标签: automatic-license-plate-recognition

在使用过程中pip install tf-models-official，我在安装库时发现了以下问题：-

Collecting tf-models-official\n  Using cached tf_models_official-2.8.0-py2.py3-none-any.whl (2.2 MB)\nRequirement already satisfied: Pillow in c:\\users\\user\\documents\\python scripts\\number_plate_recognition\\anprsys\\lib\\site-packages (from tf-models-official) (9.0.1)\nCollecting gin-config\n  Using cached gin_config-0.5.0-py3-none-any.whl (61 kB)\nCollecting seqeval\n  Using cached seqeval-1.2.2-py3-none-any.whl\nRequirement already satisfied: tensorflow~=2.8.0 in c:\\users\\user\\documents\\python scripts\\number_plate_recognition\\anprsys\\lib\\site-packages (from tf-models-official) (2.8.0)\nCollecting tensorflow-datasets\n  Using cached tensorflow_datasets-4.5.2-py3-none-any.whl (4.2 MB)\nRequirement already satisfied: six in c:\\users\\user\\documents\\python scripts\\number_plate_recognition\\anprsys\\lib\\site-packages (from tf-models-official) (1.16.0)\nRequirement already satisfied: scipy>=0.19.1 in c:\\users\\user\\documents\\python scripts\\number_plate_recognition\\anprsys\\lib\\site-packages (from tf-models-official) (1.8.0)\nCollecting pandas>=0.22.0\n  Using cached pandas-1.4.1-cp310-cp310-win_amd64.whl (10.6 MB)\nCollecting py-cpuinfo>=3.3.0\n  Using cached py_cpuinfo-8.0.0-py3-none-any.whl\nCollecting google-api-python-client>=1.6.7\n  Downloading google_api_python_client-2.42.0-py2.py3-none-any.whl (8.3 MB)\n …

我想识别NumberPlate的字符。如何在Ubuntu 16.04中为相应的号码牌训练tesseract-ocr。由于我不熟悉培训。请帮助我创建一个“ traineddata”文件以识别车牌。

我有1000张车牌图像。

请调查一下。任何帮助将不胜感激。

所以我尝试了以下命令

tesseract [langname].[fontname].[expN].[file-extension] [langname].[fontname].[expN] batch.nochop makebox

tesseract eng.arial.plate3655.png eng.arial.plate3655 batch.nochop makebox

但这会带来错误。

Tesseract Open Source OCR Engine v4.1.0-rc1-56-g7fbd with Leptonica
Error, cannot read input file eng.arial.plate3655.png: No such file or directory
Error during processing.

之后，我尝试了

tesseract plate4.png eng.arial.plate4 batch.nochop makebox

它有效，但在某些板块中。现在在步骤2中。

屏幕截图已随附。

板4图像进行训练

步骤1和终端上的Ste p2显示

步骤1和步骤2之后生成的文件

步骤1和步骤2之后生成的文件内容