AllowRotateDetection = true表示允许识别有角度的文字,即使文字并不完全水平,也能被识别。 Enable180Classification = false表示不允许识别旋转角度大于90度的文字,如果文字旋转的角度过大,可能无法被正确识别。 using关键字用于确保PaddleOcrAll对象在不再需要时能被正确地释放,避免内存泄漏。 using (Mat src2 = Cv2....
使用的代码也比较简单: FullOcrModelmodel=LocalFullModels.ChineseV3;using(PaddleOcrAllall=newPaddleOcrAll(model,PaddleDevice.Mkldnn()){AllowRotateDetection=true,/* 允许识别有角度的文字 */Enable180Classification=false,/* 允许识别旋转角度大于90度的文字 */}){// Load local file by following code:using...
以下代码实现了文本检测、方向分类器和文本识别串联推理,在执行预测时,需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径、参数rec_model_dir指定识别inference模型的路径、参数use_angle_cls指定是否使用方向分类器、参数cls_model_dir指定方向分类器inference模型的路径、...
FullOcrModelmodel=LocalFullModels.ChineseV3;using(PaddleOcrAllall=newPaddleOcrAll(model,PaddleDevice.Mkldnn()){AllowRotateDetection=true,/* 允许识别有角度的文字 */Enable180Classification=false,/* 允许识别旋转角度大于90度的文字 */}){// Load local file by following code:using(Matsrc2=Cv2.ImRead(se...
image = cv2.rotate(image, 1) return image class ClsPredictor: def __init__(self, model_path, target_size=(100, 32), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], threshold=0.9): self.target_size = target_size self.mean = mean ...
DBNet使用语义分割的方法检测文本区域,它预测概率图的同时预测阈值图,从而得到自适应地二值图,本文不多关注理论,相关资料可参考论文:Real-time Scene Text Detection with Differentiable Binarization 三、模型下载 PaddleOCR官方提供了多个预训练模型(传送门),也可以训练自己的模型,本项目以部署为主,直接部署官方提供的...
DBNet使用语义分割的方法检测文本区域,它预测概率图的同时预测阈值图,从而得到自适应地二值图,本文不多关注理论,相关资料可参考论文:Real-time Scene Text Detection with Differentiable Binarization 三、模型下载 PaddleOCR官方提供了多个预训练模型(传送门),也可以训练自己的模型,本项目以部署为主,直接部署官方提供的...
Fix image rotation and size problems, optimize the process of editing the mark frame (by ninetailskim、 edencfc). 2021.1.11: Optimize the labeling experience (by edencfc), Users can choose whether to pop up the label input dialog after drawing the detection box in "View - Pop-up Label ...
This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR. 1. Data and Weights Preparation 1.1 Data Preparation 1.2 Download Pre-trained Model 2. Training 2.1 Start Training 2.2 Load Trained Model and Continue ...
Model introductionModel nameRecommended sceneDetection modelDirection classifierRecognition model Chinese and English ultra-lightweight PP-OCRv2 model(11.6M) ch_PP-OCRv2_xx Mobile&Server inference model / pre-trained model inference model / pre-trained model inference model / pre-trained model Chinese ...