Evaluating Unstructured.io for PDF Parsing - Table Extraction

Overview

As part of our comprehensive RD-TableBench evaluation, we tested Unstructured.io's table extraction capabilities in their Hi-Res mode, which is marketed as their highest-quality parsing pipeline despite its increased latency. Our benchmark includes 1000 manually annotated complex table images, designed to evaluate performance across challenging real-world scenarios.

Overall Accuracy

Unstructured.io achieved a 60.2% average table precision score in our evaluation, despite running in Hi-Res mode. This performance lags substantially behind industry leaders, with Reducto achieving 90.2% accuracy - a striking 30 percentage point difference. The low accuracy, combined with high latency in Hi-Res mode and frequent OCR errors, raises serious concerns about its viability for production use.

Unstructured.io vs Alternatives

Our benchmark reveals several critical insights about Unstructured's market position:

1. Performance Rankings:

• Unstructured (60.2%) ranks near the bottom of tested solutions
• Trails dramatically behind Reducto (90.2%)
• Performs significantly worse than all major cloud providers:
• • Azure (82.7%)
  • AWS Textract (80.9%)
  • Google Document AI (64.6%)
• Performs worse than most LLMs

2. Market Position: Despite its open-source popularity, Unstructured shows severe limitations:

• Hi-Res mode introduces significant processing delays
• Frequent OCR errors even in high-quality mode
• Performance degrades notably on complex documents
• Popular open-source adoption doesn't translate to enterprise-grade accuracy

3. Technical Limitations: Our testing exposed several serious challenges:

• High latency in Hi-Res mode makes it impractical for large-scale processing
• Frequent OCR errors even with simple text
• Poor handling of merged cells and complex layouts
• Inconsistent table boundary detection
• Limited ability to process tables with multiple hierarchies

Unstructured vs Vision Language Models

When compared to vision language models like GPT-4o (76.0%), Unstructured's traditional approach shows significant disadvantages:

1. Performance Gap:

• VLMs achieve 15.8 percentage points better accuracy
• Even with Hi-Res mode's increased latency, accuracy remains poor
• OCR errors compound structural understanding issues

2. Architectural Limitations:

• Outdated approach to document processing
• High latency without corresponding accuracy benefits
• Poor adaptation to varying document qualities
• OCR engine shows surprising weakness even on clear text

Conclusion

Unstructured.io's performance in our RD-TableBench evaluation raises serious concerns about its suitability for enterprise document processing. Its 60.2% accuracy rate, achieved even in Hi-Res mode, falls dramatically short of industry standards and significantly behind modern solutions like Reducto (90.2% accuracy).

The combination of issues is particularly concerning:

• High latency in Hi-Res mode slows down processing pipelines
• Frequent OCR errors require additional downstream correction
• Poor structural understanding of tables
• Limited ability to handle complex documents

To put this performance gap in perspective: in a dataset of 1000 tables, choosing Unstructured over Reducto would result in approximately 300 additional tables requiring manual correction. When combined with the high latency of Hi-Res mode and frequent OCR errors, this creates a significant operational burden.

For organizations serious about document processing, particularly those dealing with complex tables or large document volumes, Unstructured's limitations present significant operational risks. While its open-source nature may make it appealing for basic use cases, organizations would benefit substantially from adopting more sophisticated approaches like Reducto that can deliver consistently higher accuracy with better processing speed and reliable OCR capabilities.