We reviewed the top PDF table extraction tools to find the best Tabula alternative and here's what we found.
Lido is the best Tabula alternative for teams that need reliable table extraction from scanned PDFs, documents with complex layouts, and files with merged cells or multi-page tables that Tabula cannot handle.
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Extract tables from any PDF, including scans.
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Lido extracts tables from any PDF including scanned documents, image-based files, and complex layouts that Tabula cannot process. No manual area selection, no Java dependency, no code.
Enterprise teams use Lido to extract tables with merged cells, spanning headers, and multi-page continuations directly into Excel, Google Sheets, or their ERP systems.
Key Features
- ✓ Table extraction from native, scanned, and image-based PDFs
- ✓ Automatic table detection with no manual area selection
- ✓ Handles merged cells, spanning headers, and multi-page tables
- ✓ Extracts fields, line items, and key-value pairs beyond just tables
- ✓ Output to Excel, Google Sheets, CSV, JSON, or API
- ✓ Batch processing for multiple PDFs in one upload
Pros
- ✓ Works on scanned PDFs that Tabula cannot read
- ✓ No Java, no Python, no code required
- ✓ Handles complex table structures automatically
- ✓ Extracts more than just tables from documents
Cons
- ✗ Not a library you can embed in your own application
- ✗ Requires internet connection
Verdict
Lido is the best Tabula alternative for teams that need reliable table extraction from any PDF without the limitations of text-layer-only processing, manual selection, and Java dependencies.
Lido vs Tabula Comparison
Here's how Lido and Tabula compare across the features that matter most for PDF table extraction.
Scanned PDFsWorks on scanned and image-based PDFsDoes not work, text-layer PDFs only
Table detectionAutomatic, no manual selectionRequires manual area selection or unreliable auto-detect
Merged cellsHandled automaticallyBreaks into misaligned columns
Multi-page tablesStitched together automaticallySplit into separate fragments per page
Beyond tablesExtracts any field, header, or line itemTables only, nothing else
OutputExcel, Google Sheets, CSV, JSON, APICSV or TSV only
Technical skillsNone requiredJava required, Python wrapper optional
Batch processingUpload multiple PDFs at onceOne file at a time
MaintenanceNoneJava dependency, limited updates
Bottom line
Choose Tabula if:
- You need a free library to embed in your own Java application
- You only work with native text-layer PDFs
Choose Lido if:
- You need table extraction from scanned or image-based PDFs
- You process documents with merged cells or multi-page tables
- You need structured output in spreadsheets or JSON
- Your team needs to extract data without coding
2. Camelot
Camelot is a Python library built specifically for extracting tables from PDF files. It offers two parsing modes, lattice for bordered tables and stream for borderless ones, giving developers more control over detection than Tabula provides.
Camelot is a Python library for extracting tables from PDFs. It offers two parsing modes (lattice and stream) and gives more control than Tabula over table detection parameters.
Key Features
- ✓ Lattice mode for bordered tables, stream mode for borderless
- ✓ Table accuracy scoring
- ✓ Visual debugging tools
- ✓ Output to CSV, Excel, JSON, HTML
Pros
- ✓ More control over table detection than Tabula
- ✓ Visual debugging helps tune extraction
Cons
- ✗ Text-layer PDFs only, same as Tabula
- ✗ Requires Python programming
- ✗ Struggles with merged cells and complex structures
Verdict
Camelot gives more control than Tabula but shares the same core limitation: no scanned PDF support and programming required. A lateral move, not an upgrade.
3. Amazon Textract
Amazon Textract is AWS's cloud-based document analysis service that detects tables, form fields, and text from any document type including scanned PDFs. It solves Tabula's biggest limitation by processing image-based files, but requires an AWS account and programming knowledge to use.
Amazon Textract is AWS's document analysis API that detects text, tables, and form fields from any document including scanned PDFs. Handles what Tabula cannot but requires engineering resources.
Key Features
- ✓ Table extraction from scanned and native PDFs
- ✓ Form field and key-value pair detection
- ✓ AWS ecosystem integration
- ✓ High scalability
Pros
- ✓ Works on scanned PDFs unlike Tabula
- ✓ Detects tables without manual selection
Cons
- ✗ Requires AWS account and programming
- ✗ No user interface for non-technical users
- ✗ Costs scale with volume
Verdict
Amazon Textract solves Tabula's scanned PDF problem but still requires engineering resources. A cloud API, not a user-facing tool.
4. Google Document AI
Google Document AI is Google Cloud's document processing service with layout analysis that detects tables, forms, and key-value pairs. It handles scanned PDFs and complex layouts that Tabula struggles with, but requires GCP infrastructure and engineering resources.
Google Document AI is Google Cloud's document processing service with layout analysis that detects tables, forms, and entities. Stronger table handling than Tabula but requires GCP setup.
Key Features
- ✓ Table and form extraction from any PDF
- ✓ Pre-trained processors for common documents
- ✓ Custom processor training
- ✓ Free tier for testing
Pros
- ✓ Handles scanned PDFs and complex layouts
- ✓ Free tier available
Cons
- ✗ Requires Google Cloud Platform setup
- ✗ Programming required to integrate
- ✗ Custom processors need training data
Verdict
Google Document AI handles tables better than Tabula but requires GCP knowledge and coding. An API upgrade, not a simpler workflow.
5. Azure AI Document Intelligence
Azure AI Document Intelligence is Microsoft's cloud-based document processing service with strong table and layout detection. It works on scanned and native PDFs alike and integrates natively with the Microsoft ecosystem, but requires Azure setup and coding to implement.
Azure AI Document Intelligence is Microsoft's cloud document processing service with strong table extraction capabilities. Works on scanned PDFs and integrates with the Microsoft ecosystem.
Key Features
- ✓ Layout analysis with table extraction
- ✓ Pre-built models for invoices and receipts
- ✓ Custom model training
- ✓ Microsoft ecosystem integration
Pros
- ✓ Strong table detection from any PDF type
- ✓ Native Microsoft integration
Cons
- ✗ Requires Azure subscription and coding
- ✗ No interface for business users
- ✗ Custom models need labeled data
Verdict
Azure AI Document Intelligence is a capable table extraction upgrade for Microsoft shops. Still requires developers, but handles what Tabula cannot.
6. Nanonets
Nanonets is an AI document processing platform that offers table extraction with a no-code interface. It handles scanned PDFs and provides pre-built models for common document types like invoices and receipts, with custom model training available for other formats.
Nanonets is an AI document processing platform with table extraction capabilities and a no-code interface. Handles scanned PDFs and offers pre-built models for common document types.
Key Features
- ✓ Table extraction from scanned and native PDFs
- ✓ Pre-built models for invoices and receipts
- ✓ Custom model training
- ✓ No-code workflow builder
Pros
- ✓ No coding required
- ✓ Handles scanned PDFs
Cons
- ✗ Custom document types need model training
- ✗ Accuracy depends on training data
- ✗ Can get expensive at volume
Verdict
Nanonets removes the coding barrier but still requires model training for non-standard documents. Less hands-off than Lido for diverse table formats.
7. pdfplumber
pdfplumber is a Python library that parses PDF content at the character level, giving developers fine-grained control over text and table extraction. It handles borderless tables more reliably than Tabula but shares the same text-layer-only limitation.
pdfplumber is a Python library for extracting text and tables from PDFs with fine-grained control over character and line positioning. More precise than Tabula on borderless tables.
Key Features
- ✓ Character-level PDF parsing
- ✓ Table extraction with custom settings
- ✓ Visual debugging with page images
- ✓ Lightweight Python library
Pros
- ✓ More precise than Tabula on borderless tables
- ✓ Fine-grained control over extraction
Cons
- ✗ Text-layer PDFs only, no scanned document support
- ✗ Requires Python programming
- ✗ Manual tuning needed per document layout
Verdict
pdfplumber offers more precision than Tabula but shares the same limitations: no scanned PDF support and Python required. Better extraction control, same fundamental gaps.
Getting Started with Lido
Most teams complete their first table extraction in under five minutes.
Step 1. Sign up for free at lido.app. No credit card required. You get 50 pages to test.
Step 2. Upload the PDFs that gave Tabula trouble. Scanned documents, image-based files, tables with merged cells, or multi-page tables.
Step 3. Describe what to extract in plain English. No area selection, no coordinates, no code.
Step 4. Compare the results. Check the structured table output against your Tabula workflow and note what Lido captured that Tabula missed.
No more manual area selection
Upload a PDF. Get clean table data back.
No Java, no code, no credit card.
Frequently Asked Questions
What is the best Tabula alternative?
Lido is the best Tabula alternative for teams that need reliable table extraction from any PDF, including scanned documents and image-based files. Unlike Tabula, Lido handles complex table structures, merged cells, and multi-page tables automatically without manual area selection.
Why does Tabula not work on scanned PDFs?
Tabula only reads text that is embedded in the PDF file layer. Scanned PDFs are images with no embedded text, so Tabula cannot detect any content. You need an OCR tool first. Lido handles both native and scanned PDFs automatically with no extra steps.
Can Tabula extract tables without selecting areas manually?
Tabula offers an auto-detect mode but it frequently misses tables or captures the wrong area, especially with complex layouts. Most users end up manually drawing selection boxes. Lido detects and extracts all tables automatically with no manual selection required.
Does Tabula work with merged cells and multi-page tables?
Tabula struggles with merged cells, spanning headers, and tables that continue across pages. It often splits these into misaligned columns or separate fragments. Lido handles merged cells and multi-page tables natively and outputs them as clean, structured data.
Can Lido replace Tabula for PDF table extraction?
Yes. Lido extracts tables from any PDF including scanned documents, handles complex layouts Tabula cannot process, and outputs directly to Excel, Google Sheets, CSV, or JSON. No Java installation, no manual area selection, no code required.
Is there a Tabula alternative that works without coding?
Lido is a no-code Tabula alternative. Upload a PDF, and Lido extracts tables and structured data automatically. No Java, no Python, no command line. Results go directly into spreadsheets or JSON.
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