| Name | Version | Summary | date |
| fadoudou2 |
2.7.0.4.7.1 |
Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices) |
2024-10-11 07:30:25 |
| unstructured.paddleocr |
2.8.1.0 |
Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices |
2024-08-23 17:31:25 |
| vaaale-paddleocr |
2.6.1.3.post2 |
Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices |
2024-06-12 17:51:22 |
| fadoudou |
2.7.0.3 |
Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices) |
2023-12-07 04:07:44 |
| toddleocr |
1.2.8 |
Awesome OCR toolkits based on Torch （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embedded and IoT devices |
2023-11-17 09:07:36 |
| ppocrlabel-japan |
0.0.2 |
PPOCRLabelv2 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 Python3 and PyQT5, supporting rectangular box, table, irregular text and key information annotation modes. Annotations can be directly used for the training of PP-OCR detection and recognition models. |
2023-06-06 05:10:30 |
| paddleocrWordLevelDetection |
2.6.1.0 |
Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices |
2023-05-10 15:40:01 |