JaidedAI/EasyOCR 源码下载与部署教程

项目概述

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

项目地址

https://github.com/JaidedAI/EasyOCR

项目页面预览

关键指标

• Stars：28732
• 主要语言：Python
• License：Apache License 2.0
• 最近更新：2025-12-05T12:23:12Z
• 默认分支：master

本站高速下载（国内可用）

点击下载（本站镜像）
– SHA256：fd26019aff3ae7291fbdbbf0c10f7e29fc91dbe3d3cf8cd5c0c7b41c09c69a6b

安装部署要点（README 精选）

Installation

Install using pip

For the latest stable release:

pip install easyocr

For the latest development release:

pip install git+https://github.com/JaidedAI/EasyOCR.git

Note 1: For Windows, please install torch and torchvision first by following the official instructions here https://pytorch.org. On the pytorch website, be sure to select the right CUDA version you have. If you intend to run on CPU mode only, select CUDA = None.

Note 2: We also provide a Dockerfile here.

Usage

import easyocr
reader = easyocr.Reader(['ch_sim','en']) # this needs to run only once to load the model into memory
result = reader.readtext('chinese.jpg')

The output will be in a list format, each item represents a bounding box, the text detected and confident level, respectively.

[([[189, 75], [469, 75], [469, 165], [189, 165]], '愚园路', 0.3754989504814148),
 ([[86, 80], [134, 80], [134, 128], [86, 128]], '西', 0.40452659130096436),
 ([[517, 81], [565, 81], [565, 123], [517, 123]], '东', 0.9989598989486694),
 ([[78, 126], [136, 126], [136, 156], [78, 156]], '315', 0.8125889301300049),
 ([[514, 126], [574, 126], [574, 156], [514, 156]], '309', 0.4971577227115631),
 ([[226, 170], [414, 170], [414, 220], [226, 220]], 'Yuyuan Rd.', 0.8261902332305908),
 ([[79, 173], [125, 173], [125, 213], [79, 213]], 'W', 0.9848111271858215),
 ([[529, 173], [569, 173], [569, 213], [529, 213]], 'E', 0.8405593633651733)]

Note 1: ['ch_sim','en'] is the list of languages you want to read. You can pass
several languages at once but not all languages can be used together.
English is compatible with every language and languages that share common characters are usually compatible with each other.

Note 2: Instead of the filepath chinese.jpg, you can also pass an OpenCV image object (numpy array) or an image file as bytes. A URL to a raw image is also acceptable.

Note 3: The line reader = easyocr.Reader(['ch_sim','en']) is for loading a model into memory. It takes some time but it needs to be run only once.

You can also set detail=0 for simpler output.

reader.readtext('chinese.jpg', detail = 0)

Result:

['愚园路', '西', '东', '315', '309', 'Yuyuan Rd.', 'W', 'E']

Model weights for the chosen language will be automatically downloaded or you can
download them manually from the model hub and put them in the ‘~/.EasyOCR/model’ folder

In case you do not have a GPU, or your GPU has low memory, you can run the model in CPU-only mode by adding gpu=False.

reader = easyocr.Reader(['ch_sim','en'], gpu=False)

For more information, read the tutorial and API Documentation.

Run on command line

$ easyocr -l ch_sim en -f chinese.jpg --detail=1 --gpu=True

常用命令（从 README 提取）

$ easyocr -l ch_sim en -f chinese.jpg --detail=1 --gpu=True

通用部署说明

1. 下载源码并阅读 README
2. 安装依赖（pip/npm/yarn 等）
3. 配置环境变量（API Key、模型路径、数据库等）
4. 启动服务并测试访问
5. 上线建议：Nginx 反代 + HTTPS + 进程守护（systemd / pm2）

免责声明与版权说明

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