Disclaimer: The views and feedback shared in this article are based on internal testing and evaluations conducted by Actualize's engineering team. This study does not intend to criticize, guarantee ownership, or take any responsibility for the performance or effectiveness of the tools discussed. Our aim is to transparently share the findings from our testing process without bias, providing insights for informational purposes only.

Whitepaper: https://bit.ly/41H13LS

Github: https://bit.ly/3DhygDh

Credits: Mohanraj Palanisamy, Nooh faisal

Published date: Dec 19, 2024

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🚀 Why PDF Extraction Matters

📊 PDF extraction is integral to the workflows of AI-driven technologies such as Retrieval-Augmented Generation (RAG), intelligent agents, and Generative AI. These systems rely on clean, structured data to enable functionalities like knowledge augmentation, automated decision-making, and content synthesis. For example, AI-powered tools used in industries like finance, healthcare, and academia need accurate data extraction to enhance productivity and drive insights.

🛠️ Tools Evaluated

Our study analyzed the following tools:

• MinerU: Recognized for its robust text extraction and Markdown conversion capabilities.
• Xerox: Excels in OCR performance, particularly for scanned documents.
• Docling: A reliable choice for local deployments with balanced features.
• Llama Parse: Ideal for extracting structured data like tables.
• Marker: A versatile tool suited for offline processing.
• Markitdown: Fastest tool for PDF-to-Markdown conversion
• Unstructured: Flexible, operating in both local and API-based modes.

📋 Key Evaluation Metrics

To provide a comprehensive comparison, the tools were evaluated against several metrics:

1. Text Extraction Accuracy: Measuring how faithfully tools replicate plain text.
2. Table Extraction: Testing their ability to handle complex and nested tables.
3. OCR Performance: Focusing on converting scanned images into editable text.
4. Markdown Conversion: Assessing their capability to generate well-structured Markdown content.
5. Logical Reading Order: Ensuring extracted text retains its intended flow.
6. Resource Utilization: Analyzing performance on CPU, GPU, and MPS platforms.

🌟 Results and Highlights

1. MinerU emerged as the best all-rounder, excelling in Markdown conversion and text extraction.
2. Xerox led in OCR performance, making it ideal for scanned documents.
3. Llama Parse demonstrated unparalleled table extraction accuracy, handling complex nested tables with ease.
4. Markitdown exhibited the fastest conversion speeds, though it requires improvement in image and table extraction.
5. Docling, Marker, and Unstructured provided balanced performances, catering to specific deployment preferences.

💡 Applications and Recommendations

Organizations seeking to automate knowledge workflows or enhance AI-driven products can benefit significantly from selecting the right PDF extraction tool. For instance:

• Financial Reports: Use Llama Parse for table-heavy documents.
• Scanned Records: Opt for Xerox for its superior OCR capabilities.
• Academic Research: MinerU’s Markdown conversion ensures clean, structured outputs for downstream AI models.

🔮 Future of PDF Extraction

As AI continues to evolve, the need for accurate and efficient PDF extraction will only grow. Our study emphasizes the importance of advancing GPU support, improving table recognition, and optimizing resource usage. By addressing these areas, future tools can better align with the demands of AI-driven workflows.

📖 Explore Further

To explore the full study, including detailed results and insights, visit our GitHub Repository. For an interactive demonstration of the benchmarking process, check out our Google Colab Notebook.Additional resources on the tools evaluated can be found here:

• Docling
• Llama Parse
• Marker
• MinerU
• Unstructured
• Zerox
• Markitdown

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