clean_images_binary: Binary ground truth of the images (normal resolution).Įvery file is a printed text image following the pattern FontABC_NoiseD_EE.png:Ī) Size of the font: footnote size (f), normal size (n) o large size (L).ī) Font type: typewriter (t), sans serif (s) or roman (r).ĭ) Type of noise: folded sheets (Noise f), wrinkled sheets (Noise w), coffee stains (Noise c), and footprints (Noise p).Į) Data set partition: training (TR), validation (VA), test (TE), real (RE).įor each type of font, one type of Noise: 17 files * 4 types of noise = 72 images. clean_images_grayscale: Grayscale ground truth of the images with smoothing on the borders (normal resolution). clean_images_grayscale_doubleresolution: Grayscale ground truth of the images with double resolution. simulated_noisy_images_grayscale: 72 grayscale images of scanned 'simulated noisy' images for training, validation and test. ![]() real_noisy_images_grayscale_doubleresolution: idem, double resolution. real_noisy_images_grayscale: 72 grayscale images of scanned 'noisy' images. There are links to the original airfoil source and dat file and the details page with polar diagrams for a range of Reynolds numbers. Click on an airfoil image to display a larger preview picture. |- clean_images_grayscale_doubleresolution Search the 1638 airfoils available in the databases filtering by name, thickness and camber. | `- real_noisy_images_grayscale_doubleresolution RealNoisyOffice folder is provided for subjective evaluation. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope - a boring and time-consuming task. SimulatedNoisyOffice folder has been prepared for training, validation and test of supervised methods. Predicting the age of abalone from physical measurements. Double resolution ground truth images are also provided in order to test superresolution methods. ![]() To this end, noisy images and their corresponding cleaned or binarized ground truth are provided. This corpus is intended to do cleaning (or binarization) and enhancement of noisy grayscale printed text images using supervised learning methods.
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