A handwritten signature is the final response to a complex cognitive and neuromuscular process which is the result of the learning process. Because of the many factors involved in signing, it is possible to study the signature from many points of view: graphologists, forensic experts, neurologists and computer vision experts have all examined them. Researchers study written signatures for psychiatric, penal, health and automatic verification purposes. As a potentially useful, multi-purpose study, this paper is focused on the lexical morphology of handwritten signatures. This we understand to mean the identification, analysis, and description of the signature structures of a given signer. In this work we analyze different public datasets involving 1533 signers from different Western geographical areas. Some relevant characteristics of signature lexical morphology have been selected, examined in terms of their probability distribution functions and modeled through a General Extreme Value distribution. This study suggests some useful models for multi-disciplinary sciences which depend on handwriting signatures.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393123 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123254 | PLOS |
J Cogn
January 2025
Department of Humanities, University of Trento, via Tommaso Gar 14, 38122, Trento, Italy.
The productive use of morphological information is considered one of the possible ways in which speakers of a language understand and learn unknown words. In the present study we investigate if, and how, also adult L2 learners exploit morphological information to process unknown words by analyzing the impact of language proficiency in the processing of novel derivations. Italian L2 learners, divided into three proficiency groups, participated in a lexical decision where pseudo-words could embed existing stems (e.
View Article and Find Full Text PDFChildren (Basel)
November 2024
Univ. Lille, CNRS, UMR 9193-SCALab-Sciences Cognitives et Sciences Affectives, F-59000 Lille, France.
Background/objectives: The present study examines the role of morphemic units in the initial word recognition stage among beginning readers. We assess whether and to what extent sublexical units, such as morphemes, are used in processing French words and how their use varies with reading proficiency.
Methods: Two experiments were conducted to investigate the perceptual and morphological effects on the recognition of words presented in central vision, using a variable-viewing-position technique.
Neuropsychologia
January 2025
Center for Aphasia Research and Rehabilitation, Georgetown University Medical Center, USA.
The underlying causes of reading impairment in neurodegenerative disease are not well understood. The current study seeks to determine the causes of surface alexia and phonological alexia in primary progressive aphasia (PPA) and typical (amnestic) Alzheimer's disease (AD). Participants included 24 with the logopenic variant (lvPPA), 17 with the nonfluent/agrammatic variant (nfvPPA), 12 with the semantic variant (svPPA), 19 with unclassifiable PPA (uPPA), and 16 with AD.
View Article and Find Full Text PDFBehav Res Methods
December 2024
McMaster University, MELD Office 4045, L.R. Wilson Hall, Hamilton, ON, L8N 1E9, Canada.
PeerJ Comput Sci
November 2024
Department of Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovak Republic.
This study introduces a new approach to text tokenization, SlovaK Morphological Tokenizer (SKMT), which integrates the morphology of the Slovak language into the training process using the Byte-Pair Encoding (BPE) algorithm. Unlike conventional tokenizers, SKMT focuses on preserving the integrity of word roots in individual tokens, crucial for maintaining lexical meaning. The methodology involves segmenting and extracting word roots from morphological dictionaries and databases, followed by preprocessing and training SKMT alongside a traditional BPE tokenizer.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!