{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Runs inference on layout-parsing and document-analysis models to extract structured elements (text, tables, regions) from PDFs, images, and other document formats.","skillfed_tags":["document-parsing","layout-detection","ocr"],"use_cases":["Extract structured text and table regions from PDF documents for downstream NLP or data-pipeline processing.","Detect and classify layout elements (headers, paragraphs, tables, images) in scanned or digital documents.","Integrate custom detection models by wrapping them in the UnstructuredObjectDetectionModel interface.","Preprocess documents for machine-learning pipelines that require clean, labeled element boundaries.","Build document-parsing workflows that combine layout detection with OCR for mixed-format inputs."],"what_it_does":"unstructured-inference provides hosted model inference for document layout analysis and element extraction. It wraps detection models (Detectron2, YOLOX) and OCR pipelines to identify and extract text regions, tables, and structural elements from PDFs and images. The core workflow detects layout regions on a page, then extracts their contents via direct text extraction, OCR, or table-specific inference.\n\nThe package is designed as a backend for the broader unstructured ecosystem\u2014it handles the model inference layer while the unstructured package orchestrates partitioning and higher-level document processing. It requires modern Python (3.11\u20133.13) and a substantial dependency stack including torch, transformers, and onnxruntime, reflecting its reliance on deep-learning models for detection and OCR.","worth_installing":"Yes, if you need to extract structured elements from documents and can meet the Python version and dependency requirements. The package is actively maintained, has no known vulnerabilities, and offers a clean API for layout-based document analysis. Install only if you accept the large dependency footprint (torch, transformers, onnxruntime) and can handle Detectron2's platform constraints\u2014particularly on Windows, where it lacks official support."},"id":"unstructured-inference","links":{"html":"https://skillfed.io/packages/unstructured-inference","md":"https://skillfed.io/packages/unstructured-inference.md","pypi":"https://pypi.org/project/unstructured-inference/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-11","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"unstructured-inference","python_support":"supports_current","summary":"A library for performing inference using trained models."},"popularity":{"monthly_downloads":1158796,"position":4288,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.6.13"}