F
Faisal Shafait
Researcher at University of the Sciences
Publications - 222
Citations - 10131
Faisal Shafait is an academic researcher from University of the Sciences. The author has contributed to research in topics: Deep learning & Optical character recognition. The author has an hindex of 41, co-authored 211 publications receiving 7810 citations. Previous affiliations of Faisal Shafait include National University of Science and Technology & German Research Centre for Artificial Intelligence.
Papers
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Proceedings ArticleDOI
A simple and effective approach for border noise removal from document images
Faisal Shafait,Thomas M. Breuel +1 more
TL;DR: This paper presents a simple and effective approach for removing both textual and non-textual noise by finding borders of noise regions using projection profile analysis and demonstrates the effectiveness of this approach by evaluating it quantitatively on the widely used University of Washington (UW3) dataset.
Journal ArticleDOI
Automatic authentication of color laser print-outs using machine identification codes
TL;DR: This paper presents an important extension of their previous work for detecting the class of printer that was used to generate a print-out, namely automatic methods for comparing two base patterns from two different print-outs to verify if two print- outs come from the same printer and for automatic decoding of the base pattern.
Proceedings ArticleDOI
Fault detection and localization in empty water bottles through machine vision
TL;DR: In this article, a vision-based approach for fault detection of empty water bottles is presented, where the main constraint is the real-time operation as the bottles move continuously on the conveyer belt.
Proceedings ArticleDOI
Text-Line Extraction Using a Convolution of Isotropic Gaussian Filter with a Set of Line Filters
TL;DR: Performance improvements to these technique based on the use of a convolution of isotropic Gaussian filter with line filters are described, which are motivated by a matched filter approach to text-lines and, in addition, require fewer operations to compute.
Proceedings ArticleDOI
A Spatio-Spectral Hybrid Convolutional Architecture for Hyperspectral Document Authentication
TL;DR: The proposed method achieves the highest accuracy among the other ink mismatch detection methods on the UWA Writing Ink Hyperspectral Images database (WIHSI), which demonstrates the effectiveness of deep learning models employing spatio-spectral hybrid features for document authentication.