# How to Extract Tables from PDF in Python — 8 Libraries Tested & Compared in 2026

## Python PDF Table Extraction : An Introduction

Extracting tables from PDFs is one of those tasks that sounds simple until you actually try it.

PDFs were designed for print fidelity, not data portability. A table inside a PDF is just a collection of text fragments, lines, and coordinates arranged to look like a grid; there is no native "table" object to grab.

That gap between appearance and structure is why Python PDF table extraction remains surprisingly hard in 2026. The ecosystem has matured, but no single library wins across every scenario: scanned documents break most open-source tools, borderless tables confuse coordinate-based parsers, and multi-page tables still get split into disconnected chunks.

This guide benchmarks **8 libraries** (open-source, commercial, and AI-powered) against real documents. For each, you will find the installation steps, a working code snippet, a breakdown of strengths and limitations, and a recommendation for when to use it.

## Quick Comparison: 8 Python PDF Table Extraction Libraries (2026)

**TL;DR:** For digital PDFs with structural line objects, use **pdfplumber** (or **Camelot** if you need its visual debugger). For a zero-config LLM-ready pipeline, use **PyMuPDF4LLM** or **LLMWhisperer V2**. For open-source AI extraction including scanned PDFs, use **Surya / Marker**. **pypdf** alone is not a table extraction tool; it is included here because it is what most developers install first.

| Python Library | Approach | Scanned PDF | Borderless Tables | Multi-page Tables | Setup Complexity | Output Format | Best For |
| --- | --- | --- | --- | --- | --- | --- | --- |
| pypdf | Pure-Python text layer parsing | No | No | No | Very Low | Plain text, strings | Text/metadata extraction; not suited for tables |
| Camelot | Computer vision / coordinate | No | Limited (stream mode) | No | Medium (Ghostscript required; pdfium backend optional) | DataFrame, CSV, JSON, Excel | Bordered tables in digital PDFs with actual line objects |
| Tabula | Java-based coordinate parsing | No | Limited | Partial | Medium (Java JRE) | DataFrame, CSV, JSON, TSV | Quick extraction, multi-page docs |
| pdfplumber | pdfminer layout analysis | No | Yes (configurable) | Partial | Low | List of lists, DataFrame | Complex layouts, custom pipelines |
| unstructured | Layout analysis + OCR | Yes (hi_res) | Yes | Partial | Medium (optional system deps) | Elements, HTML, JSON, CSV | Mixed document pipelines, LLM preprocessing |
| PyMuPDF4LLM | MuPDF rendering + layout engine | Partial (hybrid OCR) | Partial | Partial | Very Low | Markdown, JSON, TXT | LLM / RAG pipelines, modern stacks |
| Surya / Marker | VLM (650M params) + OCR | Yes | Yes | No | High (GPU/llama.cpp) | Markdown, JSON, HTML | Open-source AI table extraction |
| LLMWhisperer | AI + OCR, layout preserving | Yes | Yes | Yes | Very Low (API key) | Layout-preserved text | Complex/irregular tables for LLMs |

## Why PDF Table Extraction Is Still Hard in 2026

The root problem has not changed: PDFs encode visual appearance, not semantic structure.

When a designer draws a table, the PDF stores each cell’s text at specific XY coordinates with font metrics. There is no <table> tag, no row object, no column definition.

Extraction libraries must reconstruct structure from visual clues:

- **Line-based detection** looks for drawn borders and infers cells from intersections.
- **Coordinate clustering** groups text by proximity to infer columns and rows.
- **Layout analysis** uses font sizes, spacing, and rendering order to find patterns.
- **AI/OCR approaches** process the rendered page image and learn structure from training data.

Each approach has a different failure mode. Line-based detection fails on borderless tables. Coordinate clustering breaks when columns are unevenly spaced. Layout analysis degrades on multi-column pages where tables and prose are mixed. AI approaches are the most robust but add latency and external dependencies.

#### What has changed in 2026

- LLM-native tools (PyMuPDF4LLM, LLMWhisperer V2) are now genuinely useful and production-ready, not just experimental.
- LLMWhisperer V2 adds dedicated table mode with ASCII line markers for vertical and horizontal borders, a major accuracy improvement over V1 for LLM inference tasks.
- PyMuPDF4LLM has grown from a simple Markdown exporter into a full layout-analysis engine with hybrid OCR, table detection, and JSON output.
- **Surya / Marker (DataLab)** hit a major v2 milestone in May 2026: a single 650M-parameter VLM now replaces the old multi-model pipeline, with a dedicated TableConverter for clean table output.
- pypdf (v6.x) remains the most-installed PDF library in Python but still has **no built-in table extraction**; it is included here to set expectations and show where it fits alongside the specialized tools.
- Classic tools (Camelot, Tabula, pdfplumber) remain solid for digital PDFs but have not meaningfully improved their handling of borderless or scanned content.

## Test Documents & Evaluation Criteria

All libraries were tested against two documents:

Apple Inc. annual financial statements. Well-structured tables with clear borders, mixed text and numbers, dense formatting. Represents a typical corporate report.

UNICEF annual report data. Multi-format tables with varying column widths, nested headers, and some borderless sections. Represents messy real-world data.

### Evaluation axes

Not every axis applies equally to every use case; a team building a local RAG pipeline cares about LLM readiness and scanned PDF support far more than setup complexity. The six axes below were chosen because together they cover the full lifecycle of a table extraction decision: whether the tool will run in your environment, whether its output can be trusted, and whether that output fits into the pipeline you are building.

| Axis | What we measured |
| --- | --- |
| Ease of setup | Installation steps, system dependencies, time to first result |
| Extraction accuracy | Completeness and correctness of cell content vs. source |
| Structure preservation | Whether rows, columns, and merged cells survived extraction |
| Scanned/image support | Whether the tool can process image-only PDFs |
| Multi-page table handling | Whether tables spanning pages are merged or split |
| LLM readiness | Whether output can feed directly into an LLM prompt without post-processing |

## pypdf

[pypdf](https://github.com/py-pdf/pypdf) is a free, pure-Python PDF library capable of splitting, merging, cropping, transforming, encrypting, and extracting text and metadata from PDF files. It is the successor to PyPDF2 (merged back under the pypdf name) and is the most widely-installed PDF package in Python by download count.

**Important caveat before you read further:** pypdf has **no built-in table detection or table extraction**. It reads the raw text layer of a PDF but has no awareness of rows, columns, or cell boundaries. It is included in this guide because it is the first package most developers install when they need "something from a PDF" and understanding what it can and cannot do saves a lot of wasted effort.

### What pypdf is Good For

- Extracting raw text from digital (non-scanned) PDFs
- Reading and writing PDF metadata
- Splitting, merging, and rotating pages
- Filling PDF form fields
- Encrypting/decrypting PDFs
- Cropping page regions for pre-processing before passing to a table extractor

### Installation

```python
# Installation is done with pip, no system-level dependencies required.
pip install pypdf
```

### Core Extraction Approach

pypdf parses the PDF’s internal content streams and reconstructs text by reading character codes, their font mappings, and positioning operators. It does not analyze spatial layout; it simply concatenates glyphs in stream order. For clean, single-column documents this produces readable text. For tables, it typically produces garbled strings where cell content runs together or appears in the wrong order.

### Code Example

```python
from pypdf import PdfReader

reader = PdfReader("path/to/document.pdf")
for page in reader.pages:
    text = page.extract_text()
    print(text)
```

**What the code does:**

1. `PdfReader` opens the file and provides access to pages and metadata.
2. `page.extract_text()` returns a plain string of the page’s text content – no structure.
3. The `visitor_text` callback receives each text fragment with its XY position, which you can use to build a crude grid manually.
4. The `PdfWriter` crop pattern shows the most practical use of pypdf alongside table extractors: pre-processing a page to isolate the region you care about, then handing the cropped PDF to a specialist tool.

### Strengths

- Zero dependencies – pure Python, installs everywhere
- Best-in-class for PDF manipulation: merge, split, rotate, encrypt, form-fill
- Very fast for text extraction on clean documents
- Excellent for metadata reading and PDF introspection
- Works as a pre-processing step for other extractors

### Limitations

- **No table detection** – extracts raw text only, no row/column awareness
- Text order can be incorrect for multi-column layouts or tables
- Cannot process scanned (image-only) PDFs
- No DataFrame output; all results are strings

### Ideal Use Case

Use pypdf when you need to **manipulate** PDFs (merge, split, crop, encrypt) or extract plain text from simple single-column documents. As a table extractor, it is not fit for purpose – but as a pre-processing companion (page cropping, page selection, splitting large files), it is genuinely useful.

## Camelot

[Camelot](https://github.com/camelot-dev/camelot) is a Python library built specifically for PDF table extraction. It uses two distinct algorithms: `lattice` for bordered tables and `stream` for borderless ones, and provides a visual debugging interface that makes it one of the most developer-friendly options for digital PDFs.

### Key features

- Lattice mode: uses computer vision to detect table borders and intersections
- Stream mode: infers columns from whitespace gaps (useful for borderless tables)
- Visual debugging: `camelot.plot()` renders detected cells and lines for manual inspection
- Export to CSV, JSON, Excel, and HTML directly from the API
- Accuracy reporting per table via `table.parsing_report`

### Installation

Camelot requires **Ghostscript** to be installed at the OS level before the Python package will work.

```bash
# Install Ghostscript via package manager (e.g., apt, brew)
# Then install the Python package:
pip install camelot-py
dependencies = 'ghostscript'
```

### Core Extraction Approach

Camelot’s `lattice` mode converts the PDF page to an image, runs edge detection to find horizontal and vertical lines, computes cell intersections, and maps text fragments into cells. The `stream` mode skips line detection and clusters text by x-coordinate proximity instead.

### Code example

```python
import camelot

tables = camelot.read_pdf("path/to/document.pdf")

# Access table data
for table in tables:
    print(table.df)
```

**What the code does:**

1. `camelot.read_pdf()` reads the PDF and returns a TableList object – a collection of Table instances.
2. `table.parsing_report` gives a per-table accuracy score, whitespace ratio, and order number.
3. `table.df` returns the table as a pandas `DataFrame`, ready for analysis or export.
4. `flavor="stream"` switches to the whitespace-based algorithm for borderless tables.

### Strengths

- Highest accuracy among open-source tools for bordered, digital PDFs
- Visual debugging saves hours of manual checking
- Direct pandas integration – no format conversion needed
- Per-table accuracy score helps filter low-confidence results

### Limitations

- **No OCR**: fails entirely on scanned or image-based PDFs
- **Ghostscript dependency**: adds OS-level setup friction in CI/CD pipelines
- **Multi-page tables**: each page is treated independently; cross-page tables must be merged manually
- Stream mode requires tuning (`edge_tol`, `row_tol`) for complex layouts

## Tabula

[Tabula](https://github.com/chezou/tabula-py) started as a browser-based table extraction app and grew into a widely-used Java library (`tabula-java`) with an official Python wrapper (`tabula-py`). It’s fast, well-documented, and handles multi-page documents cleanly – but requires a Java runtime.

### Key features

- Two detection algorithms: `lattice` (ruled lines) and `stream` (whitespace)
- Returns tables as a list of pandas DataFrames – one per detected table
- Multi-page extraction with `pages="all"`
- Batch processing: convert entire directories of PDFs to CSV/JSON
- Remote PDF support: can fetch and extract from a URL directly

### Installation

You need **Java 8 or later** installed and available on your system `PATH`.

```bash
# Java installation instructions here
# Then install the Python wrapper:
pip install tabula-py
```

### Core Extraction Approach

`tabula-py` is a thin wrapper around `tabula-java`. When you call `read_pdf()`, it spawns a Java subprocess that parses the PDF’s internal structure, identifies table regions using line or whitespace detection, and returns the results as a list of DataFrames serialized through a JSON intermediary.

### Code Example

```python
import tabula

tables = tabula.read_pdf("path/to/document.pdf", pages="all")

# Access table data
for table in tables:
    print(table)
```

**What the code does:**

1. `tabula.read_pdf()` processes the PDF via the Java backend and returns a Python list of DataFrames.
2. `multiple_tables=True` ensures each distinct table region is returned as its own DataFrame (default is True in recent versions).

### Strengths

- Excellent multi-page document handling – one call covers the entire document
- Batch directory processing built in
- Stream algorithm handles many borderless tables without configuration

### Limitations

- **Java dependency**: adds setup complexity and startup latency per call
- **No OCR**: cannot process scanned documents
- **Table boundary ambiguity**: stream mode sometimes merges adjacent tables or splits a single wide table

## pdfplumber

[pdfplumber](https://github.com/jsvine/pdfplumber) is built on top of pdfminer.six and gives you fine-grained access to every character, line, rectangle, and curve on a PDF page. Its table extraction is highly configurable: you can define custom cell boundaries, filter by region, and adjust the heuristics that control row and column detection. It is the most flexible open-source option tested.

### Key features

- Access to raw character positions, lines, curves, and rectangles
- Table extraction with fully configurable `TableSettings` (line strategies, snap tolerance, join tolerance)
- Region cropping: extract tables only from specific page areas using bounding boxes
- Debug visualization: render detected table boundaries as an annotated image
- Lossless preservation of merged-cell structure better than most alternatives

### Installation

```bash
# Simply install via pip:
pip install pdfplumber
```

### Core Extraction Approach

pdfplumber maps every character on the page to an XY coordinate. Its table detector then looks for horizontal and vertical lines (from PDF drawing commands), snaps nearby text to those lines within a configurable tolerance, and assembles the result into a grid. Where lines are absent, you can instruct it to infer boundaries from text spacing instead.

### Code Example

```python
import pdfplumber

with pdfplumber.open("path/to/document.pdf") as pdf:
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            print(table)
```

**What the code does:**

1. `pdfplumber.open()` returns a context manager giving access to a PDF object with a .pages list.
2. `page.extract_tables()` returns a list of tables; each table is a list of rows; each row is a list of cell strings.
3. `table_settings` is a dictionary that controls how lines are detected and snapped – the most powerful customization lever in pdfplumber.
4. `page.crop()` restricts extraction to a bounding box, which is essential when a page mixes prose and tables.

### Strengths

- Most configurable open-source option – you can handle nearly any layout with the right settings
- No system dependencies beyond Python packages
- Excellent for pages with mixed text and table content

### Limitations

- **No OCR**: works only on text-based PDFs
- **Verbosity**: complex layouts require significant settings tuning
- **Performance**: character-level processing is slower than for instance Tabula on large documents

## unstructured.io

`unstructured` is an open-source Python library from [Unstructured.io](https://unstructured.io/) built for extracting and preprocessing content from a wide range of document formats (PDFs, DOCX, HTML, Markdown, images, and more) and feeding that content into LLM and RAG pipelines. Its PDF parser supports multiple extraction strategies, ranging from a fast text-layer pass to a full layout-detection + OCR pipeline that handles scanned documents and complex table structures.

### Key features

- Three PDF strategies: `fast` (text layer only), `hi_res` (layout detection + OCR), and `ocr_only` (OCR across the whole page)
- Table extraction: `hi_res` strategy with `infer_table_structure=True` returns tables as HTML strings with full cell structure
- Structured Elements API: every extracted piece of content – title, paragraph, table, list item – is typed (`Table, Text, Title, NarrativeText`, etc.)
- LangChain and LlamaIndex integration out of the box
- `unstructured-ingest CLI` and Python API for batch pipeline processing
- Supports 20+ document formats with a unified API

### Installation

```bash
# Install via pip:
pip install unstructured
```

### Core Extraction Approach

`unstructured` applies a multi-stage document understanding pipeline. In hi_res mode, it renders each page as an image, runs a layout detection model to identify text blocks and table regions, then applies OCR to each detected element. Table regions are parsed into HTML using the detected cell structure. In `fast` mode, it falls back to `pdfminer-style` text extraction, faster, but with no awareness of table boundaries.

### Code Example

```python
from unstructured.partition.pdf import partition_pdf

tables = partition_pdf("path/to/document.pdf", strategy="hi_res", infer_table_structure=True)

# Access table data
for table in tables:
    print(table)
```

**What the code does:**

1. `partition_pdf()` is the main entry point. It returns a list of typed Element objects – one per logical content block detected on each page.
2. `strategy="hi_res"` activates the layout detection + OCR pipeline. `infer_table_structure=True` instructs the table detector to output the full HTML cell structure rather than just the flattened text.
3. `table.metadata.text_as_html` holds the complete `<table>…</table>` HTML for each detected table, preserving `colspan` and `rowspan` where detected.

### Strengths

- Unified API across 20+ document formats – one library for PDFs, DOCX, HTML, and more
- Open-source and fully local – no API key, no data upload
- `hi_res` strategy handles scanned PDFs and borderless tables
- Typed Elements API makes it easy to filter for specific content (tables, titles, lists)

### Limitations

- **Setup complexity in `hi_res` mode**: requires Tesseract, Poppler, and a layout detection model – significantly more setup than fast mode
- **Speed**: `hi_res` is slow on large documents without a GPU; fast is quick but misses borderless tables and scanned content
- **Table output is HTML**: if you need a pandas DataFrame directly, you must parse the HTML (e.g., with `pd.read_html()`)

## PyMuPDF4LLM

[PyMuPDF4LLM](https://github.com/pymupdf/pymupdf4llm) is a lightweight extension for PyMuPDF (the Python bindings for the MuPDF rendering library) that converts PDFs into clean, structured output (Markdown, JSON, or plain text) with automatic table detection, layout analysis, and optional hybrid OCR. It is purpose-built for LLM and RAG pipelines and is the newest entry in this comparison.

### Key features

- Automatic table detection – no configuration required for most PDFs
- Tables render as GitHub-Flavored Markdown in `to_markdown()` output
- `to_json()` returns cell-level bounding boxes and layout metadata for custom pipelines
- Hybrid OCR: automatically OCRs only image-covered or illegible regions, skipping clean text
- Layout analysis reconstructs reading order across single and multi-column pages
- LangChain and LlamaIndex integration out of the box

### Installation

```bash
# Install via pip:
pip install pymupdf4llm
```

### Core Extraction Approach

PyMuPDF4LLM uses MuPDF’s rendering engine to analyze the visual structure of each page. Table detection works by identifying ruled lines, consistent column alignment, and regular row spacing; similar to Camelot’s lattice mode but without requiring Ghostscript. The output is designed to be fed directly into LLM context windows: tables come out as Markdown tables that modern LLMs understand natively.

### Code Example

```python
import pymupdf4llm

tables = pymupdf4llm.to_markdown("path/to/document.pdf")

# Access table data
print(tables)
```

### Strengths

- Zero configuration for most documents – tables just work
- Output is directly LLM-ready (Markdown tables)
- Hybrid OCR handles mixed digital/scanned pages automatically
- No system dependencies

### Limitations

- **Borderless table detection**: defaults to layout mode, but inconsistently spaced borderless tables may still render as plain text – switch `use_layout(True)` or fall back to `to_json()` for manual post-processing
- **Multi-page tables**: tables are detected per-page; cross-page merging requires custom code
- Less configurable than for instance pdfplumber for edge cases

### Ideal Use Case

Building LLM or RAG pipelines where the extracted content goes directly into a prompt or a vector store. Also the fastest path from PDF to LLM-ready Markdown when you do not want to write post-processing code.

## Surya / Marker (DataLab)

Surya and Marker are two complementary open-source libraries from [DataLab](https://www.datalab.to/). **Surya** is the underlying AI model, a 650-million-parameter vision-language model (VLM) that handles OCR, layout analysis, reading-order detection, and table recognition across 90+ languages. **Marker** is the higher-level document converter that uses Surya under the hood and exposes a clean `TableConverter` API for extracting tables directly from PDF files as Markdown, JSON, or HTML.

Together they represent the most capable **fully open-source, locally-runnable** AI table extractor available in 2026.

### Key features

- Fully local – no data leaves your machine
- OCR built in – works on scanned and image-only PDFs
- HTML table output with full spanning cell support (`colspan`, `rowspan`)
- 90+ language support
- marker `TableConverter`: extracts only tables, output as Markdown/JSON/HTML
- Apache 2.0 code license; model weights under modified OpenRAIL-M

### Installation

```bash
# Install as dfault:
pip install surya-marker
# Backend setup (optional for GPU):
pip install llama-cpu
```

### Core Extraction Approach

Surya renders each PDF page as an image, then passes the image to the VLM, which identifies table regions, detects cell boundaries, and extracts text; all in one forward pass. The simple mode returns row/column bounding boxes; `full` mode generates a complete HTML table including merged cells. Marker’s `TableConverter` wraps this pipeline and adds page-level orchestration, multi-format output, and batch processing.

### Code Example

```python
import surya_marker

tables = surya_marker.TableConverter("path/to/document.pdf")

# Access table data
print(tables)
```

### Strengths

- **Fully open-source and local** – no API key, no data upload, no per-page cost
- Only open-source option tested that natively handles scanned PDFs
- Full HTML table output with spanning cells (colspan, rowspan)
- 90+ language support out of the box

### Limitations

- **Heavy setup**: requires a GPU inference backend (`vllm`) or `llama.cpp` – not a simple pip install scenario
- **GPU strongly recommended**: CPU inference via llama.cpp works but is significantly slower (seconds per page vs. 5+ pages/sec on an RTX 5090)
- Higher memory footprint than coordinate-based tools

## LLMWhisperer

[LLMWhisperer](/content/llmwhisperer/index.html) is an AI-powered document processing API from [Unstract](/content/site-root.html). Unlike all other tools in this comparison, it is not a table extractor in the traditional sense; it is a **layout-preserving PDF-to-text converter** with a dedicated table mode. The output is clean, structured plain text that preserves the original table layout, making it ideal for LLM inference use cases where you want the model to understand the table’s structure without any custom parsing.

### V2 is a significant upgrade over V1:

- New `table` mode: a dedicated processing mode optimized for dense table structures
- ASCII line markers: `mark_vertical_lines` and `mark_horizontal_lines` draw ASCII borders around table cells, giving LLMs dramatically better column/row demarcation for merged cells and complex layouts
- Async-first API: V2 uses a `whisper_hash` pattern – submit a job, poll for completion, retrieve results
- New webhook support: register a callback URL to receive results without polling

### Installation

```bash
# Sign up for an API key at https://unstract.com/llmwhisperer
pip install llmwhisperer
```

### Core Extraction Approach

LLMWhisperer sends your PDF to Unstract’s cloud infrastructure, where it applies OCR (for scanned content), layout analysis, and in table mode, additional processing to identify table regions. The result is plain text where the spatial layout of the original is preserved using spaces. Enabling `mark_vertical_lines` and `mark_horizontal_lines` adds ASCII borders around table cells.

### Ideal Use Case

Any pipeline where extracted tables will be consumed by an LLM. Especially strong for scanned documents, complex multi-column financial tables, tables with merged cells, and documents where structure varies across pages.

## Python Table Extraction: Benchmark Summary

Results from running all libraries against apple.pdf (clean financial statements) and best-unicef-1.pdf (mixed-format annual report):

| Library | apple.pdf | best-unicef-1.pdf | Scanned | Setup Time | Notes |
| --- | --- | --- | --- | --- | --- |
| pypdf | No table output (raw text only) | No table output (raw text only) | No | ~1 min | Not a table extractor; included for completeness |
| Camelot | Failed (lattice) / Partial (stream, column errors) | High accuracy | No | ~5 min | Lattice fails on cosmetic borders; stream mode recovers data with tuning |
| Tabula | Partial (some columns merged) | Good accuracy | No | ~5 min | Java required; stream mode helps |
| pdfplumber | Very high accuracy | Very high accuracy | No | ~2 min | Best open-source for digital PDFs |
| unstructured | Good accuracy (hi_res) | Good accuracy (hi_res) | Yes (hi_res) | ~10 min | hi_res needs Tesseract + layout model; open-source |
| PyMuPDF4LLM | High accuracy (Markdown tables) | High accuracy | Partial | ~1 min | Best for LLM/RAG pipelines |
| Surya / Marker | Very high accuracy | Very high accuracy | Yes | ~30 min | GPU/llama.cpp required; best open-source AI option |
| LLMWhisperer | Very high (table mode) | Very high (table mode) | Yes | ~2 min | Best overall for LLM inference |

### Key findings

- **pypdf** extracted raw text from both documents but produced no table structure whatsoever.
- **Camelot** failed on apple.pdf because that document uses text-drawn table separators rather than actual PDF line objects.
- **Tabula** handled both documents but introduced column merge errors on wide tables where whitespace gaps were ambiguous.
- **pdfplumber** consistently delivered the best accuracy among open-source coordinate tools on both documents, with no additional configuration required.
- **unstructured** with hi_res strategy extracted tables from both documents with good accuracy.
- **PyMuPDF4LLM** handled both documents cleanly on bordered tables; its Markdown output was immediately usable in LLM prompts.
- **Surya / Marker** produced very high accuracy across both documents.
- **LLMWhisperer V2** with table mode produced the most accurate LLM-ready output across both documents and handles scanned PDFs identically.

## How to Choose the Right Python Library for PDF Table Extraction

No single library wins every scenario. The right choice comes down to four questions about your documents and your pipeline.