{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"OnnxTR extracts and recognizes text from documents (PDFs, images, webpages) using ONNX-based deep learning models, localizing and identifying words without requiring PyTorch or TensorFlow.","skillfed_tags":["ocr","document-processing","onnx-inference"],"use_cases":["Extract text from scanned PDF documents for downstream NLP or search indexing","Batch process images on CPU-only servers without GPU infrastructure","Deploy OCR on edge devices or embedded systems with limited memory and compute","Detect and recognize text in multi-page documents with automatic line and block grouping","Export document structure as XML (hOCR) for archival or accessibility compliance"],"what_it_does":"OnnxTR is an ONNX-based wrapper around the docTR library that performs optical character recognition on documents. It detects and recognizes text by localizing individual words within PDFs, images, and webpages, then returns structured output with nested document hierarchy (pages, blocks, lines, words). Unlike the base docTR library, OnnxTR avoids PyTorch and TensorFlow dependencies, instead using ONNX Runtime for inference, which reduces package size and enables deployment on resource-constrained environments.\n\nThe package supports multiple execution backends: CPU, CUDA (NVIDIA GPUs), OpenVINO (Intel CPUs and GPUs), and CoreML (Apple Silicon). It offers 8-bit quantized models for faster CPU inference and lower memory footprint. Output can be exported as nested dictionaries (JSON-compatible), human-readable text, or hOCR XML format. Configuration is granular\u2014you can tune detection and recognition batch sizes, enable orientation and language detection, control page straightening, and adjust document parsing behavior.","worth_installing":"Yes, if you need OCR without PyTorch/TensorFlow overhead and can target Python 3.10+. The low install friction, active maintenance, permissive license, and support for multiple hardware backends (CPU, GPU, Intel, Apple Silicon) make it a practical choice for document text extraction. No known security vulnerabilities. Consider it especially for resource-constrained deployments or when ONNX Runtime is already in your stack."},"id":"onnxtr","links":{"html":"https://skillfed.io/packages/onnxtr","md":"https://skillfed.io/packages/onnxtr.md","pypi":"https://pypi.org/project/onnxtr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-04","license_spdx":null,"license_treatment":"permissive","name":"onnxtr","python_support":"supports_current","summary":"Onnx Text Recognition (OnnxTR): docTR Onnx-Wrapper for high-performance OCR on documents."},"popularity":{"monthly_downloads":102258,"position":12878,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.1"}