Please use this identifier to cite or link to this item:
Title: Post Processing of Handwritten Phonetic Pitman's Shorthand Using a Bayesian Network Built on Geometric Attributes
Contributor(s): Htwe, Swe Myo (author); Higgins, Colin (author); Leedham, Graham  (author); Yang, Ma (author)
Publication Date: 2005
DOI: 10.1007/11551188_63
Handle Link:
Abstract: In this paper, we introduce a new approach to the computer transcription of handwritten Pitman shorthand as a rapid means of text entry (up to 100 words per minute) into today's handheld devices, almost at the rate of speech. It is different from previous applications of the same framework from two aspects: - firstly, a novel idea of using geometric attributes other than phonetic attributes in the abstraction of a phonetic Pitman's shorthand lexicon is proposed. Secondly, a Bayesian network representation for the organisation of shorthand-outline models is introduced, in which natural variability of Pitman shorthand is defined via different nodes and links. Using a probabilistic Bayesian network, the system shows a noticeable robustness not only in transcribing a variety of genuine handwriting, but also in estimating missing vowel components that may have been omitted in speed writing. The accuracy of the new approach (92.86%) is a considerable improvement over previous applications.
Publication Type: Conference Publication
Conference Details: ICAPR 2005: 3rd International Conference on Advances in Pattern Recognition, Bath, United Kingdom, 22nd - 25th August, 2005
Source of Publication: Pattern Recognition and Data Mining: Proceedings of the Third International Conference on Advances in Pattern Recognition (ICAPR 2005), v.1, p. 569-579
Publisher: Springer-Verlag
Place of Publication: Berlin, Germany
Field of Research (FOR): 080106 Image Processing
080199 Artificial Intelligence and Image Processing not elsewhere classified
080109 Pattern Recognition and Data Mining
Socio-Economic Objective (SEO): 810107 National Security
890299 Computer Software and Services not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Other Links:
Series Name: Lecture Notes in Computer Science
Series Number : 3686
Statistics to Oct 2018: Visitors: 357
Views: 536
Downloads: 0
Appears in Collections:Conference Publication
School of Science and Technology

Files in This Item:
3 files
File Description SizeFormat 
Show full item record

Page view(s)

checked on Feb 8, 2019
Google Media

Google ScholarTM



Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.