官网

即用型 OCR，支持 80 多种语言和所有流行的书写脚本，包括：拉丁文、中文、阿拉伯文、梵文、西里尔文等。

英文 Github 官网 (opens new window)

演示文档,教程,API (opens new window)

安装

使用 pip

pip install easyocr

从 github 安装

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 (opens new window).

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 (opens new window) 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 (opens new window) and API Documentation (opens new window).

Run on command line

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

Train/use your own model

For recognition model, Read here (opens new window).

For detection model (CRAFT), Read here (opens new window).

Implementation Roadmap

• Handwritten support
• Restructure code to support swappable detection and recognition algorithms The api should be as easy as

reader = easyocr.Reader(['en'], detection='DB', recognition = 'Transformer')

The idea is to be able to plug in any state-of-the-art model into EasyOCR. There are a lot of geniuses trying to make better detection/recognition models, but we are not trying to be geniuses here. We just want to make their works quickly accessible to the public ... for free. (well, we believe most geniuses want their work to create a positive impact as fast/big as possible) The pipeline should be something like the below diagram. Grey slots are placeholders for changeable light blue modules.