--- title: en_PP-OCRv3_mobile_rec canonical_url: "https://www.modelscope.cn/models/PaddlePaddle/en_PP-OCRv3_mobile_rec" md_url: "https://www.modelscope.cn/models/PaddlePaddle/en_PP-OCRv3_mobile_rec.md" repository: PaddlePaddle/en_PP-OCRv3_mobile_rec last_updated: 2026-06-12 license: apache-2.0 language: - en - zh downloads: 465 stars: 0 tags: - OCR - PaddlePaddle - PaddleOCR - textline_recognition --- # en_PP-OCRv3_mobile_rec > en_PP-OCRv3_mobile_rec - PaddlePaddle 在 ModelScope 开源的模型。enPP-OCRv3mobilerec is a text line recognition model within the PP-OCRv3rec series, developed by the PaddleOCR team. The enPP-OCRv3mobilerec model is an English-specific model trained based on… PaddlePaddle/en_PP-OCRv3_mobile_rec 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。 - **Repository**: PaddlePaddle/en_PP-OCRv3_mobile_rec - **License**: apache-2.0 - **Tags**: OCR, PaddlePaddle, PaddleOCR, textline_recognition - **Downloads**: 465 - **Stars**: 0 - **Last updated**: 2026-06-12 Source: https://www.modelscope.cn/models/PaddlePaddle/en_PP-OCRv3_mobile_rec --- # en_PP-OCRv3_mobile_rec ## Introduction en_PP-OCRv3_mobile_rec is a text line recognition model within the PP-OCRv3_rec series, developed by the PaddleOCR team. The en_PP-OCRv3_mobile_rec model is an English-specific model trained based on PP-OCRv3_mobile_rec, and it supports English recognition. The key accuracy metrics are as follow:
Model Recognition Avg Accuracy(%) Model Storage Size (M) Introduction
en_PP-OCRv3_mobile_rec 70.69 7.8 M An ultra-lightweight English recognition model trained based on the PP-OCRv3 recognition model, supporting English and numeric character recognition.
**Note**: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications. ## Quick Start ### Installation 1. PaddlePaddle Please refer to the following commands to install PaddlePaddle using pip: ```bash # for CUDA11.8 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # for CUDA12.6 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # for CPU python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). 2. PaddleOCR Install the latest version of the PaddleOCR inference package from PyPI: ```bash python -m pip install paddleocr ``` ### Model Usage You can quickly experience the functionality with a single command: ```bash paddleocr text_recognition \ --model_name en_PP-OCRv3_mobile_rec \ -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/QmaPtftqwOgCtx0AIvU2z.png ``` You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine. ```python from paddleocr import TextRecognition model = TextRecognition(model_name="en_PP-OCRv3_mobile_rec") output = model.predict(input="QmaPtftqwOgCtx0AIvU2z.png", batch_size=1) for res in output: res.print() res.save_to_img(save_path="./output/") res.save_to_json(save_path="./output/res.json") ``` After running, the obtained result is as follows: ```json {'res': {'input_path': '/root/.paddlex/predict_input/QmaPtftqwOgCtx0AIvU2z.png', 'page_index': None, 'rec_text': 'the number of model parameters and FLOPs get larger, it', 'rec_score': 0.990352988243103}} ``` The visualized image is as follows: ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/aT_U3ssYetaz4MiYfIwfZ.png) For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/text_recognition.html#iii-quick-start). ### Pipeline Usage The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios. #### PP-OCRv3 The general OCR pipeline is used to solve text recognition tasks by extracting text information from images and outputting it in string format. And there are 5 modules in the pipeline: * Document Image Orientation Classification Module (Optional) * Text Image Unwarping Module (Optional) * Text Line Orientation Classification Module (Optional) * Text Detection Module * Text Recognition Module Run a single command to quickly experience the OCR pipeline: ```bash paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/c3hSldnYVQXp48T5V0Ze4.png \ --text_recognition_model_name en_PP-OCRv3_mobile_rec \ --use_doc_orientation_classify False \ --use_doc_unwarping False \ --use_textline_orientation True \ --save_path ./output \ --device gpu:0 ``` Results are printed to the terminal: ```json {'res': {'input_path': '/root/.paddlex/predict_input/c3hSldnYVQXp48T5V0Ze4.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([[[252, 172], ..., [254, 241]], ..., [[665, 566], ..., [663, 601]]], 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': ['The moon tells the sky', 'The sky tells the sea', 'The sea tells the tide', 'And the tide tells me', 'Lemn Sissay'], 'rec_scores': array([0.99890286, ..., 0.99840254]), 'rec_polys': array([[[252, 172], ..., [254, 241]], ..., [[665, 566], ..., [663, 601]]], dtype=int16), 'rec_boxes': array([[252, ..., 241], ..., [663, ..., 612]], dtype=int16)}} ``` If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/PPUZ6f72Op5zCyq7T3ZKV.png) The command-line method is for quick experience. For project integration, also only a few codes are needed as well: ```python from paddleocr import PaddleOCR ocr = PaddleOCR( text_recognition_model_name="en_PP-OCRv3_mobile_rec", use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module use_textline_orientation=True, # Use use_textline_orientation to enable/disable textline orientation classification model device="gpu:0", # Use device to specify GPU for model inference ) result = ocr.predict("https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/c3hSldnYVQXp48T5V0Ze4.png") for res in result: res.print() res.save_to_img("output") res.save_to_json("output") ``` The default model used in pipeline is `PP-OCRv5_server_rec`, so it is needed that specifing to `en_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](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/OCR.html#2-quick-start). ## Links [PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)