onnxtr
Onnx Text Recognition (OnnxTR): docTR Onnx-Wrapper for high-performance OCR on documents.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or higher.
- GPU support requires CUDA and cuDNN to be pre-installed separately.
- Low install friction; pure Python wheel with 11 runtime dependencies.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 (permissive): you can use, modify, and distribute OnnxTR freely in commercial and private projects, provided you include the license and attribute the original work.
last release 2026-02-04 (191 days) · last repo commit 2026-07-28 · 193 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 102,258 downloads/mo, #12,878 on PyPI
Alternatives
Verify before relying
pip install "onnxtr[cpu]"
from onnxtr.io import DocumentFile
from onnxtr.models import ocr_predictor
model = ocr_predictor(det_arch='fast_base', reco_arch='vitstr_base')
doc = DocumentFile.from_pdf("path/to/doc.pdf")
result = model(doc)
json_output = result.export()- Accuracy comparison with PyTorch/TensorFlow-based OCR pipelines on standard benchmarks
- Performance metrics (inference latency, memory usage) for 8-bit quantized models on typical hardware
- Whether weasyprint is required for webpage parsing or optional
What it is and 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.
The 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—you can tune detection and recognition batch sizes, enable orientation and language detection, control page straightening, and adjust document parsing behavior.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
onnxtr on PyPI
Before you install
Low install friction; pure Python wheel with 11 runtime dependencies. Actively maintained with recent commits. Requires Python 3.10 or higher. Optional extras for GPU (CUDA), Intel (OpenVINO), and visualization support are available.
Requires Python 3.10 or higher. GPU support requires CUDA and cuDNN to be pre-installed separately.
License in practice
Apache License 2.0 (permissive): you can use, modify, and distribute OnnxTR freely in commercial and private projects, provided you include the license and attribute the original work.
Quickstart
pip install "onnxtr[cpu]"
from onnxtr.io import DocumentFile
from onnxtr.models import ocr_predictor
model = ocr_predictor(det_arch='fast_base', reco_arch='vitstr_base')
doc = DocumentFile.from_pdf("path/to/doc.pdf")
result = model(doc)
json_output = result.export()Verify before relying
- Accuracy comparison with PyTorch/TensorFlow-based OCR pipelines on standard benchmarks
- Performance metrics (inference latency, memory usage) for 8-bit quantized models on typical hardware
- Whether weasyprint is required for webpage parsing or optional
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release <4,>=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesnumpyscipypypdfium2pyclipperrapidfuzzlangdetecthuggingface-hubPillowdefusedxmlanyasciitqdm |
| Maintenance | Actively maintained 191 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 102,258 / month, #12,878 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: onnxtr-0.8.1-py3-none-any.whl
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