language: en
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
Task: recognition
Example usage:
1>>> from doctr.io import DocumentFile
2>>> from doctr.models import ocr_predictor, from_hub
3
4>>> img = DocumentFile.from_images(['<image_path>'])
5>>> # Load your model from the hub
6>>> model = from_hub('mindee/my-model')
7
8>>> # Pass it to the predictor
9>>> # If your model is a recognition model:
10>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
11>>> reco_arch=model,
12>>> pretrained=True)
13
14>>> # If your model is a detection model:
15>>> predictor = ocr_predictor(det_arch=model,
16>>> reco_arch='crnn_mobilenet_v3_small',
17>>> pretrained=True)
18
19>>> # Get your predictions
20>>> res = predictor(img)
Run Configuration
{
"arch": "crnn_vgg16_bn",
"train_path": "C:\Users\Admin\Documents\AI\git_repos\doctr\data\pn\train",
"val_path": "C:\Users\Admin\Documents\AI\git_repos\doctr\data\pn\val",
"train_samples": 1000,
"val_samples": 20,
"font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf,NotoSeridGurmukhi.ttf",
"min_chars": 1,
"max_chars": 12,
"name": null,
"epochs": 1,
"batch_size": 64,
"device": 0,
"input_size": 32,
"lr": 0.001,
"weight_decay": 0,
"workers": 16,
"resume": null,
"vocab": "panjabi",
"test_only": false,
"freeze_backbone": false,
"show_samples": false,
"wb": false,
"push_to_hub": true,
"pretrained": false,
"sched": "cosine",
"amp": false,
"find_lr": false,
"early_stop": false,
"early_stop_epochs": 5,
"early_stop_delta": 0.01
}
