2026 PDF Table Extraction Tools Review: 15 Tools Benchmarked

PDF was designed for "what you see is what you get" document exchange, not structured data — this creates fundamental barriers for table extraction:

• PDF lacks a table semantic model: Tables in PDFs are merely collections of lines and characters, with no abstract definition of rows, columns, or cells
• Complex table structures pose typical challenges: Merged cells, rotated text, cross-page tables, borderless tables — each of these scenarios causes most tools to produce severely degraded output
• Scanned vs. text-based PDFs are fundamentally different: The former relies on OCR engines to convert images to text, while the latter can directly parse the character encoding layer — each demands very different tool capabilities

This article presents a horizontal benchmark of mainstream commercial SDKs/APIs, open-source tools, and free online tools for PDF table extraction, producing quantifiable comparison results based on unified test samples.

Test PDF Description

Test File: Scanned PDF (SEO: The Art of SEO 3rd Edition, English, Page 114)

Output note: Screenshot of original scanned test file (tools that cannot process scanned files were tested on other text-based PDF tables instead)

This sample represents a high-difficulty test scenario: a scanned PDF with no text layer, semi-bordered table structure, and hierarchical headers. It requires tools to handle both OCR recognition and table structure reconstruction — most pure-parsing Python libraries cannot process it directly.

Online PDF Table Extraction Tools

Quick Selection Guide

1. ExtractTable

Web demo supports images only (JPG/PNG); paid version supports PDF. Output format: CSV/Excel.

Test Results: Due to demo limitations (images only, 2/day), full scanned PDF testing was not possible. Tested with image input instead — basic continuous text extraction was usable, but = was misrecognized as —, and bold styling, cell sizing, unordered lists, and other formatting were all lost.

According to Mark Kramer's benchmark, ExtractTable has issues with merged cell content misalignment and missing data.

2. SmallPDF

Offers PDF to Excel conversion with easy workflow. However, recognition capability is limited with complex structures like merged cells and rotated text.

Test Results: Performed well among online tools — table structure, merged cells, vertical text, and superscript/subscript were all effectively recognized.

3. iLovePDF

Offers PDF to Excel, PDF to Word, and multi-format conversion. Free version does not include OCR.

Test Results: OCR recognition accuracy was insufficient — some text areas were converted successfully, but significant content remained embedded as raw image slices within tables, failing to achieve true structured output.

4. PDF24 Tools

German-developed free PDF toolset offering PDF to Excel conversion with no file size limits and strong privacy protection. Limited support for complex table structures.

Test Results: Scanned documents were not OCR-processed — images were embedded directly into Excel output. Table structure recognition failed, with text loss and incorrect cell merge logic.

5. PDFTables.com (Online)

Supports drag-and-drop upload and conversion. Good conversion quality for standard bordered tables, but does not support scanned documents and has no free trial.

Commercial PDF Table Extraction Tools

Quick Selection Guide

1. ComPDF (Recommended)

Core Capabilities (Source: ComPDF Official)

ComPDF is one of the few commercial table extraction SDKs covering all three of the following capabilities:

Third-Party Benchmark Context: Mark Kramer from MITRE tested 12 mainstream tools in a horizontal benchmark, with the following conclusion (Source: Medium):

"Among all the commercial solutions, ComPDF was the only tool to correctly capture the hierarchical column headers."

Our Test Results:

2. AWS Textract

import boto3
client = boto3.client('textract')
response = client.analyze_document(
    Document={'S3Object': {'Bucket': 'my-bucket', 'Name': 'invoice.pdf'}},
    FeatureTypes=['TABLES', 'FORMS']
)

Evaluation:

• Basic Table Recognition: Good performance on text-based PDF bordered tables, accurate structure reconstruction
• Scanned OCR: Leveraging AWS's underlying OCR engine, scanned document processing is at the top tier of commercial APIs
• Merged Cells: Limited support; hierarchical header scenarios show deviations from individual cell outputs
• Ecosystem Integration: Native connectivity with AWS services (S3/Lambda/SageMaker), suitable for teams with existing AWS infrastructure

3. Nanonets

Third-Party Reference: Mark Kramer's benchmark shows lower omission rates than ExtractTable, but footnote content is output as garbled text, and merged cells cannot correctly express hierarchical relationships.

Our Test: Continuous text recognition is basically usable; basic text styles (font/typeface/size) are preserved; text color, table border line styles, and other formatting are not reconstructed.

4. Nutrient (formerly PSPDFKit)

Nutrient (formerly PSPDFKit) is a well-known cross-platform PDF SDK vendor, with core capabilities focused on PDF rendering, annotation, and editing. Table extraction is provided as an API module.

Product Positioning:

• Cross-platform native SDK with outstanding PDF rendering and interaction performance
• Table extraction is not its core scenario; custom development and integration are required
• Suitable for teams with existing Nutrient deployments needing supplementary table capabilities

Test Results: Text content recognition accuracy was insufficient, with issues including positional drift, spacing distortion, and partial text loss. Hierarchical relationships between headers and content were not correctly reconstructed.

5. Adobe Acrobat

Product Features:

• Strengths: Low barrier to entry, no coding required; Pro version includes OCR for direct scanned document export
• Limitations: No batch automation API; output quality is unstable with merged cells

Test Results: Overall table structure was reconstructed, but text loss and individual character recognition errors were present. Table border line styles and cell formatting were not preserved.

6. iText (iText 8 Core / iText 7 Community)

iText is one of the oldest PDF processing libraries (founded in 1998). iText does not provide a dedicated table extraction API — you must use LocationTextExtractionStrategy to parse text positions and infer table structure.

Test Results Across Three PDFs:

Capability Summary:

Key Takeaway: iText is mature in the domain of low-level PDF operations, but it is not an out-of-the-box table extraction tool. Text content extraction (cumulative 4,146 characters) is complete and reliable, but table structure (column alignment, merged cells, rotated text) is entirely lost. For structured table output, use dedicated table reconstruction tools like ComPDF (commercial), Camelot, or Docling (open-source).

Open-Source PDF Table Extraction Tools

Quick Selection Guide

1. Docling (IBM)

Core Features (Source: GitHub README + Technical Report)

• Multi-format support (PDF/DOCX/PPTX/XLSX/HTML/images/audio/email etc.)
• Built-in TableFormer (claims 93.6% accuracy vs Tabula 67.9%, Camelot 73.0%)
• Built-in OCR (via RapidOCR for scanned documents)
• Integrations: LangChain / LlamaIndex / Crew AI / Haystack

Test Results (June 2026)

Environment: Models downloaded successfully via HF_ENDPOINT=https://hf-mirror.com

• Conversion time: 9.39s
• Markdown length: 2573 chars
• Heron layout model: loaded (770/770 weights)
• OCR engine: RapidOCR
• Table detection: successful

Conclusions:

• Table Structure Recognition: 4-column multi-row table structure fully preserved, row-column correspondence correct
• Scanned Document Processing: Successfully extracted a structured table from a pure image PDF, validating AI model feasibility for scanned documents
• OCR English Accuracy: Low due to RapidOCR's optimization for Chinese characters; English recognition drift (e.g., "Google" misrecognized as "Googfe") is the current version's main bottleneck

Improvement Direction: Pairing with an English-specialized OCR engine (e.g., Tesseract English model) would significantly improve English table recognition accuracy on scanned documents.

2. pdfplumber

Test Results (June 2026):

Chars: 0, Lines: 0, Rects: 0, Tables: 0

Cannot process scanned files — 0 characters, 0 lines, 0 tables. Consistent with official documentation: "Works best on machine-generated, rather than scanned, PDFs".

Text-based PDF Tests (New):

Transcript (1).pdf (Student transcript, 2 pages):

Chars: 2905 | Tables: 0 | Time: 0.13s

pdfplumber successfully extracted 2905 characters of text but detected zero tables (0 tables). This is because pdfplumber's table detection depends on graphical lines (lines/rects), while this transcript uses borderless tables ("ghost tables") where the visual table structure is formed purely by text alignment.

Prot_000 8.pdf (Clinical protocol schedule, 1 page):

Chars: 1017 | Tables: 1 | Time: 1.19s

Successfully detected 1 table (Schedule of Events), benefiting from the table's complete border lines. However, merged cell information (e.g., cross-column time axis headers) was lost, and rotated text could not be recognized.

Conclusion: pdfplumber detects bordered tables accurately (consistent with Mark Kramer's findings), but is completely ineffective on borderless tables. As a low-level library, significant custom code is required for complex scenarios.

3. Camelot

5 Parsers: lattice (bordered) / stream (borderless) / network (text alignment) / ml (Table Transformer) / auto (automatic)

Test Results:

• Lattice Mode: Cannot process scanned files; parser explicitly rejects image-based page input

Text-based PDF Tests (New):

Transcript (1).pdf (transcript, borderless table):

Prot_000 8.pdf (clinical protocol, bordered table):

Conclusion: The extracted Excel files showed disorganized structure and text-to-cell mapping misalignment.

4. tabula-py

Test Results (Java 21 + tabula-py 2.10.0):

Tables found: 0

Cannot process scanned files. Even with Java installed, tabula-py cannot parse image-based PDFs without a text layer. Tabula's official documentation states: "Tabula only works on text-based PDFs, not scanned documents."

Text-based PDF Tests (New):

Transcript (1).pdf (Student transcript, 2 pages):

Tables found: 2 | Time: 1.54s
Table 1: 29 rows x 7 cols (transfer credits)
Table 2: 8 rows x 7 cols (GE 2022 semester)

tabula-py successfully identified the two semester course tables as separate DataFrames with complete column structure (Course Code / Description / Credits / Grade / Quality Points). All 29 transfer credit course records were correctly captured.

Prot_000 8.pdf (Clinical protocol schedule, 1 page):

ERROR: 'utf-8' codec can't decode byte 0xa1

tabula-py threw an encoding exception when processing this PDF. The PDF contains special characters (e.g., registered trademark symbol ®, ±), and tabula-py's Java subprocess output decoding failed.

Summary: tabula-py's text recognition for standard tables (e.g., transcripts) is basically correct, but the structure reconstruction is disorganized, text-to-row/column mapping is misaligned, and table borders were not correctly identified.

Conclusion

This article presents a horizontal benchmark of 15 tools using three test files of varying difficulty (scanned no-text-layer PDF / text-based transcript PDF / text-based clinical protocol PDF). The key findings are:

1. Scanned Documents Remain the Biggest Differentiator

• Of the 15 tools tested, only Docling, ComPDF, AWS Textract, Nanonets, Nutrient, and Adobe Acrobat can process scanned documents
• Pure-parsing tools like pdfplumber, Camelot(lattice), tabula-py, and iText completely fail on scanned documents (0 chars/0 tables)
• OCR capability is the first differentiator for PDF table extraction tools

2. Table Structure Reconstruction on Text-Based PDFs Is the Real Test

• Even among tools that successfully extract text from text-based PDFs, very few can fully preserve table structure (row-column correspondence, merged cells, border styles)
• Docling produced 27 Markdown tables (18,048 chars) from the text transcript and 21 tables (12,922 chars) from the clinical protocol — its AI-driven layout analysis significantly outperforms other open-source tools
• Camelot achieves the highest accuracy on bordered tables (lattice 97.97%), and its stream mode works on borderless tables (95.8%) — the best choice among pure-parsing tools
• tabula-py correctly identifies columns on standard transcript tables but has encoding compatibility issues
• iText extracts text completely (4,146 characters cumulative), but table structure is entirely lost — only continuous text stream output

3. The Gap Between Commercial and Open-Source Tools Is Largest for Complex Tables

• For merged cells/hierarchical headers, ComPDF is the only commercially available SDK that passed verification
• Open-source tools are adequate for simple bordered tables, but the gap widens significantly for complex table scenarios