easyocr
End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution
Decision gist · record as of 2026-08-14
Yes, if you need multilingual OCR and can accept the aging maintenance status. The library is production-stable, permissively licensed, and has low install friction. However, the last release was 689 days ago—verify compatibility with your PyTorch and Python versions before deploying to production, and monitor the repository for security updates or breaking changes in your dependencies.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- PyTorch and torchvision must be installed first; on Windows, follow official PyTorch instructions to select the correct CUDA version or CPU-only mode.
- Installation is straightforward via pip with low friction.
- The package is in production/stable status but aging—last release was 689 days ago.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2024-09-24 (689 days) · last repo commit 2025-12-05 · 29,907 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,852,280 downloads/mo, #2,477 on PyPI
Alternatives
Verify before relying
pip install easyocr
import easyocr
reader = easyocr.Reader(['en'])
result = reader.readtext('image.jpg')
print(result) # List of (bbox, text, confidence) tuples- Whether the package actively maintains compatibility with current PyTorch and torchvision versions beyond the last release date.
- Performance characteristics (speed, accuracy) across the 80+ supported languages and writing scripts.
- GPU memory requirements and whether CPU-only mode is practical for typical use cases.
What it is and what it does
EasyOCR is a ready-to-use optical character recognition library built on PyTorch that detects and recognizes text in images across 80+ languages and popular writing scripts including Latin, Chinese, Arabic, Devanagari, and Cyrillic. It combines CRAFT-based text detection with CRNN-based character recognition to extract text regions as bounding boxes with confidence scores.
The library is designed for accessibility—you instantiate a Reader with your target languages, pass an image (file path, numpy array, or URL), and receive structured output. Model weights download automatically. It supports both GPU and CPU inference, and offers a command-line interface alongside the Python API. The package depends on torch, torchvision, opencv-python-headless, scipy, numpy, Pillow, scikit-image, and several geometry/formatting utilities.
Use it for
- Extract text from multilingual documents or signs in images for data entry or indexing.
- Build document scanning workflows that preserve text location and confidence metadata.
- Process images with mixed scripts (e.g., English and Chinese) in a single pass.
- Run OCR on CPU-only systems or low-memory environments using the gpu=False flag.
- Integrate OCR into web services or batch pipelines via the Python API or CLI.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need multilingual OCR and can accept the aging maintenance status.
The library is production-stable, permissively licensed, and has low install friction. However, the last release was 689 days ago—verify compatibility with your PyTorch and Python versions before deploying to production, and monitor the repository for security updates or breaking changes in your dependencies.
Install
easyocr on PyPI
Before you install
Installation is straightforward via pip with low friction. The package is in production/stable status but aging—last release was 689 days ago. The 12 runtime dependencies include torch, torchvision, and opencv-python-headless, which are substantial but standard for deep-learning OCR work.
PyTorch and torchvision must be installed first; on Windows, follow official PyTorch instructions to select the correct CUDA version or CPU-only mode.
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install easyocr
import easyocr
reader = easyocr.Reader(['en'])
result = reader.readtext('image.jpg')
print(result) # List of (bbox, text, confidence) tuplesVerify before relying
- Whether the package actively maintains compatibility with current PyTorch and torchvision versions beyond the last release date.
- Performance characteristics (speed, accuracy) across the 80+ supported languages and writing scripts.
- GPU memory requirements and whether CPU-only mode is practical for typical use cases.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagestorchtorchvisionopencv-python-headlessscipynumpyPillowscikit-imagepython-bidiPyYAMLShapelypyclipperninja |
| Maintenance | Aging 689 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,852,280 / month, #2,477 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/Stable |
Evidence: easyocr-1.7.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “ocr multi-language text extraction”
- easyocrEasyOCR performs optical character recognition on images across 80+…
- paddlexPaddleX is a low-code framework for training, inference, and…
- python-doctrpython-doctr extracts and localizes text from documents and images…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also keras-ocr · rapidocr-onnxruntime · cnocr · ocrmac · python-doctr · pyocr · amazon-textract-textractor · rapidocr · ddddocr · onnxtr