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| Model | Recognition Avg Accuracy(%) | Model Storage Size (M) | Introduction |
|---|---|---|---|
| arabic_PP-OCRv3_mobile_rec | 73.55 | 7.8 M | An ultra-lightweight Arabic alphabet recognition model trained based on the PP-OCRv3 recognition model, supporting Arabic alphabet and numeric character recognition. |
1# for CUDA11.8
2python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
3
4# for CUDA12.6
5python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
6
7# for CPU
8python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/python -m pip install paddleocr1paddleocr text_recognition \
2 --model_name arabic_PP-OCRv3_mobile_rec \
3 -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/EPkFpN_xkKTh1URMOsjE6.png1from paddleocr import TextRecognition
2model = TextRecognition(model_name="arabic_PP-OCRv3_mobile_rec")
3output = model.predict(input="EPkFpN_xkKTh1URMOsjE6.png", batch_size=1)
4for res in output:
5 res.print()
6 res.save_to_img(save_path="./output/")
7 res.save_to_json(save_path="./output/res.json"){'res': {'input_path': '/root/.paddlex/predict_input/EPkFpN_xkKTh1URMOsjE6.png', 'page_index': None, 'rec_text': 'ددعتم يبرع صن رابتخا ةلاح', 'rec_score': 0.9411801695823669}}1paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/Z1WF0PAh9lBnXZdOYE9QL.png \
2 --text_recognition_model_name arabic_PP-OCRv3_mobile_rec \
3 --use_doc_orientation_classify False \
4 --use_doc_unwarping False \
5 --use_textline_orientation True \
6 --save_path ./output \
7 --device gpu:0 1{'res': {'input_path': '/root/.paddlex/predict_input/Z1WF0PAh9lBnXZdOYE9QL.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': True, 'use_textline_orientation': True}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': False, 'use_doc_unwarping': False}, 'angle': -1}, 'dt_polys': array([[[ 6, 5],
2 ...,
3 [ 6, 38]],
4
5 [[199, 32],
6 ...,
7 [200, 68]]], dtype=int16), 'text_det_params': {'limit_side_len': 64, 'limit_type': 'min', 'thresh': 0.3, 'max_side_limit': 4000, 'box_thresh': 0.6, 'unclip_ratio': 1.5}, 'text_type': 'general', 'textline_orientation_angles': array([0, 0]), 'text_rec_score_thresh': 0.0, 'rec_texts': ['ددعتم يبرع صن رابتخاةلاح', 'رطسألا'], 'rec_scores': array([0.98472452, 0.99996692]), 'rec_polys': array([[[ 6, 5],
8 ...,
9 [ 6, 38]],
10
11 [[199, 32],
12 ...,
13 [200, 68]]], dtype=int16), 'rec_boxes': array([[ 6, ..., 38],
14 [199, ..., 68]], dtype=int16)}}1from paddleocr import PaddleOCR
2
3ocr = PaddleOCR(
4 text_recognition_model_name="arabic_PP-OCRv3_mobile_rec",
5 use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model
6 use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module
7 use_textline_orientation=True, # Use use_textline_orientation to enable/disable textline orientation classification model
8 device="gpu:0", # Use device to specify GPU for model inference
9)
10result = ocr.predict("https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/Z1WF0PAh9lBnXZdOYE9QL.png")
11for res in result:
12 res.print()
13 res.save_to_img("output")
14 res.save_to_json("output")PP-OCRv5_server_rec, so it is needed that specifing to arabic_PP-OCRv3_mobile_rec by argument text_recognition_model_name. And you can also use the local model file by argument text_recognition_model_dir. For details about usage command and descriptions of parameters, please refer to the Document.