanup.

The Test: Real Proposals, Real Complexity

I used consulting proposals with project cost summaries, budget breakdowns with multi-level headers, milestone schedules, and staffing matrices. The tables included merged cells, currency formatting, task hierarchies, and formatting quirks from working documents.

The BHP proposal contained a seemingly simple five-row cost summary and a complex 17-by-24 budget breakdown spanning multiple staff categories and task hierarchies. The LKAB proposal included milestone tables with TBD placeholders and cost estimates with subtotals. Both documents included the standard noise of professional proposals: letterheads, contact information, resume sections, and boilerplate formatting.

The Critical Metric: Table Title Detection

For any downstream use of extracted tables, the table title is essential context. A table labeled “Project Costs” serves a different analytical purpose than one labeled “Historical Spending.” Without accurate title detection, extracted data requires expensive manual review before it becomes useful.

Docling detected titles correctly on only 11% to 14% of tables across the test documents. ScaleDP improved to roughly 20%, but with a significant caveat: many of its “correct” titles were actually wrong. In the BHP proposal, ScaleDP labeled Table 1 with the content “+1 604 681 4196 office.” That’s the phone number from the letterhead, not the actual table title “Summary of Project Costs by Task.”

The Claude-based approach achieved 100% title detection. It correctly identified “Table 1: Summary of Project Costs by Task” and “Detailed Budget Breakdown by Task and Staff” with complete accuracy.

The 44% False Positive Problem

Beyond title accuracy, both open-source tools generated substantial noise. Docling detected 27 tables in the BHP proposal, of which 12 were completely empty (0x0 dimensions). That’s a 44% false positive rate. Nearly half of the “tables” found were phantoms that would pollute any downstream processing.

ScaleDP showed a different failure mode. While it detected fewer empty tables, it consistently misidentified document elements as tables. Letterheads became tables. Resume section headers became 1x2 tables. The header block containing contact information was dutifully extracted as tabular data, complete with its phone number serving as a table title.

At a 44% false positive rate, nearly half the extracted tables require storage, processing, and filtering without providing useful data. Those costs accumulate across thousands or millions of documents.

Structural Preservation Under Pressure

Simple tables with clean rows and columns extract reasonably well across all three approaches. The differentiation appears with complex tables.

The 17-by-24 budget breakdown in the BHP proposal tested each tool’s ability to preserve structure across merged headers, nested task hierarchies, and multiple currency columns. Docling’s output had “structure compromised due to complexity.” ScaleDP extracted the table but added phantom empty columns. That’s a parsing artifact that would break any automated analysis expecting consistent column counts.

The Claude-based extraction preserved the complete structure: the relationships between headers and data cells, the task hierarchy, and the staff-rate associations. It also captured contextual metadata. Costs were in Canadian dollars. The table detailed a specific section of the proposal. It related to other tables in the document.

Tool Selection by Processing Volume

The practical implications depend on your scale and tolerance for post-processing.

For small-scale extraction, tens of documents where manual review is feasible, any tool will work. Budget time for cleanup. ScaleDP’s speed makes it attractive for quick extraction tasks where you’ll verify results anyway.

For medium-scale processing, hundreds to thousands of documents, the false positive rate becomes the dominant concern. At 44% false positives, you’re effectively doubling your data volume with noise. The time saved on extraction gets consumed by filtering and validation.

For large-scale processing, the document corpuses that motivate tool selection in the first place, accuracy compounds. Extrapolating from these results across a 200,000-document corpus, the Claude-based approach would yield approximately 200,000 correctly titled tables. Docling would produce roughly 25,000 correct titles alongside substantial noise. ScaleDP would generate 40,000 apparent titles, many of which would be wrong.

The Hybrid Reality

No single tool solves every extraction problem perfectly. The evaluation suggests a practical hierarchy for tool selection.

For title detection and contextual understanding, AI-native approaches have a substantial advantage. They understand that a phone number is not a table title, that a resume header is not tabular data, and that “Table 1: Summary of Project Costs” has a semantic structure worth preserving.

For raw extraction speed at massive scale with tolerance for post-processing, ScaleDP offers throughput. Budget for significant cleanup. Column alignment issues and false positives will require additional processing steps.

For structural accuracy on simpler tables, both open-source tools perform adequately. The false positive rate remains a concern.

Document understanding requires semantic comprehension. Tools that match PDF structure alone will continue to mistake letterheads for tables, phone numbers for titles, and resume headers for data structures. Tools that interpret the content will continue outperforming them on measures relevant to downstream use.

The 44% false positive rate reflects the approach’s reliance on visual or structural resemblance to tables without establishing what the content represents. A release-level bug fix will not resolve that difference. For production systems, it determines whether extraction saves work or adds cleanup.