{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"LayoutParser provides deep learning-based document layout detection and analysis, with APIs for detecting layout regions, filtering elements, performing OCR, and loading/visualizing document structures from images and PDFs.","skillfed_tags":["document-analysis","ocr","layout-detection"],"use_cases":["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."],"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.\n\nTypical 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).","worth_installing":"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\u2014verify that its dependencies (especially opencv-python and deep learning model URLs) remain compatible with your environment before committing to production use."},"id":"layoutparser","links":{"html":"https://skillfed.io/packages/layoutparser","md":"https://skillfed.io/packages/layoutparser.md","pypi":"https://pypi.org/project/layoutparser/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2022-04-06","license_spdx":null,"license_treatment":"permissive","name":"layoutparser","python_support":"supports_current","summary":"A unified toolkit for Deep Learning Based Document Image Analysis"},"popularity":{"monthly_downloads":1190511,"position":4241,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.3.4"}