{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"python-doctr extracts and localizes text from documents and images using a two-stage deep learning pipeline: text detection (finding word locations) followed by text recognition (identifying characters), with support for PDFs, images, and rotated pages.","skillfed_tags":["ocr","document-processing","computer-vision"],"use_cases":["Extract text and bounding boxes from scanned invoices, receipts, or forms for downstream processing.","Build a document search index by OCR-ing a large PDF archive and exporting structured text.","Detect and extract specific fields (dates, addresses) from documents using the KIE predictor with a custom multi-class detector.","Automate data entry workflows by recognizing and localizing text in handwritten or printed documents.","Validate or correct OCR output by visualizing detected text regions and character predictions interactively."],"what_it_does":"python-doctr is a PyTorch-based OCR library that combines text detection and recognition models to extract structured text from documents. It reads PDFs and images, localizes each word, recognizes its characters, and returns a nested document structure (Page, Block, Line, Word) that can be exported as JSON or visualized. The library offers multiple architectures for both detection (DBNet, LinkNet, FAST) and recognition (CRNN, SAR, MASTER, ViTSTR, PARSeq, VIPTR), letting you choose speed versus accuracy trade-offs.\n\nThe package handles rotated pages and multi-orientation text through configurable options, supports a KIE (Key Information Extraction) predictor for multi-class detection, and includes utilities for document synthesis and interactive result visualization. It integrates with Hugging Face Hub for model distribution and includes a Streamlit demo app for local testing.","worth_installing":"Yes, if you need production-grade OCR with flexible model selection and don't mind the weight of torch and torchvision. The library is actively maintained, permissively licensed, and well-documented. Install only if you have a genuine OCR task; the dependency footprint is substantial but justified for deep learning work."},"id":"python-doctr","links":{"html":"https://skillfed.io/packages/python-doctr","md":"https://skillfed.io/packages/python-doctr.md","pypi":"https://pypi.org/project/python-doctr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-04","license_spdx":null,"license_treatment":"permissive","name":"python-doctr","python_support":"supports_current","summary":"Document Text Recognition (docTR): deep Learning for high-performance OCR on documents."},"popularity":{"monthly_downloads":340733,"position":7410,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.1"}