PyTorch 0.4.1 implementation of the following paper: Le Kang, et al. "A DEEP LEARNING APPROACH TO DOCUMENT IMAGE QUALITY ASSESSMENT." 2014 ICIP.
The SOC dataset can be downloaded in DIQA: Document Image Quality Assesment Datasets
Download the dataset and put all images in a directory and set this directory as root in 'config.yaml'
The ground truth for the dataset has been pre-processed and saved as a excel file SOC_gt.xlsx stored in ./data/gt_files/SOC_gt.xlsx
The ground truth file contains:
- img_name: the image name
- img_set: the index of reference image from which the current degraded image generated.
- acc_f: OCR accuracy by ABBYY Finereader
- acc_t: OCR accuracy by Tesseract
- acc_o: OCR accuracy by Omnipage
- acc_avg: average accuracy of the three OCR engines above
The creating details about this dataset:
python main.py --batch_size=128 --epochs=500 --lr=0.001
Before training, the root in config.yaml must be specified.
python demo_DIQA.py
When a DIQA model has been trained, demo_DIQA.py can be used to predict the quality of a document image directly.
Before running demo_DIQA.py, the model_path and img_path must be specified.
- PyTorch 0.4.1
- pytorch/ignite