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--- |
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license: apache-2.0 |
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library_name: PaddleOCR |
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language: |
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- en |
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- zh |
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pipeline_tag: image-to-text |
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tags: |
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- OCR |
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- PaddlePaddle |
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- PaddleOCR |
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--- |
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# ch_RepSVTR_rec |
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## Introduction |
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RepSVTR is a mobile-side text recognition model based on SVTRv2. It won the first prize in the PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task, with a 2.5% improvement in end-to-end recognition accuracy on Leaderboard B compared to PP-OCRv4, while maintaining similar inference speed. It supports text line recognition in general Chinese and English scenarios, but mainly focuses on Chinese. The key accuracy metrics are as follow: |
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<table> |
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<tr> |
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<th>Model</th> |
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<th>Recognition Avg Accuracy(%)</th> |
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<th>Model Storage Size (M)</th> |
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</tr> |
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<tr> |
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<td>ch_RepSVTR_rec</td> |
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<td>65.07</td> |
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<td>22.1 M</td> |
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</tr> |
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</table> |
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**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. |
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## Quick Start |
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### Installation |
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1. PaddlePaddle |
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Please refer to the following commands to install PaddlePaddle using pip: |
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```bash |
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# for CUDA11.8 |
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python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ |
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# for CUDA12.6 |
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python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ |
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# for CPU |
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python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ |
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``` |
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For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). |
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2. PaddleOCR |
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Install the latest version of the PaddleOCR inference package from PyPI: |
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```bash |
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python -m pip install paddleocr |
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``` |
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### Model Usage |
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You can quickly experience the functionality with a single command: |
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```bash |
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paddleocr text_recognition \ |
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--model_name ch_RepSVTR_rec \ |
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-i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/QmaPtftqwOgCtx0AIvU2z.png |
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``` |
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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. |
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```python |
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from paddleocr import TextRecognition |
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model = TextRecognition(model_name="ch_RepSVTR_rec") |
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output = model.predict(input="QmaPtftqwOgCtx0AIvU2z.png", batch_size=1) |
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for res in output: |
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res.print() |
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res.save_to_img(save_path="./output/") |
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res.save_to_json(save_path="./output/res.json") |
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``` |
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After running, the obtained result is as follows: |
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```json |
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{'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': 0.9997341632843018}} |
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``` |
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The visualized image is as follows: |
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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). |
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### Pipeline Usage |
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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. |
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#### PP-OCR |
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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: |
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* Document Image Orientation Classification Module (Optional) |
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* Text Image Unwarping Module (Optional) |
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* Text Line Orientation Classification Module (Optional) |
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* Text Detection Module |
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* Text Recognition Module |
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Run a single command to quickly experience the OCR pipeline: |
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```bash |
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paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/818ebrVG4OtH3sjLR-NRI.png \ |
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--text_recognition_model_name ch_RepSVTR_rec \ |
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--use_doc_orientation_classify False \ |
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--use_doc_unwarping False \ |
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--use_textline_orientation True \ |
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--save_path ./output \ |
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--device gpu:0 |
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``` |
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Results are printed to the terminal: |
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```json |
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{'res': {'input_path': '/root/.paddlex/predict_input/818ebrVG4OtH3sjLR-NRI.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([[[ 0, 10], |
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..., |
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[ 0, 72]], |
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..., |
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[[189, 915], |
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..., |
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[190, 960]]], 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([1, ..., 0]), 'text_rec_score_thresh': 0.0, 'rec_texts': ['等将海中中：0028866', 'PASS', '登机牌', 'BOARDING', '座位号', 'SEAT NO.', '舱位', 'CLASS', '序号', '日期DATE', 'SERIAL NO.', '航班FLIGHT', 'W', '035', 'MU237903DEC', '始发地', 'FROM', '登机口', 'GATE', '登机时间BDT', '目的地TO', '福州', 'TAIYUAN', 'G11', 'FUZHOU', '身份识别IDNO.', '姓名', 'NAME', 'ZHANGQIWEI', '票号TKTNO', '张祺伟', '票价FARE', 'ETKT7813699238489/1', '登机口于起飞前1O分钟关闭GATESCLOSE10MINUTESBEFOREDEPARTURETIME'], 'rec_scores': array([0.58707291, ..., 0.98247343]), 'rec_polys': array([[[ 0, 10], |
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..., |
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[ 0, 72]], |
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..., |
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[[189, 915], |
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..., |
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[190, 960]]], dtype=int16), 'rec_boxes': array([[ 0, ..., 72], |
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..., |
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[189, ..., 960]], dtype=int16)}} |
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``` |
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If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: |
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The command-line method is for quick experience. For project integration, also only a few codes are needed as well: |
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```python |
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from paddleocr import PaddleOCR |
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ocr = PaddleOCR( |
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text_recognition_model_name="ch_RepSVTR_rec", |
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use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model |
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use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module |
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use_textline_orientation=True, # Use use_textline_orientation to enable/disable textline orientation classification model |
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device="gpu:0", # Use device to specify GPU for model inference |
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) |
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result = ocr.predict("https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/818ebrVG4OtH3sjLR-NRI.png") |
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for res in result: |
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res.print() |
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res.save_to_img("output") |
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res.save_to_json("output") |
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``` |
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The default model used in pipeline is `PP-OCRv5_server_rec`, so it is needed that specifing to `ch_RepSVTR_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). |
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## Links |
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[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) |
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[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html) |
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