rapidocr/python/README.md

运行

1. 下载相应模型和用于显示的字体文件
   • 提取码：30jv
   • 下载之后放在models目录即可
2. 运行rapidOCR.py文件, 即可

   cd python
   python rapidOCR.py
   

文本检测+方向分类+文本识别

from ch_ppocr_mobile_v2_cls import TextClassifier
from ch_ppocr_mobile_v2_det import TextDetector
from ch_ppocr_mobile_v2_rec import TextRecognizer

det_model_path = 'models/ch_ppocr_mobile_v2.0_det_infer.onnx'
cls_model_path = 'models/ch_ppocr_mobile_v2.0_cls_infer.onnx'
rec_model_path = 'models/ch_ppocr_mobile_v2.0_rec_infer.onnx'

image_path = r'test_images/det_images/1.jpg'

text_sys = TextSystem(det_model_path,
                        rec_model_path,
                        use_angle_cls=True,
                        cls_model_path=cls_model_path)
dt_boxes, rec_res = text_sys(image_path)
visualize(image_path, dt_boxes, rec_res)

onnxruntime-gpu版推理配置

1. 安装GPU版的onnxruntime: pip install onnxruntime-gpu
2. 推理代码中，加载onnx模型部分，用以下对应语言代码替换即可
   • python版本

     import onnxruntime as ort
     
     model_path = '<path to model>'
     
     providers = [
        ('CUDAExecutionProvider', {
           'device_id': 0,
           'arena_extend_strategy': 'kNextPowerOfTwo',
           'gpu_mem_limit': 2 * 1024 * 1024 * 1024,
           'cudnn_conv_algo_search': 'EXHAUSTIVE',
           'do_copy_in_default_stream': True,
        }),
        'CPUExecutionProvider',
     ]
     
     session = ort.InferenceSession(model_path, providers=providers)
     

   • C/C++版本

     OrtSessionOptions* session_options = /* ... */;
     
     OrtCUDAProviderOptions options;
     options.device_id = 0;
     options.arena_extend_strategy = 0;
     options.gpu_mem_limit = 2 * 1024 * 1024 * 1024;
     options.cudnn_conv_algo_search = OrtCudnnConvAlgoSearch::EXHAUSTIVE;
     options.do_copy_in_default_stream = 1;
     
     SessionOptionsAppendExecutionProvider_CUDA(session_options, &options);