layoutparser
A unified toolkit for Deep Learning Based Document Image Analysis
Decision gist · record as of 2026-08-14
Yes, if you need document layout detection and are comfortable with a dormant package. LayoutParser has low install friction, permissive licensing, no known vulnerabilities, and strong community adoption. However, the last release was 2022-04-06—verify that its dependencies (especially opencv-python and deep learning model URLs) remain compatible with your environment before committing to production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a pre-trained model URL or local model path; deep learning model downloads may be large and require network access on first use.
- Low friction installation with a pure Python wheel and nine runtime dependencies (numpy, opencv-python, scipy, pandas, pillow, pyyaml, iopath, pdfplumber, pdf2image).
- Package is dormant—last release was 2022-04-06, over 1591 days ago, though the repository remains active with recent commits.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. No notable licensing constraints for most use cases.
last release 2022-04-06 (1591 days) · last repo commit 2024-08-15 · 5,770 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,190,511 downloads/mo, #4,241 on PyPI
Alternatives
Verify before relying
pip install layoutparser
import layoutparser as lp
model = lp.AutoLayoutModel('lp://EfficientDete/PubLayNet')
layout = model.detect(image)- Whether pre-trained models are automatically downloaded or must be manually configured.
- Current compatibility with modern versions of opencv-python, scipy, and pandas given the 2022-04-06 release date.
- Whether Detectron2 backend setup (mentioned in docs) is required for all layout detection tasks or only specific models.
- Actual monthly download volume and PyPI ranking to confirm production-readiness despite dormant status.
What it is and what it does
LayoutParser is a unified toolkit for document image analysis built on deep learning. It provides pre-trained models for detecting layout regions (text blocks, tables, figures) in document images and PDFs, along with specialized data structures and APIs for filtering, cropping, and analyzing those regions. The package integrates with OCR tools and supports loading layout data from JSON, CSV, and PDF files.
Typical workflows involve loading an image, running a layout detection model to identify regions, filtering or cropping regions of interest, optionally running OCR on each region, and visualizing results. It's designed for tasks like table extraction, hierarchical document parsing, and structured data extraction from scanned or digital documents. The package depends on computer vision (opencv-python, pillow, scipy) and data handling (numpy, pandas, pyyaml) libraries, plus PDF-specific tools (pdfplumber, pdf2image).
Use it for
- Extract tables from PDFs or scanned documents by detecting table regions and running OCR on each cell.
- Analyze complex multi-column documents by filtering layout regions by spatial position (e.g., left column only).
- Build document processing pipelines that detect and segment different content types (text, images, tables) for downstream processing.
- Convert unstructured document images into structured data by detecting layout regions and extracting text via OCR.
- Visualize document structure by drawing detected layout regions on images with element IDs and transparency.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need document layout detection and are comfortable with a dormant package.
LayoutParser has low install friction, permissive licensing, no known vulnerabilities, and strong community adoption. However, the last release was 2022-04-06—verify that its dependencies (especially opencv-python and deep learning model URLs) remain compatible with your environment before committing to production use.
Install
layoutparser on PyPI
Before you install
Low friction installation with a pure Python wheel and nine runtime dependencies (numpy, opencv-python, scipy, pandas, pillow, pyyaml, iopath, pdfplumber, pdf2image). Package is dormant—last release was 2022-04-06, over 1591 days ago, though the repository remains active with recent commits.
Requires a pre-trained model URL or local model path; deep learning model downloads may be large and require network access on first use.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. No notable licensing constraints for most use cases.
Quickstart
pip install layoutparser
import layoutparser as lp
model = lp.AutoLayoutModel('lp://EfficientDete/PubLayNet')
layout = model.detect(image)Verify before relying
- Whether pre-trained models are automatically downloaded or must be manually configured.
- Current compatibility with modern versions of opencv-python, scipy, and pandas given the 2022-04-06 release date.
- Whether Detectron2 backend setup (mentioned in docs) is required for all layout detection tasks or only specific models.
- Actual monthly download volume and PyPI ranking to confirm production-readiness despite dormant status.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesnumpyopencv-pythonscipypandaspillowpyyamliopathpdfplumberpdf2image |
| Maintenance | Dormant 1,591 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,190,511 / month, #4,241 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: layoutparser-0.3.4-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “deep learning document analysis”
- layoutparserLayoutParser provides deep learning-based document layout detection…
- python-doctrpython-doctr extracts and localizes text from documents and images…
- onnxtrOnnxTR extracts and recognizes text from documents (PDFs, images,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also unstructured-inference · surya-ocr · python-doctr · marker-pdf · docling-ibm-models · cnstd · pymupdf-layout · pdfminer · docling · img2table