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- 注意：两个包接口一致，只是推理引擎不同而已
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2. 推理使用
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- 📌初始化RapidOCR可不提供`config.yaml`，默认使用**rapidocr_onnxruntime**目录下的。如有特殊需求，可以自行更改目录下的`config.yaml`。
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```python
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import cv2
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from rapidocr_onnxruntime import RapidOCR
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# from rapidocr_openvino import RapidOCR
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- 脚本使用：
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- 📌初始化RapidOCR可不提供`config.yaml`，默认使用**rapidocr_onnxruntime**目录下的。如有自定义需求，可直接通过初始化参数传入。详情参数参考命令行部分，和`config.yaml`基本对应。
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- 输入：`Union[str, np.ndarray, bytes, Path]`
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- 输出：`[[文本框坐标], 文本内容, 置信度]`, 为空：`(None, None)`
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- 示例：
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```python
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import cv2
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from rapidocr_onnxruntime import RapidOCR
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# from rapidocr_openvino import RapidOCR
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# RapidOCR可传入参数参考下面的命令行部分
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rapid_ocr = RapidOCR()
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# RapidOCR可传入参数参考下面的命令行部分
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rapid_ocr = RapidOCR()
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img_path = 'tests/test_files/ch_en_num.jpg'
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img_path = 'tests/test_files/ch_en_num.jpg'
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# 支持四种格式的输入：Union[str, np.ndarray, bytes, Path]
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# str
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result, elapse = rapid_ocr(img_path)
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# 输入格式一：str
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result, elapse = rapid_ocr(img_path)
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# np.ndarray
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img = cv2.imread('tests/test_files/ch_en_num.jpg')
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result, elapse = rapid_ocr(img)
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# 输入格式二：np.ndarray
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img = cv2.imread('tests/test_files/ch_en_num.jpg')
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result, elapse = rapid_ocr(img)
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# bytes
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with open(img_path, 'rb') as f:
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result, elapse = rapid_ocr(f.read())
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# 输入格式三：bytes
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with open(img_path, 'rb') as f:
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img = f.read()
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result, elapse = rapid_ocr(img)
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# Path
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result, elapse = rapid_ocr(Path(img_path))
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print(result)
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# 输入格式四：Path
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result, elapse = rapid_ocr(Path(img_path))
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print(result)
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# result: [[文本框坐标], 文本内容, 置信度]
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# 示例：[[左上, 右上, 右下, 左下], '小明', '0.99']
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# result: [[文本框坐标], 文本内容, 置信度]
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# 示例：[[左上, 右上, 右下, 左下], '小明', '0.99']
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# elapse: [det_elapse, cls_elapse, rec_elapse]
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# all_elapse = det_elapse + cls_elapse + rec_elapse
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# elapse: [det_elapse, cls_elapse, rec_elapse]
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# all_elapse = det_elapse + cls_elapse + rec_elapse
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# 如果没有有效文本，则result: (None, None)
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```
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# 如果没有有效文本，则result: (None, None)
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```
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- 命令行使用：
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```bash
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$ rapidocr_onnxruntime -h
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usage: rapidocr_onnxruntime [-h] -img IMG_PATH [-p] [--text_score TEXT_SCORE]
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[--use_angle_cls USE_ANGLE_CLS]
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[--use_text_det USE_TEXT_DET]
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[--print_verbose PRINT_VERBOSE]
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[--min_height MIN_HEIGHT]
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[--width_height_ratio WIDTH_HEIGHT_RATIO]
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[--det_model_path DET_MODEL_PATH]
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[--det_limit_side_len DET_LIMIT_SIDE_LEN]
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[--det_limit_type {max,min}]
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[--det_thresh DET_THRESH]
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[--det_box_thresh DET_BOX_THRESH]
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[--det_unclip_ratio DET_UNCLIP_RATIO]
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[--det_use_dilation DET_USE_DILATION]
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[--det_score_mode {slow,fast}]
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[--cls_model_path CLS_MODEL_PATH]
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[--cls_image_shape CLS_IMAGE_SHAPE]
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[--cls_label_list CLS_LABEL_LIST]
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[--cls_batch_num CLS_BATCH_NUM]
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[--cls_thresh CLS_THRESH]
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[--rec_model_path REC_MODEL_PATH]
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[--rec_image_shape REC_IMAGE_SHAPE]
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[--rec_batch_num REC_BATCH_NUM]
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```bash
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$ rapidocr_onnxruntime -h
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usage: rapidocr_onnxruntime [-h] -img IMG_PATH [-p] [--text_score TEXT_SCORE]
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[--use_angle_cls USE_ANGLE_CLS]
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[--use_text_det USE_TEXT_DET]
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[--print_verbose PRINT_VERBOSE]
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[--min_height MIN_HEIGHT]
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[--width_height_ratio WIDTH_HEIGHT_RATIO]
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[--det_model_path DET_MODEL_PATH]
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[--det_limit_side_len DET_LIMIT_SIDE_LEN]
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[--det_limit_type {max,min}]
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[--det_thresh DET_THRESH]
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[--det_box_thresh DET_BOX_THRESH]
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[--det_unclip_ratio DET_UNCLIP_RATIO]
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[--det_use_dilation DET_USE_DILATION]
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[--det_score_mode {slow,fast}]
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[--cls_model_path CLS_MODEL_PATH]
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[--cls_image_shape CLS_IMAGE_SHAPE]
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[--cls_label_list CLS_LABEL_LIST]
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[--cls_batch_num CLS_BATCH_NUM]
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[--cls_thresh CLS_THRESH]
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[--rec_model_path REC_MODEL_PATH]
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[--rec_image_shape REC_IMAGE_SHAPE]
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[--rec_batch_num REC_BATCH_NUM]
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optional arguments:
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-h, --help show this help message and exit
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-img IMG_PATH, --img_path IMG_PATH MUST
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-p, --print_cost
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optional arguments:
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-h, --help show this help message and exit
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-img IMG_PATH, --img_path IMG_PATH MUST
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-p, --print_cost
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Global:
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--text_score TEXT_SCORE
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--use_angle_cls USE_ANGLE_CLS
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--use_text_det USE_TEXT_DET
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--print_verbose PRINT_VERBOSE
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--min_height MIN_HEIGHT
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--width_height_ratio WIDTH_HEIGHT_RATIO
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Global:
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--text_score TEXT_SCORE
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--use_angle_cls USE_ANGLE_CLS
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--use_text_det USE_TEXT_DET
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--print_verbose PRINT_VERBOSE
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--min_height MIN_HEIGHT
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--width_height_ratio WIDTH_HEIGHT_RATIO
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Det:
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--det_model_path DET_MODEL_PATH
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--det_limit_side_len DET_LIMIT_SIDE_LEN
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--det_limit_type {max,min}
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--det_thresh DET_THRESH
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--det_box_thresh DET_BOX_THRESH
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--det_unclip_ratio DET_UNCLIP_RATIO
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--det_use_dilation DET_USE_DILATION
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--det_score_mode {slow,fast}
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Det:
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--det_model_path DET_MODEL_PATH
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--det_limit_side_len DET_LIMIT_SIDE_LEN
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--det_limit_type {max,min}
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--det_thresh DET_THRESH
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--det_box_thresh DET_BOX_THRESH
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--det_unclip_ratio DET_UNCLIP_RATIO
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--det_use_dilation DET_USE_DILATION
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--det_score_mode {slow,fast}
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Cls:
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--cls_model_path CLS_MODEL_PATH
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--cls_image_shape CLS_IMAGE_SHAPE
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--cls_label_list CLS_LABEL_LIST
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--cls_batch_num CLS_BATCH_NUM
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--cls_thresh CLS_THRESH
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Cls:
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--cls_model_path CLS_MODEL_PATH
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--cls_image_shape CLS_IMAGE_SHAPE
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--cls_label_list CLS_LABEL_LIST
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--cls_batch_num CLS_BATCH_NUM
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--cls_thresh CLS_THRESH
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Rec:
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--rec_model_path REC_MODEL_PATH
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--rec_image_shape REC_IMAGE_SHAPE
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--rec_batch_num REC_BATCH_NUM
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Rec:
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--rec_model_path REC_MODEL_PATH
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--rec_image_shape REC_IMAGE_SHAPE
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--rec_batch_num REC_BATCH_NUM
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$ rapidocr_onnxruntime -img tests/test_files/ch_en_num.jpg
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```
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$ rapidocr_onnxruntime -img tests/test_files/ch_en_num.jpg
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```
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### 源码使用步骤
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1. 下载整个项目到本地
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@@ -206,13 +210,11 @@
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- 基于onnxruntime推理所需环境安装：
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```bash
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pip install onnxruntime>=1.7.0
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pip install -r requirements.txt
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```
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- 基于openvino推理所需环境安装：
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```bash
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pip install openvino
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pip install -r requirements.txt
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```
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- Note: 在Windows端，Shapely库自动安装可能会有问题，解决方案参见[Q15](https://github.com/RapidAI/RapidOCR/blob/main/docs/FAQ.md#q-windows系统下装完环境之后运行示例程序之后报错oserror-winerror-126-找不到指定的模組)
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