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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Writer verification of partially damaged handwritten Arabic documents based on individual character shapes Khan, Majid A.; Mohammad, Nazeeruddin; Ben Brahim, Ghassen; Bashar, Abul; Latif, Ghazanfar In: PeerJ Comput Sci (2022) Abstract: Author verification of handwritten text is required in several application domains and has drawn a lot of attention within the research community due to its importance. Though, several approaches have been proposed for the text-independent writer verification of handwritten text, none of these have addressed the problem domain where author verification is sought based on partially-damaged handwritten documents (e.g., during forensic analysis). In this paper, we propose an approach for offline text-independent writer verification of handwritten Arabic text based on individual character shapes (within the Arabic alphabet). The proposed approach enables writer verification for partially damaged documents where certain handwritten characters can still be extracted from the damaged document. We also provide a mechanism to identify which Arabic characters are more effective during the writer verification process. We have collected a new dataset, Arabic Handwritten Alphabet, Words and Paragraphs Per User (AHAWP), for this purpose in a classroom setting with 82 different users. The dataset consists of 53,199 user-written isolated Arabic characters, 8,144 Arabic words, 10,780 characters extracted from these words. Convolutional neural network (CNN) based models are developed for verification of writers based on individual characters with an accuracy of 94% for isolated character shapes and 90% for extracted character shapes. Our proposed approach provided up to 95% writer verification accuracy for partially damaged documents. Keyword: Artificial Intelligence URL: https://doi.org/10.7717/peerj-cs.955 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044228/ BASE Hide details 2 Reciprocals and Semantic Typology Evans, Nicholas; Gaby, Alice; Levinson, Stephen C.. - : John Benjamins Publishing Company, 2021 BASE Show details 3 Hot and cold smells: Odor-temperature associations across cultures (Maniq, Thai, Dutch) ... Wnuk, E.M.; Valk, J.M. De; Huisman, J.L.A.. - : Data Archiving and Networked Services (DANS), 2017 BASE Show details 4 Human olfaction at the intersection of language, culture and biology ... : Flavor Naming of Experts ... Majid, A; Croijmans, I.. - : Data Archiving and Networked Services (DANS), 2016 BASE Show details 5 Shades of emotion: What the addition of sunglasses or masks to faces reveals about the development of facial expression processing Roberson, D; Kikutani, M; D�ge, P. - : Elsevier, 2012 BASE Show details 6 The linguistic description of minimal social scenarios affects the extent of causal inference making Majid, A; Sanford, AJ; Pickering, MJ. - 2007 BASE Show details 7 The linguistic description of minmal social scenarios affects the extent of causal inference making Majid, A.; Sanford, A.J.; Pickering, M.J.. - 2005 BASE Show details

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