Pinned Repositories
PanelSeg
Fastest_Image_Pattern_Matching
C++ implementation of a ScienceDirect paper "An accelerating cpu-based correlation-based image alignment for real-time automatic optical inspection"
PaddleClas
A treasure chest for visual classification and recognition powered by PaddlePaddle
PaddleDetection
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
PaddleOCR
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 deployment among server, mobile, embedded and IoT devices)
PPOCRLabel
PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PP-OCR model to automatically detect and re-recognize data. It is written in Python 3 and PyQT5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PP-OCR detection and recognition mod
JieZou1's Repositories
JieZou1/Fastest_Image_Pattern_Matching
C++ implementation of a ScienceDirect paper "An accelerating cpu-based correlation-based image alignment for real-time automatic optical inspection"
JieZou1/PaddleOCR
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 deployment among server, mobile, embedded and IoT devices)
JieZou1/PaddleDetection
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
JieZou1/PaddleClas
A treasure chest for visual classification and recognition powered by PaddlePaddle
JieZou1/PPOCRLabel
PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PP-OCR model to automatically detect and re-recognize data. It is written in Python 3 and PyQT5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PP-OCR detection and recognition mod
JieZou1/PanelSeg