All Projects → bigppwong → idcardocr

bigppwong / idcardocr

Licence: GPL-3.0 License
离线环境下第二代居民身份证信息识别

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第二代身份证信息识别

可识别身份证上所有信息:姓名,性别,民族,出生日期,住址,身份证号码。提供Docker镜像部署方式

  • 2018/8/21 更新,应该较大提升了准确率
  • 2018/11/8 更新,优化算法,更新至tesseract4.0, 单张图片识别时间降低到3s以下

依赖:

本项目在Ubuntu 18.10基于tesseract 4.0 rc3,OpenCV2; 使用Python3.6进行开发
apt依赖安装:
sudo apt install python3 python3-pip tesseract-ocr tesseract-ocr-chi-sim tzdata libsm6 libxext6 python3-tk -y

Python依赖安装:
sudo pip3 install -r idcardocr/requirements.txt

tessdata配置:
sudo cp tessdata/* /usr/share/tesseract-ocr/tessdata

使用方法:

识别本地图片
import idcard_recognize;print idcard_recognize.process('testimages/3.jpg')

http_server远程接收图片
python3 idcard_recognize.py
默认监听端口为8080

Docker运行http_server:
docker pull raymondwong/idcardocr;docker run -d -p 8080:8080 raymondwong/idcardocr

测试:

使用curl向服务器发送图片:
curl --request POST \ --url http://127.0.0.1:8080 \ --header 'content-type: multipart/form-data; boundary=----WebKitFormBoundary7MA4YWxkTrZu0gW' \ --form 'pic=@./testimages/3.jpg'

使用Postman:
avatar

性能

平台: I5 8259u + 16g macOS 13.14 关闭OpenCL
处理单张图片时间在2.5秒左右(单张图片只能使用单核心)
处理4张图片时间也是4秒左右(4核心)
关于OPENCL: 开启并不会使单张图片处理速度加快,但是能让你在同时间处理更多图片(譬如I5 6500每秒能处理4张图片,开启OPENCL后每秒能处理6张图片)
开启OPENCL: 默认关闭,可以自行修改idcard_recognize.http_server中的cv2.ocl.setUseOpenCL(False)开启

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