WEZUT OCR Dataset ver. 1.00 consists of 176 non-uniformly illuminated document images captured by Digital Single Lens Reflex (DSLR) camera Nikon N70 together with the reference text file 00_GT.txt containing commonly used placeholder text "Lorem ipsum". The images represent the photos of the documents printed using 5 different popular font shapes (Arial, Times New Roman, Calibri, Courier and Verdana) with some typical modifications of attributes (normal, bold and italic versions of all fonts as well as bold italics).

This dataset has been developed at the Faculty of Electrical Engineering (WE) of West Pomeranian University of Technology in Szczecin, Poland (ZUT). The authors (Hubert Michalak and Krzysztof Okarma) are with Department of Signal Processing and Multimedia Engineering (KPSiIM). It is intended mainly for the evaluation of image binarization algorithms, developed for the pre-processing of non-uniformly illuminated document images subjected to further text recognition using various OCR engines.

In case of publishing results obtained by means of the WEZUT OCR Dataset please refer to one or more of the following papers (published in Open Access mode):

• Michalak H., Okarma K.: Robust combined binarization method of non-uniformly illuminated document images for alphanumerical character recognition. Sensors, vol. 20 no. 10, article no. 2914, 2020, DOI: 10.3390/s20102914, (BIBTeX citation data)
• Michalak H., Okarma K.: Improvement of image binarization methods using image preprocessing with local entropy filtering for alphanumerical character recognition purposes. Entropy, vol. 21 no. 6, article no. 562, 2019, DOI: 10.3390/e21060562, (BIBTeX citation data)
• Michalak H., Okarma K.: Fast binarization of unevenly illuminated document images based on background estimation for optical character recognition purposes. Journal of Universal Computer Science, vol. 25 no. 6, pp. 627-646, 2019, DOI: 10.3217/jucs-025-06-062, (BIBTeX citation data)

WEZUT OCR Dataset file for download (ZIP - 85.5 MB)
Mirror download linked at DIB website hosted by Centro de Informática (CIn), Universidade Federal de Pernambuco (UFPE), Brazil

WEZUT Video OCR Dataset ver. 1.00 contains 20 non-uniformly illuminated video sequences captured by Olympus Tough TG-5 12 MPix camera with Multi-motion Movie IS stabilization. Individual frames of these video files contain the images of the same placeholder text "Lorem ipsum". The dataset is split into two parts: 12 files recorded in typical non-uniform lighting conditions and 8 video sequences affected by the presence of shadows.

This dataset has been developed at the Faculty of Electrical Engineering (WE) of West Pomeranian University of Technology in Szczecin, Poland (ZUT). Both authors (Piotr Lech and Krzysztof Okarma) are with Department of Signal Processing and Multimedia Engineering (KPSiIM).

It is intended mainly for the evaluation of image binarization and quality assessment algorithms, developed for the pre-processing of non-uniformly illuminated document images and videos subjected to further text recognition using various OCR engines.In case of publishing results obtained by means of the WEZUT Video OCR Dataset please refer the following paper (published in Open Access mode)::

• Okarma K., Lech P.: A method supporting fault-tolerant optical text recognition from video sequences recorded with handheld cameras. Engineering Applications of Artificial Intelligence, vol. 123 Part B, article no. 106330, 2023, DOI: 10.1016/j.engappai.2023.106330, (BIBTeX citation data)

WEZUT Video OCR Dataset file for download (ZIP - 1.27 GB)

WEZUT 3DPrint Quality Dataset ver. 1.00 consists of 107 images and 107 depth maps obtained for planar samples (their dimensions are 35 mm × 35 mm × 4 mm) captured by a Sony DSC-HX100V camera in controlled illumination conditions and the GOM ATOS 3D scanner, respectively. All samples were produced with popular FDM 3D printers, such as Prusa i3, RepRap Ormerod 3, and da Vinci 1.0 Pro 3-in-1, from two popular types of filaments, namely PLA and ABS, using several colors of materials. The size of captured images obtained using the camera without flash for the exposure time 1/125 s and 5 mm focal length using an automatic white balance is 1600 × 1600 pixels.

An integral part of the database is the set of expert assessments expressed on a 4-point scale (good - 1, moderately good - 4, moderately poor - 6, and poor quality - 10), as well as subjective assessment values (Mean Opinion Scores – MOS) obtained as a result of perceptual experiments conducted among a group of several dozen volunteers in the scale from 1 to 5. These MOS values, verified as consistent with expert assessments, were used as a reference for the development and optimization of automatic quality assessment metrics to achieve the highest possible correlation with subjective quality scores.

This dataset has been developed at the Faculty of Electrical Engineering (WE) of West Pomeranian University of Technology in Szczecin, Poland (ZUT). The authors (Jarosław Fastowicz, Mateusz Tecław and Krzysztof Okarma) are with Department of Signal Processing and Multimedia Engineering (KPSiIM).
It is intended mainly for the evaluation of image-based quality assessment methods of 3D printed surfaces, developed for the quality control of the 3D printed objects using the cameras observing the manufacturing process from a side with visible individual layers of the filament.

Permission to use, copy, or modify this database and its documentation for educational and research purposes only and without fee is hereby granted, provided that this copyright notice and the original authors' names appear on all copies and supporting documentation. This database shall not be modified without first obtaining written permission of the authors. The authors make no representations about the suitability of this database for any purpose. It is provided "as is" without express or implied warranty. In case of publishing results obtained by means of the WEZUT 3DPrint Quality Dataset please refer to one or more of the following papers (published mainly in Open Access mode):

• Okarma K., Fastowicz J,. Lech P., Lukin V.: Quality assessment of 3D printed surfaces using combined metrics based on mutual Structural Similarity approach correlated with subjective aesthetic evaluation. Applied Sciences, vol. 10 no. 18, article no. 6248, 2020, DOI: 10.3390/app10186248 (BIBTeX citation data)
• Okarma K., Fastowicz J.: Improved quality assessment of colour surfaces for additive manufacturing based on image entropy. Pattern Analysis and Applications, vol. 23 no. 3, pp. 1035-1047, 2020, DOI: 10.1007/s10044-020-00865-w 10.1007/s10044-020-00865-w (BIBTeX citation data)
• Fastowicz J., Lech P., Okarma K.: Combined metrics for quality assessment of 3D printed surfaces for aesthetic purposes: towards higher accordance with subjective evaluations. Lecture Notes in Computer Science, vol. 12143 (ICCS'2020), pp. 326-339, Springer, 2020, DOI: 10.1007/978-3-030-50436-6_24 (BIBTeX citation data)
• Okarma K., Fastowicz J.: Adaptation of full-reference image quality assessment methods for automatic visual evaluation of the surface quality of 3D prints. Elektronika Ir Elektrotechnika, vol. 25 no. 5, pp. 57-62, 2019, DOI: 10.5755/j01.eie.25.5.24357 10.5755/j01.eie.25.5.24357 (BIBTeX citation data)
• Fastowicz J., Okarma K.: Quality assessment of photographed 3D printed flat surfaces using Hough transform and histogram equalization. Journal of Universal Computer Science, vol. 25 no. 6, pp. 701-717, 2019, DOI: 10.3217/jucs-025-06-0701 (BIBTeX citation data)
• Fastowicz J., Grudziński M., Tecław M., Okarma K.: Objective 3D printed surface quality assessment based on entropy of depth maps. Entropy, vol. 21 no. 1, article no. 97, 2019, DOI: 10.3390/e21010097 (BIBTeX citation data)

WEZUT 3D Print Quality Dataset file for download (ZIP - 672 MB)