PaddleOCR
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Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and de...
 我正在制作表格识别的数据集,如上图所示的pipeline中涉及到 Text Coords 和 Cell Coords的概念,请问这两个有什么区别吗?标注Cell Coords该怎么标注呢?
垂类数据集只要5000张,并且种类不丰富,达不到比较好的效果。进行fine-tuning是否能提高模型的性能,需要用哪些数据集进行预训练。
请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem - 系统环境/System Environment: - 版本号/Version:Paddle: PaddleOCR: 问题相关组件/Related components: - 运行指令/Command Code: - 完整报错/Complete Error Message:
 请问下 label_list.txt文件中的标签是什么?
请教一下,怎么做成本地服务器???可以在WEB访问那种。 我按这个安装教程,装好了也只是一个本地执行环境 像这样可以生成的页面:https://wenxin.baidu.com/moduleApi/ernieVilg
flask启服务后调用paddleocr,client调用服务时不时出现如下错误,三个client同时循环调用服务的话,client调用大概十几次服务就会出现这个问题 File "ocr_infer\tools\rec_infer.py", line 19, in rec result = ocr.ocr(img, det=False) File "D:\Anaconda3\envs\paddleocr\lib\site-packages\paddleocr\paddleocr.py", line 535, in ocr img, cls_res, elapse = self.text_classifier(img) File "D:\Anaconda3\envs\paddleocr\lib\site-packages\paddleocr\tools\infer\predict_cls.py", line 114, in __call__ prob_out =...
我看`tools/infer/predict_rec.py` 支持多张图片一个batch同时识别,`tools/infer/predict_system.py`只支持单张推理吗
paddle_inference版本是2.2.2 在PaddleOCR项目中,C++推理时,本地两台机器上代码运行正常,使用GPU和tensorrt均正常。但是用另一部机器(也是Paddle支持的显卡),在这上运行时,设备GPU的利用率就为0,GPU用不起来,然后程序就会报错。代码和环境都一致,请问这是什么原因呢?? 报错信息如下: WARNING: Logging before InitGoogleLogging() is written to STDERR I1028 14:30:40.557919 22346 analysis_config.cc:1044] In CollectShapeInfo mode, we will disable optimizations and collect the shape information of all intermediate...
执行环境:ubuntu 20.04 + cpu + mkldnn 错误信息: File "/usr/src/app.py", line 160, in invoke ocr_result = ocr_inference(disk_im_fullpath) File "/usr/src/app.py", line 87, in ocr_inference result = ocr.ocr(im, cls=False) File "/usr/local/lib/python3.8/dist-packages/paddleocr/paddleocr.py", line 524,...
I'm training my model on a large 9M dataset which usually gets high accuracy in Training using Models like vanilla CRNN or SAR (above 95%) but when using PPOCRv3 the...