easyocr

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

EasyOCR performs optical character recognition on images across 80+ languages and writing scripts, extracting text with bounding boxes and confidence scores.

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

pip install easyocr

uv add easyocr

poetry add easyocr

Installing easyocr

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.

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) tuples

PyTorch and torchvision must be installed first; on Windows, follow official PyTorch instructions to select the correct CUDA version or CPU-only mode.

Verify 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

Evidence: easyocr-1.7.2-py3-none-any.whl

Keywords: ocr, optical, character, recognition, deep, learning, neural, network

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