# Document Extraction Benchmark In today's data-driven world, we're all swimming in documents. From complex PDFs and scanned images to standard DOCX files, the challenge isn't just storing them it's getting the information *out* accurately and efficiently. This is a crucial step for any data processing pipeline. To find the best tool for the job, we conducted a comprehensive comparative analysis of various document extraction packages. We tested their ability to convert a diverse set of documents into Markdown, focusing on three key metrics: **content quality, accuracy, and the preservation of the original structure and formatting**. This report evaluates popular open-source and proprietary packages to give you a clear comparison of their strengths and weaknesses, helping you choose the best tool for your specific needs. We put nine popular packages to the test: * Docling * PDFPlumber * PyMuPDF * PDFMiner * Tesseract * EasyOCR * PaddleOCR * PythonDocx * Mammoth To see how they would compare, we created a set of 50 documents representing common real-world scenarios: 1. **Complex Scanned PDF:** A scanned exam registration document with tables and a multi-column layout. 2. **PDF with Mixed Content:** A report containing text, tables, and images. 3. **Certificate PDF:** A visually rich PDF of a certificate. 4. **Invoice Image:** A PNG screenshot of a sales invoice. 5. **Standard DOCX:** A technical manual with standard formatting. --- ### Part 1: Overall Performance First, we looked at the overall performance based on average extraction time across all 50 documents. When it comes to pure speed, the results are stark. **Table 1: Overall Performance Metrics** | Extractor | Avg. Time (s) | | --- | --- | | Docling | 8.678 | | PDFPlumber | 0.332 | | PyMuPDF | 0.480 | | PDFMiner | 0.127 | | Tesseract | 1.612 | | EasyOCR | 8.282 | | PaddleOCR | 21.515 | | PythonDocx | 0.050 | | Mammoth | 0.097 | The specialized DOCX and text-based PDF tools were incredibly fast, while the complex OCR-based tools took significantly longer. ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1761843428612/b4cf532d-888a-4fcd-b87a-89d26f7521a1.png align="center") --- ### Part 2: Detailed Analysis by Document Type Speed isn't everything. A fast extraction is useless if the output is wrong. Here’s how the packages handled our specific challenges. #### The PDF Gauntlet: Scanned, Mixed, and Stylized Performance on PDFs varied significantly based on the file's complexity. * **Complex Scanned PDF:** This proved to be a major hurdle for non-OCR tools. * **Docling, Tesseract, and EasyOCR** successfully extracted the text, though with some character recognition and formatting errors. Docling notably managed to preserve some of the tabular structure. * **PDFPlumber, PyMuPDF, and PDFMiner** failed to extract any meaningful content, as they simply aren't designed for scanned, image-based PDFs. * **PaddleOCR** did extract text, but its accuracy was lower than the other OCR tools. * **PDF with Mixed Content (Text, Tables, Images):** This document was handled well by most packages. * **Docling, PDFPlumber, PyMuPDF, and PDFMiner** all extracted the text with high fidelity. * **PDFPlumber and PyMuPDF** also accurately extracted the tables. * **Tesseract and EasyOCR** also performed well, showing they can handle text-based PDFs too. * **Certificate PDF:** This test focused on non-standard layouts and stylized text. * All packages successfully extracted the main text content. * **Docling, PDFPlumber, and PyMuPDF** were the most successful at preserving the certificate's layout and formatting. ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1761843502153/1e919803-5e39-44ef-909e-9e0964bec3a0.png align="center") #### The Image-Only Test: PNG Invoice This test required pure OCR capabilities to extract data from an invoice image. * **Tesseract and EasyOCR** provided the most accurate extraction. EasyOCR was slightly better at recognizing the line items and prices. * **Docling** also performed well, demonstrating a robust OCR engine. * **PaddleOCR** struggled significantly with this document, producing an output that was largely inaccurate and unusable. ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1761843564350/b5190036-1d4f-4135-abb8-c3044dde97d9.png align="center") #### The DOCX Showdown: Standard Technical Manual As you might expect, the packages designed specifically for DOCX files performed flawlessly. * **PythonDocx and Mammoth** both excelled, extracting the text with perfect accuracy. They also preserved the original formatting, including headings and lists. * **Docling** also handled the DOCX file effectively, providing a clean Markdown output. ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1761843603818/30908229-788e-47c9-bfb5-b8bf7a96d37c.png align="center") --- ### Part 3: The Verdict and Recommendations This benchmark analysis reveals one clear truth: the best document extraction package is **highly dependent on your specific use case** and the types of documents you process. To help you decide, here is a summary of our findings: **Table 2: Summary of extractor performance, based on benchmark report data.** | **Extractor** | **Success Rate** | **Avg. Time (s)** | **Best Use Case** | | --- | --- | --- | --- | | PythonDocx | 100.0% | 0.050 | .docx files with tables | | Mammoth | 100.0% | 0.097 | .docx files (text-only) | | PDFMiner | 66.7% | 0.127 | Basic text extraction from PDFs | | PDFPlumber | 66.7% | 0.332 | Text-based PDFs with tables | | PyMuPDF | 66.7% | 0.480 | Fast text extraction from PDFs | | Tesseract | 100.0% | 1.612 | Scanned documents and images (fastest OCR) | | Easy OCR | 100.0% | 8.282 | Scanned documents (high accuracy OCR) | | Docling | 100.0% | 8.678 | Versatile all-in-one for mixed document types | | Paddle OCR | 100.0% | 21.515 | Not Recommended (failed quality tests) | #### Key Takeaways: * **The All-Rounder:** **Docling** stands out as a versatile and robust solution that performs well across *all* tested document types (DOCX, scanned PDFs, text PDFs, and images). This makes it a strong contender for a general-purpose document extraction tool. * **The Specialists:** If your task is highly specific, a specialized library is better. * For **high-speed DOCX processing**, **PythonDocx** is the clear winner. * For **fast extraction from text-based PDFs (especially with tables)**, **PDFPlumber** is an excellent choice. * For **high-accuracy OCR on scanned images**, **EasyOCR** had a slight edge, while **Tesseract** provided the fastest OCR performance. #### Key Takeaways: * **The All-Rounder:** **Docling** stands out as a versatile and robust solution that performs well across *all* tested document types (DOCX, scanned PDFs, text PDFs, and images). This makes it a strong contender for a general-purpose document extraction tool. * **The Specialists:** If your task is highly specific, a specialized library is better. * For **high-speed DOCX processing**, **PythonDocx** is the clear winner. * For **fast extraction from text-based PDFs (especially with tables)**, **PDFPlumber** is an excellent choice. * For **high-accuracy OCR on scanned images**, **EasyOCR** had a slight edge, while **Tesseract** provided the fastest OCR performance.