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easyocr

Python OCR library for text detection and recognition in 80+ languages

Stars
30.1k
License
Apache-2.0
Last commit
Dec 2025
Last release
Sep 2024
Language
Python
Stars, last 29 weeks+1,138
25 Mar 2026: 28,927 stars1 Apr 2026: 28,991 stars8 Apr 2026: 29,046 stars15 Apr 2026: 29,103 stars22 Apr 2026: 29,144 stars29 Apr 2026: 29,184 stars6 May 2026: 29,229 stars13 May 2026: 29,272 stars20 May 2026: 29,325 stars27 May 2026: 29,372 stars3 Jun 2026: 29,407 stars10 Jun 2026: 29,461 stars17 Jun 2026: 29,493 stars24 Jun 2026: 29,548 stars1 Jul 2026: 29,593 stars8 Jul 2026: 29,628 stars15 Jul 2026: 29,671 stars22 Jul 2026: 29,713 stars29 Jul 2026: 29,747 stars5 Aug 2026: 29,790 stars12 Aug 2026: 29,826 stars19 Aug 2026: 29,853 stars26 Aug 2026: 29,881 stars2 Sept 2026: 29,908 stars9 Sept 2026: 29,936 stars16 Sept 2026: 29,954 stars23 Sept 2026: 29,986 stars30 Sept 2026: 30,011 stars7 Oct 2026: 30,035 stars14 Oct 2026: 30,065 stars

28,927 on 25 Mar 202630,065 on 14 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

EasyOCR is a Python package that detects text regions with the CRAFT model and reads them with a CRNN recognizer, built on PyTorch. It returns bounding boxes, text and confidence scores from file paths, URLs, bytes or numpy arrays, and also has a CLI. Model weights download automatically per language, and it runs on GPU or CPU.

Who it is for: Python developers adding OCR to apps or document pipelines

Strengths

  • Two-line Python API returns boxes, text and confidence from paths, URLs, bytes or arrays
  • 80+ languages across Latin, Chinese, Arabic, Devanagari and Cyrillic scripts
  • CPU-only mode via gpu=False; also ships a command-line tool
  • Documents custom recognition and CRAFT detection model training

Weaknesses

  • Not every language combination works together in one Reader; English works with all
  • Handwritten text support is listed as upcoming, not available
  • Last release 1.7.2 was September 2024
  • Issues older than 6 months are auto-closed; maintainers cite limited resources

What it needs

  • GPU optional
  • Docker
  • Needs PyTorch, torchvision
  • Models: CRAFT detection, CRNN recognition (ResNet/VGG, LSTM, CTC)

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