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Easyocr

EasyOCR is an open-source Python library that detects and recognizes text in images across more than 80 languages and multiple writing systems, including Latin, Chinese, Arabic, Devanagari and Cyrillic.

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Independent OpenViST project profile, based on linked sources.

Easyocr — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use EasyOCR when an application needs a ready-to-use, self-hosted Python OCR component for multilingual image inputs and can manage its PyTorch-based model setup.

Who it is for

  • Python developers building image-text extraction workflows.
  • Teams processing multilingual documents, receipts, invoices, forms or photographs.
  • Operators creating self-hosted OCR or searchable image archives.

Documented uses

  • Extract text from image files and raw image inputs.
  • Return detected text with bounding boxes and confidence levels.
  • Process several compatible languages through one reader.
  • Use CPU-only processing when a suitable GPU is unavailable.
  • Adapt recognition or detection models through the project’s model guidance.
Before you choose

Requirements & limits

The repository is public and identifies an Apache-2.0 license. Installation is documented through pip, with a development installation available from the Git repository.

  • Language selection matters because not all language combinations can be used together.
  • Detailed output includes bounding boxes and confidence levels; detail=0 provides text strings only.
  • PyTorch and torchvision setup is an explicit prerequisite on Windows.
  • CPU-only mode is supported through gpu=False, while the source also documents CUDA configuration for Windows.
  • Model weights are downloaded automatically or can be placed manually in ~/.EasyOCR/model.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install EasyOCR and prepare a multilingual reader

Set up the Python library, select compatible languages and load the OCR model into memory.

Before you start

  • Python environment with pip.
  • For Windows, install PyTorch and torchvision first and choose the applicable CUDA setting; CPU-only use is supported.
  • An input image filepath, OpenCV image object, NumPy array, image bytes or raw-image URL.
  • Language codes for the text to recognize.

Steps

  1. Install the stable release with pip, or install the development release from the Git repository.

  2. Import EasyOCR and create a Reader with the selected language codes, such as Chinese Simplified and English.

  3. Allow the selected model weights to download automatically, or place manually downloaded weights in the documented ~/.EasyOCR/model directory.

  4. Pass the image to readtext; use detail=0 when only recognized strings are required, or retain the default detailed output for bounding boxes, text and confidence levels.

Expected outcome

A configured reader can return recognized text, locations and confidence values, or a simpler list of strings.

Guide limitations

  • Language combinations are not universally compatible; English is compatible with every language and shared-character combinations are generally compatible.
  • Reader initialization loads the model into memory and takes time, although the reader can then be reused.
  • CPU-only processing is available, but the source does not specify performance expectations.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
1 October 2026
Original website title
JaidedAI/EasyOCR · Official homepage title · retained in its source language
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